new normal research paper

• Viewpoints/ Controversies
• Published: 24 November 2020

The “new normal” in education

• José Augusto Pacheco   ORCID: orcid.org/0000-0003-4623-6898 1  

PROSPECTS volume  51 ,  pages 3–14 ( 2021 ) Cite this article

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Effects rippling from the Covid 19 emergency include changes in the personal, social, and economic spheres. Are there continuities as well? Based on a literature review (primarily of UNESCO and OECD publications and their critics), the following question is posed: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education? Technologization, while an ongoing and evidently ever-intensifying tendency, is not without its critics, especially those associated with the humanistic tradition in education. This is more apparent now that curriculum is being conceived as a complicated conversation. In a complex and unequal world, the well-being of students requires diverse and even conflicting visions of the world, its problems, and the forms of knowledge we study to address them.

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From the past, we might find our way to a future unforeclosed by the present (Pinar 2019 , p. 12)

Texts regarding this pandemic’s consequences are appearing at an accelerating pace, with constant coverage by news outlets, as well as philosophical, historical, and sociological reflections by public intellectuals worldwide. Ripples from the current emergency have spread into the personal, social, and economic spheres. But are there continuities as well? Is the pandemic creating a “new normal” in education or simply accenting what has already become normal—an accelerating tendency toward technologization? This tendency presents an important challenge for education, requiring a critical vision of post-Covid-19 curriculum. One must pose an additional question: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education?

The ongoing present

Unpredicted except through science fiction, movie scripts, and novels, the Covid-19 pandemic has changed everyday life, caused wide-scale illness and death, and provoked preventive measures like social distancing, confinement, and school closures. It has struck disproportionately at those who provide essential services and those unable to work remotely; in an already precarious marketplace, unemployment is having terrible consequences. The pandemic is now the chief sign of both globalization and deglobalization, as nations close borders and airports sit empty. There are no departures, no delays. Everything has changed, and no one was prepared. The pandemic has disrupted the flow of time and unraveled what was normal. It is the emergence of an event (think of Badiou 2009 ) that restarts time, creates radical ruptures and imbalances, and brings about a contingency that becomes a new necessity (Žižek 2020 ). Such events question the ongoing present.

The pandemic has reshuffled our needs, which are now based on a new order. Whether of short or medium duration, will it end in a return to the “normal” or move us into an unknown future? Žižek contends that “there is no return to normal, the new ‘normal’ will have to be constructed on the ruins of our old lives, or we will find ourselves in a new barbarism whose signs are already clearly discernible” (Žižek 2020 , p. 3).

Despite public health measures, Gil ( 2020 ) observes that the pandemic has so far generated no physical or spiritual upheaval and no universal awareness of the need to change how we live. Techno-capitalism continues to work, though perhaps not as before. Online sales increase and professionals work from home, thereby creating new digital subjectivities and economies. We will not escape the pull of self-preservation, self-regeneration, and the metamorphosis of capitalism, which will continue its permanent revolution (Wells 2020 ). In adapting subjectivities to the recent demands of digital capitalism, the pandemic can catapult us into an even more thoroughly digitalized space, a trend that artificial intelligence will accelerate. These new subjectivities will exhibit increased capacities for voluntary obedience and programmable functioning abilities, leading to a “new normal” benefiting those who are savvy in software-structured social relationships.

The Covid-19 pandemic has submerged us all in the tsunami-like economies of the Cloud. There is an intensification of the allegro rhythm of adaptation to the Internet of Things (Davies, Beauchamp, Davies, and Price 2019 ). For Latour ( 2020 ), the pandemic has become internalized as an ongoing state of emergency preparing us for the next crisis—climate change—for which we will see just how (un)prepared we are. Along with inequality, climate is one of the most pressing issues of our time (OECD 2019a , 2019b ) and therefore its representation in the curriculum is of public, not just private, interest.

Education both reflects what is now and anticipates what is next, recoding private and public responses to crises. Žižek ( 2020 , p. 117) suggests in this regard that “values and beliefs should not be simply ignored: they play an important role and should be treated as a specific mode of assemblage”. As such, education is (post)human and has its (over)determination by beliefs and values, themselves encoded in technology.

Will the pandemic detoxify our addiction to technology, or will it cement that addiction? Pinar ( 2019 , pp. 14–15) suggests that “this idea—that technological advance can overcome cultural, economic, educational crises—has faded into the background. It is our assumption. Our faith prompts the purchase of new technology and assures we can cure climate change”. While waiting for technology to rescue us, we might also remember to look at ourselves. In this way, the pandemic could be a starting point for a more sustainable environment. An intelligent response to climate change, reactivating the humanistic tradition in education, would reaffirm the right to such an education as a global common good (UNESCO 2015a , p. 10):

This approach emphasizes the inclusion of people who are often subject to discrimination – women and girls, indigenous people, persons with disabilities, migrants, the elderly and people living in countries affected by conflict. It requires an open and flexible approach to learning that is both lifelong and life-wide: an approach that provides the opportunity for all to realize their potential for a sustainable future and a life of dignity”.

Pinar ( 2004 , 2009 , 2019 ) concevies of curriculum as a complicated conversation. Central to that complicated conversation is climate change, which drives the need for education for sustainable development and the grooming of new global citizens with sustainable lifestyles and exemplary environmental custodianship (Marope 2017 ).

The new normal

The pandemic ushers in a “new” normal, in which digitization enforces ways of working and learning. It forces education further into technologization, a development already well underway, fueled by commercialism and the reigning market ideology. Daniel ( 2020 , p. 1) notes that “many institutions had plans to make greater use of technology in teaching, but the outbreak of Covid-19 has meant that changes intended to occur over months or years had to be implemented in a few days”.

Is this “new normal” really new or is it a reiteration of the old?

Digital technologies are the visible face of the immediate changes taking place in society—the commercial society—and schools. The immediate solution to the closure of schools is distance learning, with platforms proliferating and knowledge demoted to information to be exchanged (Koopman 2019 ), like a product, a phenomenon predicted decades ago by Lyotard ( 1984 , pp. 4-5):

Knowledge is and will be produced in order to be sold, it is and will be consumed in order to be valued in a new production: in both cases, the goal is exchange. Knowledge ceases to be an end in itself, it loses its use-value.

Digital technologies and economic rationality based on performance are significant determinants of the commercialization of learning. Moving from physical face-to-face presence to virtual contact (synchronous and asynchronous), the learning space becomes disembodied, virtual not actual, impacting both student learning and the organization of schools, which are no longer buildings but websites. Such change is not only coterminous with the pandemic, as the Education 2030 Agenda (UNESCO 2015b ) testified; preceding that was the Delors Report (Delors 1996 ), which recoded education as lifelong learning that included learning to know, learning to do, learning to be, and learning to live together.

Transnational organizations have specified competences for the 21st century and, in the process, have defined disciplinary and interdisciplinary knowledge that encourages global citizenship, through “the supra curriculum at the global, regional, or international comparative level” (Marope 2017 , p. 10). According to UNESCO ( 2017 ):

While the world may be increasingly interconnected, human rights violations, inequality and poverty still threaten peace and sustainability. Global Citizenship Education (GCED) is UNESCO’s response to these challenges. It works by empowering learners of all ages to understand that these are global, not local issues and to become active promoters of more peaceful, tolerant, inclusive, secure and sustainable societies.

These transnational initiatives have not only acknowledged traditional school subjects but have also shifted the curriculum toward timely topics dedicated to understanding the emergencies of the day (Spiller 2017 ). However, for the OECD ( 2019a ), the “new normal” accentuates two ideas: competence-based education, which includes the knowledges identified in the Delors Report , and a new learning framework structured by digital technologies. The Covid-19 pandemic does not change this logic. Indeed, the interdisciplinary skills framework, content and standardized testing associated with the Programme for International Student Assessment of the OECD has become the most powerful tool for prescribing the curriculum. Educationally, “the universal homogenous ‘state’ exists already. Globalization of standardized testing—the most prominent instance of threatening to restructure schools into technological sites of political socialization, conditioning children for compliance to a universal homogeneous state of mind” (Pinar 2019 , p. 2).

In addition to cognitive and practical skills, this “homogenous state of mind” rests on so-called social and emotional skills in the service of learning to live together, affirming global citizenship, and presumably returning agency to students and teachers (OECD 2019a ). According to Marope ( 2017 , p. 22), “this calls for higher flexibility in curriculum development, and for the need to leave space for curricula interpretation, contextualization, and creativity at the micro level of teachers and classrooms”. Heterogeneity is thus enlisted in the service of both economic homogeneity and disciplinary knowledge. Disciplinary knowledge is presented as universal and endowed with social, moral, and cognitive authority. Operational and effective knowledge becomes central, due to the influence of financial lobbies, thereby ensuring that the logic of the market is brought into the practices of schools. As Pestre ( 2013 , p. 21) observed, “the nature of this knowledge is new: what matters is that it makes hic et nunc the action, its effect and not its understanding”. Its functionality follows (presumably) data and evidence-based management.

A new language is thus imposed on education and the curriculum. Such enforced installation of performative language and Big Data lead to effective and profitable operations in a vast market concerned with competence in operational skills (Lyotard 1984 ). This “new normal” curriculum is said to be more horizontal and less hierarchical and radically polycentric with problem-solving produced through social networks, NGOs, transnational organizations, and think tanks (Pestre 2013 ; Williamson 2013 , 2017 ). Untouched by the pandemic, the “new (old) normal” remains based on disciplinary knowledge and enmeshed in the discourse of standards and accountability in education.

Such enforced commercialism reflects and reinforces economic globalization. Pinar ( 2011 , p. 30) worries that “the globalization of instrumental rationality in education threatens the very existence of education itself”. In his theory, commercialism and the technical instrumentality by which homogenization advances erase education as an embodied experience and the curriculum as a humanistic project. It is a time in which the humanities are devalued as well, as acknowledged by Pinar ( 2019 , p. 19): “In the United States [and in the world] not only does economics replace education—STEM replace the liberal arts as central to the curriculum—there are even politicians who attack the liberal arts as subversive and irrelevant…it can be more precisely characterized as reckless rhetoric of a know-nothing populism”. Replacing in-person dialogical encounters and the educational cultivation of the person (via Bildung and currere ), digital technologies are creating uniformity of learning spaces, in spite of their individualistic tendencies. Of course, education occurs outside schools—and on occasion in schools—but this causal displacement of the centrality of the school implies a devaluation of academic knowledge in the name of diversification of learning spaces.

In society, education, and specifically in the curriculum, the pandemic has brought nothing new but rather has accelerated already existing trends that can be summarized as technologization. Those who can work “remotely” exercise their privilege, since they can exploit an increasingly digital society. They themselves are changed in the process, as their own subjectivities are digitalized, thus predisposing them to a “curriculum of things” (a term coined by Laist ( 2016 ) to describe an object-oriented pedagogical approach), which is organized not around knowledge but information (Koopman 2019 ; Couldry and Mejias 2019 ). This (old) “new normal” was advanced by the OECD, among other international organizations, thus precipitating what some see as “a dynamic and transformative articulation of collective expectations of the purpose, quality, and relevance of education and learning to holistic, inclusive, just, peaceful, and sustainable development, and to the well-being and fulfilment of current and future generations” (Marope 2017 , p. 13). Covid-19, illiberal democracy, economic nationalism, and inaction on climate change, all upend this promise.

Understanding the psychological and cultural complexity of the curriculum is crucial. Without appreciating the infinity of responses students have to what they study, one cannot engage in the complicated conversation that is the curriculum. There must be an affirmation of “not only the individualism of a person’s experience but [of what is] underlining the significance of a person’s response to a course of study that has been designed to ignore individuality in order to buttress nation, religion, ethnicity, family, and gender” (Grumet 2017 , p. 77). Rather than promoting neuroscience as the answer to the problems of curriculum and pedagogy, it is long-past time for rethinking curriculum development and addressing the canonical curriculum question: What knowledge is of most worth from a humanistic perspective that is structured by complicated conversation (UNESCO 2015a ; Pinar 2004 , 2019 )? It promotes respect for diversity and rejection of all forms of (cultural) hegemony, stereotypes, and biases (Pacheco 2009 , 2017 ).

Revisiting the curriculum in the Covid-19 era then expresses the fallacy of the “new normal” but also represents a particular opportunity to promote a different path forward.

Looking to the post-Covid-19 curriculum

Based on the notion of curriculum as a complicated conversation, as proposed by Pinar ( 2004 ), the post-Covid-19 curriculum can seize the possibility of achieving a responsive, ethical, humane education, one which requires a humanistic and internationally aware reconceptualization of curriculum.

While beliefs and values are anchored in social and individual practices (Pinar 2019 , p. 15), education extracts them for critique and reconsideration. For example, freedom and tolerance are not neutral but normative practices, however ideology-free policymakers imagine them to be.

That same sleight-of-hand—value neutrality in the service of a certain normativity—is evident in a digital concept of society as a relationship between humans and non-humans (or posthumans), a relationship not only mediated by but encapsulated within technology: machines interfacing with other machines. This is not merely a technological change, as if it were a quarantined domain severed from society. Technologization is a totalizing digitalization of human experience that includes the structures of society. It is less social than economic, with social bonds now recoded as financial transactions sutured by software. Now that subjectivity is digitalized, the human face has become an exclusively economic one that fabricates the fantasy of rational and free agents—always self-interested—operating in supposedly free markets. Oddly enough, there is no place for a vision of humanistic and internationally aware change. The technological dimension of curriculum is assumed to be the primary area of change, which has been deeply and totally imposed by global standards. The worldwide pandemic supports arguments for imposing forms of control (Žižek 2020 ), including the geolocation of infected people and the suspension—in a state of exception—of civil liberties.

By destroying democracy, the technology of control leads to totalitarianism and barbarism, ending tolerance, difference, and diversity. Remembrance and memory are needed so that historical fascisms (Eley 2020 ) are not repeated, albeit in new disguises (Adorno 2011 ). Technologized education enhances efficiency and ensures uniformity, while presuming objectivity to the detriment of human reflection and singularity. It imposes the running data of the Curriculum of Things and eschews intellectual endeavor, critical attitude, and self-reflexivity.

For those who advocate the primacy of technology and the so-called “free market”, the pandemic represents opportunities not only for profit but also for confirmation of the pervasiveness of human error and proof of the efficiency of the non-human, i.e., the inhuman technology. What may possibly protect children from this inhumanity and their commodification, as human capital, is a humane or humanistic education that contradicts their commodification.

The decontextualized technical vocabulary in use in a market society produces an undifferentiated image in which people are blinded to nuance, distinction, and subtlety. For Pestre, concepts associated with efficiency convey the primacy of economic activity to the exclusion, for instance, of ethics, since those concepts devalue historic (if unrealized) commitments to equality and fraternity by instead emphasizing economic freedom and the autonomy of self-interested individuals. Constructing education as solely economic and technological constitutes a movement toward total efficiency through the installation of uniformity of behavior, devaluing diversity and human creativity.

Erased from the screen is any image of public education as a space of freedom, or as Macdonald ( 1995 , p. 38) holds, any image or concept of “the dignity and integrity of each human”. Instead, what we face is the post-human and the undisputed reign of instrumental reality, where the ends justify the means and human realization is reduced to the consumption of goods and experiences. As Pinar ( 2019 , p. 7) observes: “In the private sphere…. freedom is recast as a choice of consumer goods; in the public sphere, it converts to control and the demand that freedom flourish, so that whatever is profitable can be pursued”. Such “negative” freedom—freedom from constraint—ignores “positive” freedom, which requires us to contemplate—in ethical and spiritual terms—what that freedom is for. To contemplate what freedom is for requires “critical and comprehensive knowledge” (Pestre 2013 , p. 39) not only instrumental and technical knowledge. The humanities and the arts would reoccupy the center of such a curriculum and not be related to its margins (Westbury 2008 ), acknowledging that what is studied within schools is a complicated conversation among those present—including oneself, one’s ancestors, and those yet to be born (Pinar 2004 ).

In an era of unconstrained technologization, the challenge facing the curriculum is coding and STEM (science, technology, engineering, and mathematics), with technology dislodging those subjects related to the human. This is not a classical curriculum (although it could be) but one focused on the emergencies of the moment–namely, climate change, the pandemic, mass migration, right-wing populism, and economic inequality. These timely topics, which in secondary school could be taught as short courses and at the elementary level as thematic units, would be informed by the traditional school subjects (yes, including STEM). Such a reorganization of the curriculum would allow students to see how academic knowledge enables them to understand what is happening to them and their parents in their own regions and globally. Such a cosmopolitan curriculum would prepare children to become citizens not only of their own nations but of the world. This citizenship would simultaneously be subjective and social, singular and universal (Marope 2020 ). Pinar ( 2019 , p. 5) reminds us that “the division between private and public was first blurred then erased by technology”:

No longer public, let alone sacred, morality becomes a matter of privately held values, sometimes monetized as commodities, statements of personal preference, often ornamental, sometimes self-servingly instrumental. Whatever their function, values were to be confined to the private sphere. The public sphere was no longer the civic square but rather, the marketplace, the site where one purchased whatever one valued.

New technological spaces are the universal center for (in)human values. The civic square is now Amazon, Alibaba, Twitter, WeChat, and other global online corporations. The facts of our human condition—a century-old phrase uncanny in its echoes today—can be studied in schools as an interdisciplinary complicated conversation about public issues that eclipse private ones (Pinar 2019 ), including social injustice, inequality, democracy, climate change, refugees, immigrants, and minority groups. Understood as a responsive, ethical, humane and transformational international educational approach, such a post-Covid-19 curriculum could be a “force for social equity, justice, cohesion, stability, and peace” (Marope 2017 , p. 32). “Unchosen” is certainly the adjective describing our obligations now, as we are surrounded by death and dying and threatened by privation or even starvation, as economies collapse and food-supply chains are broken. The pandemic may not mean deglobalization, but it surely accentuates it, as national borders are closed, international travel is suspended, and international trade is impacted by the accompanying economic crisis. On the other hand, economic globalization could return even stronger, as could the globalization of education systems. The “new normal” in education is the technological order—a passive technologization—and its expansion continues uncontested and even accelerated by the pandemic.

Two Greek concepts, kronos and kairos , allow a discussion of contrasts between the quantitative and the qualitative in education. Echoing the ancient notion of kronos are the technologically structured curriculum values of quantity and performance, which are always assessed by a standardized accountability system enforcing an “ideology of achievement”. “While kronos refers to chronological or sequential time, kairos refers to time that might require waiting patiently for a long time or immediate and rapid action; which course of action one chooses will depend on the particular situation” (Lahtinen 2009 , p. 252).

For Macdonald ( 1995 , p. 51), “the central ideology of the schools is the ideology of achievement …[It] is a quantitative ideology, for even to attempt to assess quality must be quantified under this ideology, and the educational process is perceived as a technically monitored quality control process”.

Self-evaluation subjectively internalizes what is useful and in conformity with the techno-economy and its so-called standards, increasingly enforcing technical (software) forms. If recoded as the Internet of Things, this remains a curriculum in allegiance with “order and control” (Doll 2013 , p. 314) School knowledge is reduced to an instrument for economic success, employing compulsory collaboration to ensure group think and conformity. Intertwined with the Internet of Things, technological subjectivity becomes embedded in software, redesigned for effectiveness, i.e., or use-value (as Lyotard predicted).

The Curriculum of Things dominates the Internet, which is simultaneously an object and a thing (see Heidegger 1967 , 1971 , 1977 ), a powerful “technological tool for the process of knowledge building” (Means 2008 , p. 137). Online learning occupies the subjective zone between the “curriculum-as-planned” and the “curriculum-as-lived” (Pinar 2019 , p. 23). The world of the curriculum-as-lived fades, as the screen shifts and children are enmeshed in an ocularcentric system of accountability and instrumentality.

In contrast to kronos , the Greek concept of kairos implies lived time or even slow time (Koepnick 2014 ), time that is “self-reflective” (Macdonald 1995 , p. 103) and autobiographical (Pinar 2009 , 2004), thus inspiring “curriculum improvisation” (Aoki 2011 , p. 375), while emphasizing “the plurality of subjectivities” (Grumet 2017 , p. 80). Kairos emphasizes singularity and acknowledges particularities; it is skeptical of similarities. For Shew ( 2013 , p. 48), “ kairos is that which opens an originary experience—of the divine, perhaps, but also of life or being. Thought as such, kairos as a formative happening—an opportune moment, crisis, circumstance, event—imposes its own sense of measure on time”. So conceived, curriculum can become a complicated conversation that occurs not in chronological time but in its own time. Such dialogue is not neutral, apolitical, or timeless. It focuses on the present and is intrinsically subjective, even in public space, as Pinar ( 2019 , p. 12) writes: “its site is subjectivity as one attunes oneself to what one is experiencing, yes to its immediacy and specificity but also to its situatedness, relatedness, including to what lies beyond it and not only spatially but temporally”.

Kairos is, then, the uniqueness of time that converts curriculum into a complicated conversation, one that includes the subjective reconstruction of learning as a consciousness of everyday life, encouraging the inner activism of quietude and disquietude. Writing about eternity, as an orientation towards the future, Pinar ( 2019 , p. 2) argues that “the second side [the first is contemplation] of such consciousness is immersion in daily life, the activism of quietude – for example, ethical engagement with others”. We add disquietude now, following the work of the Portuguese poet Fernando Pessoa. Disquietude is a moment of eternity: “Sometimes I think I’ll never leave ‘Douradores’ Street. And having written this, it seems to me eternity. Neither pleasure, nor glory, nor power. Freedom, only freedom” (Pesssoa 1991 ).

The disquietude conversation is simultaneously individual and public. It establishes an international space both deglobalized and autonomous, a source of responsive, ethical, and humane encounter. No longer entranced by the distracting dynamic stasis of image-after-image on the screen, the student can face what is his or her emplacement in the physical and natural world, as well as the technological world. The student can become present as a person, here and now, simultaneously historical and timeless.

Conclusions

Slow down and linger should be our motto now. A slogan yes, but it also represents a political, as well as a psychological resistance to the acceleration of time (Berg and Seeber 2016 )—an acceleration that the pandemic has intensified. Covid-19 has moved curriculum online, forcing children physically apart from each other and from their teachers and especially from the in-person dialogical encounters that classrooms can provide. The public space disappears into the pre-designed screen space that software allows, and the machine now becomes the material basis for a curriculum of things, not persons. Like the virus, the pandemic curriculum becomes embedded in devices that technologize our children.

Although one hundred years old, the images created in Modern Times by Charlie Chaplin return, less humorous this time than emblematic of our intensifying subjection to technological necessity. It “would seem to leave us as cogs in the machine, ourselves like moving parts, we keep functioning efficiently, increasing productivity calculating the creative destruction of what is, the human now materialized (de)vices ensnaring us in convenience, connectivity, calculation” (Pinar 2019 , p. 9). Post-human, as many would say.

Technology supports standardized testing and enforces software-designed conformity and never-ending self-evaluation, while all the time erasing lived, embodied experience and intellectual independence. Ignoring the evidence, others are sure that technology can function differently: “Given the potential of information and communication technologies, the teacher should now be a guide who enables learners, from early childhood throughout their learning trajectories, to develop and advance through the constantly expanding maze of knowledge” (UNESCO 2015a , p. 51). Would that it were so.

The canonical question—What knowledge is of most worth?—is open-ended and contentious. In a technologized world, providing for the well-being of children is not obvious, as well-being is embedded in ancient, non-neoliberal visions of the world. “Education is everybody’s business”, Pinar ( 2019 , p. 2) points out, as it fosters “responsible citizenship and solidarity in a global world” (UNESCO 2015a , p. 66), resisting inequality and the exclusion, for example, of migrant groups, refugees, and even those who live below or on the edge of poverty.

In this fast-moving digital world, education needs to be inclusive but not conformist. As the United Nations ( 2015 ) declares, education should ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. “The coming years will be a vital period to save the planet and to achieve sustainable, inclusive human development” (United Nations 2019 , p. 64). Is such sustainable, inclusive human development achievable through technologization? Can technology succeed where religion has failed?

Despite its contradictions and economic emphases, public education has one clear obligation—to create embodied encounters of learning through curriculum conceived as a complicated conversation. Such a conception acknowledges the worldliness of a cosmopolitan curriculum as it affirms the personification of the individual (Pinar 2011 ). As noted by Grumet ( 2017 , p. 89), “as a form of ethics, there is a responsibility to participate in conversation”. Certainly, it is necessary to ask over and over again the canonical curriculum question: What knowledge is of most worth?

If time, technology and teaching are moving images of eternity, curriculum and pedagogy are also, both ‘moving’ and ‘images’ but not an explicit, empirical, or exact representation of eternity…if reality is an endless series of ‘moving images’, the canonical curriculum question—What knowledge is of most worth?—cannot be settled for all time by declaring one set of subjects eternally important” (Pinar 2019 , p. 12).

In a complicated conversation, the curriculum is not a fixed image sliding into a passive technologization. As a “moving image”, the curriculum constitutes a politics of presence, an ongoing expression of subjectivity (Grumet 2017 ) that affirms the infinity of reality: “Shifting one’s attitude from ‘reducing’ complexity to ‘embracing’ what is always already present in relations and interactions may lead to thinking complexly, abiding happily with mystery” (Doll 2012 , p. 172). Describing the dialogical encounter characterizing conceived curriculum, as a complicated conversation, Pinar explains that this moment of dialogue “is not only place-sensitive (perhaps classroom centered) but also within oneself”, because “the educational significance of subject matter is that it enables the student to learn from actual embodied experience, an outcome that cannot always be engineered” (Pinar 2019 , pp. 12–13). Lived experience is not technological. So, “the curriculum of the future is not just a matter of defining content and official knowledge. It is about creating, sculpting, and finessing minds, mentalities, and identities, promoting style of thought about humans, or ‘mashing up’ and ‘making up’ the future of people” (Williamson 2013 , p. 113).

Yes, we need to linger and take time to contemplate the curriculum question. Only in this way will we share what is common and distinctive in our experience of the current pandemic by changing our time and our learning to foreclose on our future. Curriculum conceived as a complicated conversation restarts historical not screen time; it enacts the private and public as distinguishable, not fused in a computer screen. That is the “new normal”.

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Pacheco, J.A. The “new normal” in education. Prospects 51 , 3–14 (2021). https://doi.org/10.1007/s11125-020-09521-x

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COVID-19 Pandemic and Stress: Coping with the New Normal

Anjana bhattacharjee.

1 Dept. of Psychology, Tripura University, Suryamani Nagar, Tripura, India

Tatini Ghosh

COVID-19 is the new face of pandemic. Since the discovery of COVID-19 in December 2019 in Wuhan, China, it has spread all over the world and the numbers are increasing day by day. Anyone can be susceptible to this infection but children, older adults, pregnant women, and people with comorbidity are more vulnerable. The spread of coronavirus resulted in closures of schools, businesses, and public spaces worldwide and forced many communities to enact stay at home orders, causing stress to all irrespective of their age, gender, or socioeconomic status. The sudden and unexpected changes caused by the outbreak of coronavirus are overwhelming for both adults and children, causing stress and evoking negative emotions like fear, anxiety, and depression, among different populations. The aim of the paper is to ascertain how stress during this pandemic inculcates various psychological health issues like depression anxiety, OCD, panic behavior, and so on. Further, the paper is an attempt to identify different general as well as population specific coping strategies to reduce the stress level among individuals and prevent various stress-induced psychological disorders with reference to different theories and research articles.

COVID-19, also known as coronavirus disease, is a severe respiratory disease first discovered in Wuhan, China, in December 2019. Since then, it has spread globally. The World Health Organization declared it a “Public Health Emergency of International Concern” on the 30 th of January 2020. There were more than 365,000 confirmed cases worldwide by October 2020 ( World Health Organization, 2020 ). According to report by the Ministry of Health and Family Welfare, Government of India (2021), since the inception of COVID-19 in India, there were more than 10,130,000 cases till february 2021. Even though vaccination has started in India and many parts of the world, the COVID-19 cases are still increasing day by day. The common symptoms of coronavirus are fever, cough, shortness of breath, and in some cases no symptoms at all. Anyone can be susceptible to this infection but children, older adults, pregnant women, and people with comorbidity are more vulnerable to this virus ( World Health Organization, 2020 ; National Institute of Mental Health and Neurosciences, 2020 ).

According to the National Institute of Mental Health and Neurosciences ( NIMHANS, 2020 ) the sudden lockdown and restricted mobility, along with isolation and social distancing during the starting of the pandemic, has caused stress, boredom, irritation, adjustment disorder, frustration and aggressive behavior. The sudden drastic change in known usual life acted as a gateway for increasing mental illness. A study shows that any severe epidemic or outbreak in society generally has severe negative effects on the society as well on the lives of humans ( Dodgen et al., 2002 ).

The unexpected outbreak of COVID-19 and its related consequences are causing severe changes in our lifestyle. These sudden changes can be overwhelming for both adults and children causing stress and evoking negative emotions like stress, fear, anxiety, depression among different populations. Several studies stated that the most prevalent mental health issues reported during the pandemic are stress, anxiety, fear, anger, insomnia, and denial. These issues were seen among different population groups ranging from children to older people, frontline workers, and people with pre-existing physical and mental health issues ( Roy et al., 2020 ; Torales et al., 2020 ). Different studies found that stress, anxiety, and depression coincide with the COVID-19 pandemic and due to the ongoing pandemic, there is an increase in the prevalence rate of these mental health issues around the globe ( Mohindra et al., 2020 ; Xiao et al., 2020 ).

A study comprising of 113,285 participants from India, China, Spain, Italy, and Iran revealed that among this surveyed population, the prevalence of depression, anxiety and stress was 20%, 35%, and 53%, respectively, during the pandemic and it seems to be increasing day by day ( Lakhan et al., 2020 ). A similar study found prevalence of post-traumatic stress among the general population has increased from 23.88% to 24.84% during the pandemic ( Cooke et al., 2020 ). A study on racial and ethnic disparity of stress and other mental health conditions during COVID-19 showed that Hispanic adults have four times higher prevalence of psychological stress and mental health issues than any other ethnic groups in the United States, along with increases in substance use and suicidal ideation ( McKnight-Eily et al., 2021 ). Wu et al. (2020) reported in a study that life stress, especially stress associated with uncertainty, has led to mental health disorders. The higher the level of perceived uncertainty stress, the greater the prevalence of mental health disorders. The study further found that the prevalence of mental health disorders due to the COVID-19 pandemic was 22.8%.

Abbott (2021) investigated stress caused by the COVID-19 pandemic and its related consequences and found that there is an increase in prevalence of stress, anxiety, and depression in the U.S. population from 11% to 42% due to this pandemic. The surge in stress among people is also during the rise of new COVID-19 covariant cases. A recent study showed an association between high level of stress, anxiety, and sleep disturbance and the period of social distancing ( Esteves et al., 2021 ).

Currently, several vaccines are being produced and vaccination has started, resulting in the discontinuation of lockdown orders in most countries. Educational institutions and workplaces are reopening and people are returning to the new normal life, maintaining social distancing. Recently Ministry of Health and Family Welfare (2021) reported a sudden surge during December 2020 in COVID-19 cases all over the world, due to a new covariant of COVID-19 virus that was first, testified by the Government of United Kingdom (UK) to World Health Organization (WHO). The new variants are reported to be more infectious and spread more easily among people. This is again aggravating the situation and increasing stress in people even after being vaccinated due to excessive fear and uncertainty.

The objectives of this article are two-fold. First it explores how stress plays a significant role in increasing the number of mental health problems during this pandemic. Second, it tries to identify different general as well as population specific coping mechanisms for dealing with this stressful situation with reference to different theories (both on stress and coping) and research articles.

Methodology

This paper reviews secondary data available through conceptual models, various past journals, research papers, and other useful websites related to coronavirus pandemic and its psychological effect on people. Finally, the paper extensively reviews different articles related to psycho-social coping mechanisms to reduce stress level among individuals.

When conducting this review research, related articles were focused on and keywords like “coronavirus,” “coronavirus and mental health,” “COVID-19,” “stress,” “psychological disorder,” and “coping strategies” were used. In order to identify articles that focused on specific terms like, “stress,” “pandemic and mental health,” “depression,” “anxiety,” “post-traumatic stress disorder,” and other related terms were used. The databases that were used for identifying related articles were Google Scholar, Medline, PubMed, NCBI (National Center for Biotechnology Information), and various other journals. Figure 1

PRISM diagram of systematic literature review process.

The systematic review started with 1137 articles, which were screened and reviewed and some were removed on different grounds. Finally, 106 articles were selected for the review article based on aims and objectives of the paper. The selection process of different articles is shown in the following PRISM flow chart. Finally, conclusions have been made on the basis of findings from different reviewed articles. Figure 2

Table showing discussion.

Theories of Stress

Hans selye’s theory.

In the year of 1930s, 1940s, and 1950s, Han Selye elaborated on the Walter Cannon’s theory of fight-or-flight reaction to stress and named it general adaptation syndrome (GAS). In this theory, Han Selye explained GAS as a physiological reaction to stress consisting of three stage reactions, namely: alarm reaction stage, resistance stage, and exhaustion stage ( Selye, 1956 ). When a person faces a stressful situation, alarm reaction stage is initiated. During this stage the body prepares itself for fight-or-flight reaction and makes the necessary physiological changes in body. If the stressor persists, the body progress to the second stage (the resistance stage), where the body reacts in an opposite way of alarm reaction and tries to repair the body from any damage. If the stressor still continues, in the third stage (exhaustion stage), the body’s energy is depleted and the person succumbs to various mental and physical health issues caused by the extreme stress. Selye (1991) further stated that prolonged exposure to extreme stress can cause mental and physical illness, or even death.

The transactional model of Stress

The transactional model of stress developed by Lazarus and Folkman (1984) explained that the feeling of stress is cumulative in nature. The amount of stress we experience is the result of our thoughts, feelings, emotions, and behaviors attached with our evaluation of our external and internal demands. When the demands of the external and internal environment exceed the resources we possess, it causes stress. If the situational demands are more than that of the available resources, it causes stress in multiple ways: acute, episodic or intermittent, and chronic, which further result in physical and mental dysfunction.

Conservation of resources (COR) theory

This theory ( Hobfoll, 1989 ) proposed a framework of stress and what resources are needed to be conserved for physical and mental well-being, in the face of stressors. According to COR theory, the primary motive of human beings is to conserve resources and tools that would help to maintain their overall well-being. COR states that there are four primary kinds of resources (e.g., objects, conditions, personal characteristics, and energies) that help in fostering and protecting well-being. It proposes that individuals lacking in resources will be more vulnerable to experience stress and those with abundant resources will be resilient to stress ( Hobfoll et al., 1996 ).

Holmes and Rahe’s model of stress

Holmes and Rahe’s (1967) developed a model of stress associated with major life changes, which cause stress and ultimately may result in illness. The model states that there is a positive correlation between stress inducing major life changes and illness. In other words, with an increase in major life events, there is greater likelihood of developing subsequent illness.

Stress-disease model

According to the stress-disease model by Kagan and Levi (1971) , there are several components explain how stress can lead to disease. First, stressors or stressful situations (both social or psychological stressors), second, the individual psychobiological programming (genetic and predisposing factors, learning, and previous experiences), third, how an individual reacts to stress. When the three components work together, it leads to the fourth component, that is, precursors of disease, which ultimately leads to the final outcome which is physical illness. This model explains how different physiological pathways can act as a mediator between stress and physical illness/disease ( Levi & Kagan, 1971 ).

Stress-Induced Mental Health Problems During COVID-19

Humans are social animals and it is a human tendency to establish social interactions with others. Due to COVID-19, our social interactions have been cut down, thus resulting in psychological distress ( Usher et al., 2020 ). Brodeur et al. (2004) revealed that the pandemic is severely affecting our mental health and there is an increase in web searches for loneliness, anxiety, depression, suicide, and divorce. Similarly, other studies also showed that epidemic and post-epidemic situations can cause psychological problems like stress, anxiety, and stigma as well as long lasting effects like post-traumatic stress symptoms and physical conditions like migraines and headaches ( Bhugra, 2004 ; Brooks et al., 2020 ; Cheng et al., 2004 ; Duan & Zhu, 2020 ; Fan et al., 2015 ). Post-traumatic stress disorder is a serious concern in the times of the COVID-19 pandemic, and females were found to be more prone psychological problems ( Alshehri et al., 2020 ; Bridgland et al., 2021 ).

In a recent study, Dubey et al. (2020) revealed that the current pandemic situation has not only affected the health of people but also badly affected the economy of the country. It has caused fear amongst people, which they have termed as “coronaphobia.” Many studies have revealed that stress, anxiety, fear, depression, and other psychological disorders are very commonly experienced during pandemic situations. The pandemic stress has a devastating effect on mental health ( Kumar & Nayar, 2021 ; Montano & Acebes, 2020 ; Van Bortel et al., 2016 ). Many studies over the past few decades proved that the impact of psychological stress is harmful for the immune system and the body’s response to vaccines, and these findings are applicable for COVID-19 vaccine as well ( Madison et al., 2021 ; Xiang et al., 2020 ).

A study by Shrilatha and Durga (2020) revealed that during this pandemic there was a rise in the use of social media and smartphones to is more than four hours a day, and the most used app was found to be WHATSAPP . Along with the increase in social media use, the use of other apps like ZOOM and HOUSE PARTY are also increasing since people are working from home ( Chanchani & Mishra, 2020 ). Even though social media helps in connecting with others from home, still there is a big disadvantage to it. During the coronavirus pandemic, social media is overloaded with misinformation and rumors that create more stress, fear, and panic among all ( Kumar & Nayar, 2021 ). Fear of COVID-19 due to misinformation results in the spread of maladaptive, obsessive-compulsive behaviors. Fear of contamination and regular washing of hands are common symptoms of OCD. Stress during COVID-19 and unavailability of proper treatment and therapy can lead to initiation and maintenance of OCD ( Adams et al., 2018 ).

The study of Kashif et al. (2020) revealed that along with the spike in screen usage, there has been a spike in cyber-crime during the coronavirus period. It has been further reported that personal data have also been stolen and hacked. Similar studies showed that there has been an increase in the number of cyber-crimes and cyber frauds since the first case of coronavirus in China and cyber fraud can lead to fear, panic, and stress ( Gross et al., 2016 ; Lallie et al., 2020 ). At such critical times, when hard earned money is lost, it can cause mental distress that may further develop severe psychological disorders. Hence financial loss and hardships can lead to psychological distress ( Bradshaw & Ellison, 2020 ).

Increased stress also plays a key role in substance abuse and addiction ( Sinha, 2001 ), and the stress, anxiety, and increased isolation lead people to indulge in use of psychoactive substances (like smoking, drugs, and alcohol drinking) and other substance dependent behaviors (like excessive use of social media, online gaming, and pornography). This results in substance abuse disorders during the pandemic ( Clay & Parker, 2020 ; Columb et al., 2020 ).

According to the WHO (2020) , due to the current pandemic and related measures taken to control it like social distancing, lockdown, etc., there has been a rise in the hazardous use of alcohol and drug, as well as suicidal ideation and attempts. Similarly, studies by Cheung et al. (2008) and Gardner et al. (2020) showed that pandemics can increase the rate of suicide among older adults. Not only isolation and loneliness but also death of a near one from COVID-19 are also risk factors for the suicidal ideation of an individual ( Sahoo et al., 2020 ). Figure 3

Summary of review on stress induced mental health problems during COVID-19.

Theories of Coping

Haan’s model of coping, defense, and fragmentation.

Norma Haan (1963) proposed a triarchic model of coping and described how the ego processes different stressors of daily life by using coping, defense, and fragmentation. She proposed in her model that “ego process” is a psychological approach in dealing with the stressors of daily life, which ultimately helps in sustaining a realistic connection with the self and the environment ( Haan, 1969 ). Haan (1993) defined three ways of dealing with stressors: coping, defense, and fragmentation. Coping is an effort to overcome the hardships of life by reaching out and within self for resources. Defense is an unconscious mechanism that greatly helps in reducing anxiety from harmful stimuli. Fragmentation is a method to adapt or accept failure when the stress is too extreme to handle/cope and may result in psychotic behavior. Thus, by controlling belief or behavior (defense), an individual can cope with the stressors, whereas when coping or defense fails, fragmentation occurs ( Haan, 1977 ).

Lazarus and Folkman’s theory of coping

Lazarus and Folkman propounded a theory of coping with stress ( Folkman & Lazarus, 1885 , 1991 ; Lazarus & Folkman, 1984 ), and the theory emphasized how coping transactionally interacts with cognition and emotion. According to the theory ( Lazarus & Folkman, 1987 ), there are two types of coping, namely: problem-focused coping and emotion-focused coping. Problem-focused coping deals with focusing with the problem, planning, and taking action and steps proactively about the problem, which may include gathering resources, seeking social support, or taking action to change or to overcome the problematic situation. On the contrary, emotion-focused coping deals with focusing more on the emotions, while dealing and managing the emotions caused due to the stressors. Emotions can be dealt by meditation, yoga, venting out frustrations, focusing on the positive, etc. Folkman (2013) explained that coping involves resorting to both cognitive and behavioral responses to manage the internal and external stressors.

The Hardiness Theory

Kobasa (1979) had defined hardiness as a personality type that helps in overcoming stress related illness. Hardiness is a general feeling of being satisfied with the environment. Maddi and Kobasa’s (1984) , hardiness theory of coping emphasizes that a hardy person would view stressful or challenging situations as a meaningful and interesting situation and an opportunity for personal growth. Such kind of outlook towards challenges helps people to remain healthy during stress. According to the theory, there are three ways to adhere to hardiness as coping: First, “controlling” the beliefs that can influence their environment; second, “commitment” and deep involvement in their tasks and duties; third, viewing “challenges” as an opportunity for growth and working for it.

The sense of coherence theory

Sense of coherence (SOC) ( Antonovsky, 1987 ) refers to a coping technique to deal with life stressors and emotional distress. It a feeling of confidence that both internal and external environment are predictable. According to the sense of coherence theory, there are three elements that are necessary for coping with daily life stressors: comprehensibility, manageability, and meaningfulness. People with weak SOC have a pessimistic outlook that things will go wrong in the end, whereas people with strong SOC have a good understanding of life and anticipate that all will turn good in the end. By successfully applying the elements of coherency, it is possible to cope with stress without hampering the physical health.

Stress and coping social support theory

The stress and coping social support theory by Cohen and Wills (1985) explained that social support acts as a coping method that protects the people from the stresses of life and the harmful physical effect of stressors. The theory further suggests that social support promotes adaptive appraisal and coping techniques in dealing with stressful events ( Thoits, 2010 ). According to Glanz et al. (2015) there are four types of social support that assist in coping with stress: emotional support comprising love, care, understanding; information support referring to information, guidance, and counseling; appraisal support referring to providing evaluative help; and finally, instrumental support referring to the physical or action-oriented help.

Suggestive Coping Strategies (General and Specific) During Pandemic

General psycho-social coping techniques.

According to various reports by International and National Institutes, like Ministry of Health and Family Welfare, Government of India, 2021 ; National Institute of Mental Health & Neurosciences (NIMHANS), 2020 and World Health Organization, 2020 , different psychological coping strategies have been suggested to reduce stress levels among individuals and to prevent various psychological disorders. COVID-19 has increased people's psychological burden and caused severe stress, which has challenged the resilience and coping ability to overcome hardships ( Polizzi et al., 2020 ). So, it is very requisite of the moment to foster and practice some psycho-social coping strategies to overcome stresses associated with COVID-19.

• 1. Psycho-education refers to educating people about various psychological disorders and their consequences. A study showed that psycho-education has helped people to deal with psychological disorders more successfully than those who were not given psycho-education ( Vieta, 2005 ).
• 2. Acceptance is an important coping mechanism to deal with stressors. If acceptance of the prevailing circumstances is not there, then it can lead to a negative coping strategy known as denial, which is very dangerous. In denial, the person will not follow any guidelines and it may affect others as well. One study shows that acceptance is a good way to cope with stressors and their harmful physical effects ( Lindsay et al., 2018 ).
• 3. Practice Positive Thinking. One negative thought leads to another and it creates a chain reaction of negative thoughts. To break this cycle, positive thinking practice should be adopted. Positive thinking refers to the process of focusing on positive emotions and positive behavioral habits. One study on positive thinking shows that it helps in coping with stress, anxiety, and other psychological disorders as well ( Naseem & Khalid, 2010 ).
• 4. Cognitive redefinition is a psychological coping strategy to redefine or change the way we see, perceive, and feel about any situation or events. Instead of perceiving this current pandemic situation as something very stressful, cognitive redefining can help in perceiving this as time to reconnect with family and to indulge in creative activities and self-care. One study shows that cognitive redefinition or change in mindset is helpful in dealing with stressful situations ( Crum et al., 2013 ).
• 5. Limiting Social Media and News. According to The Centers for Disease Control and Prevention, too much information and news about the COVID-19 pandemic can be overwhelming and upsetting, and it can cause panic among the people. So, it has been suggested to limit social media use and listening/reading news about the current pandemic situation. It has also been suggested by WHO to read about it from trusted sources only, as, factual information can help lessen fear and panic.
• 6. Proper Sleep Hygiene. The current pandemic situation is very crucial for everyone and it is very important to be biologically fit so as to reduce the risk of COVID-19 and its associated issues. Studies proved that having proper sleep hygiene can help in dealing with stress, anxiety, mood disturbances, and other mental health problems associated with the current pandemic ( Jakupcak et al., 2020 ; Thoits, 2010 ).
• 7. Physical Fitness. It has been reported that regular physical exercise not only boosts physical health but also helps in mental health by reducing stress, anxiety, and depression. It would be helpful to do regular physical exercises at home during this pandemic to stay fit both physically and mentally ( Altena et al., 2020 ; Muraki et al., 1993 ; Sunder et al., 2020 ).
• 8. Spending Time on Hobbies. Currently, we are either locked at home or have restricted mobility due to the pandemic situation. So, there is an immense amount of time for us at home now. We can utilize the time by indulging in various activities like cooking, painting, gardening, etc. One study shows that people who indulges in hobbies in their leisure time are less likely to have mental health issues than those who do not have hobbies ( Jeoung et al., 2013 ). Reports by NIMHANS advised people to indulge in various activities so as to distract themselves from the constant worrying about the situation.
• 9. Work life Balance. During the current COVID-19 outbreak when many people are working from home to avoid contamination, it has become very important to maintain work–life balance. Work–life balance is the process of maintaining a proper balance between work and other activities of daily life in a way that one does not hamper the other. Studies also prove that maintaining a proper work life balance boosts positive mental health and reduces anxiety and depression among employees ( LaBrie et al., 2010 ).
• 10. Healthy Daily Routine. Day-to-day routines have been disrupted during the COVID-19 pandemic, and it negatively hampers our both physical and mental health. Various reports show that unhealthy life styles impede our physical and mental health whereas healthy lifestyle like eating healthy, getting enough sleep, focusing on positive thoughts, etc. can boost healthy mind and body ( Haar et al., 2014 ; Takeda et al., 2015 ).
• 11. Mindfulness Practice. Mindfulness refers to the state of physical and mental awareness of a person, without being affected by the surroundings. There are various mindfulness techniques like meditation, physical exercise, yoga, guiding imagery and spiritual practices. Studies also show that mindfulness practices help in dealing with mental and emotional disorders and also boost physical health ( Call et al., 2014 ; Koenig, 2010 ; Mayo Clinic, 2020 ; Melnyk et al., 2006 ).
• 12. Following Government Guidelines. It is being advised all the time to follow the Government guidelines to curb the COVID-19 outbreak. Some of the guidelines by the Ministry of Health and Family Welfare, Government of India suggest to maintain social distancing, wear face coverings whenever outside, wash hands using soap regularly and avoid public gathering.
• 13. Professional Help. Urgent professional help has to be sought if the person is not able to deal with the sudden life changes and if it is severely hampering their physical and mental health. If people are suffering from any kind of physical and emotional disorder, professional help like consulting a psychologist, counselor, or psychiatrist is advisable ( Wang et al., 2020 ).
• 14. Avoid Stigmatization. There has been negative attitudes and stigma about mental health exist. Due to stigma attached to mental health and mental health care providers, people hesitate to express their mental turmoil or stress, which further leads to serious psychological conditions. So, it is suggested to avoid stigmatizing mental health or mental health professionals and seek help whenever needed ( Yang, 2007 ).
• 15. Good Social Support. Studies reveal that people who have good social support are less likely to have any psychological disorders. People with good social networking will have less or no depression, suicidal thoughts, or suicidal risk in the future ( Duan & Zhu, 2020 ). So, it is important to have good social support during this pandemic situation and to stay connected with family or friends through various online mediums.

Specific coping techniques

Besides the above-mentioned general coping strategies, which are suggested for everyone irrespective of any sociodemographic differences to cope up with a stressful life, there are certain coping techniques that are being followed by people belonging to specific age, gender, and community.

• • Age Specific Coping. People of different age groups face different levels of stress, and hence, their coping techniques are different as well ( Paykel, 1983 ). Cognitive behavior therapy seems to help in reducing PTSD, stress, depression, and anxiety among youth during periods of crisis and improves resilience ( Chen et al., 2014 ) whereas, avoidant coping seems to escalate PTSD ( McGregor et al., 2015 ). Youth also seem to apply approach coping and habitual coping styles ( Steiner et al., 2002 ); active coping followed by social coping and avoidant coping style (Brown et al., 2015). Further studies revealed that older adults seem to prefer problem-focused coping in terms of stressful events ( Chen et al., 2017 ); proactive coping ( Pearman et al., 2021 ); adaptive and active strategies of coping ( Kuria, 2012 ).
• • Gender Specific Coping. Several studies have reported gender-differences in terms of coping strategies ( Matud, 2004 ; Ptacek et al., 1994 ). Men seem to resort to approach coping style (Gan et al., 2009), problem-focused approach ( Sinha & Latha, 2018 ; Tolor & Fehon, 1987 ), rational, detachment and rumination coping style ( Matud, 2004) , and cognitive hardiness ( Beasley et al., 2003 ), whereas women are found to use emotion-focused coping style ( Loukzadeh & Mazloom Bafrooi, 2013 ; Manna et al., 2007 ), avoidance coping style followed by approach coping style ( Gan et al., 2009 ), planned-breather leisure coping method ( Tsaur & Tang, 2012 ), active coping strategies ( Lin, 2016 ), and social support ( Linnabery et al., 2014 ).
• • Community Specific Coping. There are variations in the use of coping strategies based on community and racial differences. White Americans are found to use approach behavior coping style, whereas African-Americans are more likely to use avoidance cognitive coping style ( Anshel et al., 2009 ). Similarly, African-American young adults were found to resort to avoidance coping style in comparison to White young adults, who prefer problem-focused coping ( Van Gundy et al., 2015 ). White women seem to use a self-directing coping style, whereas African-American women more often use religious coping ( Ark et al., 2006 ).

COVID-19 is the new face of the pandemic, and it has put the whole world into a pause. It has become a threat to the entire civilization. The COVID-19 pandemic not only affects physical health but also severely affects the mental health of people, whether infected or not. Nationwide lockdowns, social isolation, and restricted mobility have increased the prevalence of mental health problems, and people all over the world are suffering from loneliness, feelings of helplessness, hopelessness, anxiety, stress, and adjustment disorder. Not only these, dependency on social media and alcohol and other psychoactive substances has increased, which further raises the incidents of domestic violence or intimate partner violence. So, healthcare providers should give attention to both physical and psychological well-being of the people. Hence, it is the need of the hour to follow all the physical measures suggested by the healthcare professionals to prevent COVID-19, along with practicing psycho-social coping strategies for better quality of life and overall sound health and well-being of the individual.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Tatini Ghosh https://orcid.org/0000-0002-7221-9381

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• Research article
• Open access
• Published: 15 February 2018

Blended learning: the new normal and emerging technologies

• Charles Dziuban 1 ,
• Charles R. Graham 2 ,
• Patsy D. Moskal   ORCID: orcid.org/0000-0001-6376-839X 1 ,
• Anders Norberg 3 &
• Nicole Sicilia 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  3 ( 2018 ) Cite this article

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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

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Acknowledgements

The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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Dziuban, C., Graham, C.R., Moskal, P.D. et al. Blended learning: the new normal and emerging technologies. Int J Educ Technol High Educ 15 , 3 (2018). https://doi.org/10.1186/s41239-017-0087-5

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The 'new normal': The future of education after Covid–19

The Covid-19 pandemic has resulted in an unprecedented disruption to schools and learners in England.

There has been significant focus on ‘recovering’ the existing system but there is also an opportunity to ‘build back better’. This research has identified three areas where the pandemic has the potential to open up new conversations about the future of schooling in England. These can be summarised as:

• a conversation about how our education system can prepare children for life, not just exams
• a conversation about where and how learning takes place – as well as who is involved in it
• a conversation about the need to tackle inequalities outside, as well as inside, the classroom.

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The “new normal” for researching the new normal

Posted by Isuru Samaratunga on November 19, 2020  /  0 Comments

The COVID-19 pandemic has changed life as we know. It required citizens to ‘stay at home’. Access to basic needs such as food and medicine was limited under lockdown. Social distancing forced those involved in the last mile provision of food and medicine to quickly shift to remote service provision. Similarly, schools and hospitals attempted to provide services remotely. On the other hand, those in need of such services adapted to new means of them.

The vast spectrum of behavior change triggers many research questions. Who can access those services? Is it easier for one group than the other? How do you ensure inclusion in the whole process? Who are the winners in this situation? Who is losing out? We thought of researching these questions soon after the first wave of COVID pandemic in Sri Lanka.

We are serious about methodology, about making sure we do rigorous research and acquire data that is representative. We got to work selecting research sites, populations, target groups and data collection tools etc. Sri Lanka came out of lockdown in June 2020. By August, research design was completed. We were ready to go out to the field armed with quantitative and qualitative methods and tookl, to find out how the digital landscape and everything connected with it had changed since lockdown in March 2020.

September 2020 the pandemic hit again. The second wave. Though we had talked about new normal and how it affected the world around us, we hadn’t properly understood how it affected us: the researchers. As a result, our research design was not resilient to the new normal. We had planned to conduct focus group discussions, observations and in-depth interviews to explore respondents’ experiences. The objective was to get their reflections on how they accessed basic needs during lockdown. But now we were back in lockdown and the second wave did not allow us to go to the field and research barriers encountered by people in lockdown.

Everyone was talking about going back to work in the new normal. Now we were faced with what it is like for a qualitative researcher. If you cannot carryout fieldwork, collect data and connect with your respondent how does a researcher resume work? This was the biggest challenge.

Then I found  this innovative research  conducted by UNDP. It was published about two years ago; WhatsApp-based surveys of Syrian Refugees and Host Communities in Lebanon. It was all about collecting data remotely. Respondents shared images, voice clips and video clips through WhatsApp. It was not the classic qualitative data that you collectedthrough discussions or observations. But it added a different dimension to the inquiry.

The UNDP study in Lebanon triggered us to design a qualitative research which is doable under lockdown, while maintaining social distancing. We came up with an e-diary method to research persons living under lockdown.

Respondents were recruited via phone with help from field recruiters, consent forms shared and voice consent received. Data was collected via WhatsApp. Respondents sent us messages, images and voice clips, daily, sharing their experience with key issues (access to essentials, cash, healthcare, education etc.) over a period of 14 days. At the end of the 14-day period, researchers called each respondent to discuss points of interest in detail. Instead of getting respondents’ reflections in retrospect, the e-diary gave us a live feed of information.

Though the e-diary interactions between researcher and respondent were remote, we saw that gradually therespondents opened up to the researchers.

A respondent initiates a friendly WhatsApp chat with the researcher.

In some cases, the researchers’ voice call or WhatsApp message was something that the respondents looked forward to, each day. Those who prepared special food wanted to share photos with the researcher. Children wanted to share an image of their completed homework. What was the reason for this kind of high interaction? Was it because of the data collection tool? This begs the question: what is the impact of the data collection tool on the data?

A whole new set of questions have emerged for us, on how we do our work. This is the challenge of the new normal. We have to figure out ways to resume our work. We have to innovate. And we have to keep asking questions about our innovations. This is the new normal of the researcher.

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• Published: 30 October 2023

Chicxulub impact winter sustained by fine silicate dust

• Cem Berk Senel   ORCID: orcid.org/0000-0002-7677-9597 1 , 2 ,
• Pim Kaskes   ORCID: orcid.org/0000-0002-2605-6366 2   na1 ,
• Orkun Temel 1 , 3   na1 ,
• Johan Vellekoop   ORCID: orcid.org/0000-0001-6977-693X 4 , 5 ,
• Steven Goderis   ORCID: orcid.org/0000-0002-6666-7153 2 ,
• Robert DePalma 6 , 7 ,
• Maarten A. Prins   ORCID: orcid.org/0000-0001-7679-1134 8 ,
• Philippe Claeys   ORCID: orcid.org/0000-0002-4585-7687 2 &
• Özgür Karatekin 1  

Nature Geoscience volume  16 ,  pages 1033–1040 ( 2023 ) Cite this article

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• Palaeoclimate

The Chicxulub impact is thought to have triggered a global winter at the Cretaceous-Palaeogene (K-Pg) boundary 66 million years ago. Yet the climatic consequences of the various debris injected into the atmosphere following the Chicxulub impact remain unclear, and the exact killing mechanisms of the K-Pg mass extinction remain poorly constrained. Here we present palaeoclimate simulations based on sedimentological constraints from an expanded terrestrial K-Pg boundary deposit in North Dakota, United States, to evaluate the relative and combined effects of impact-generated silicate dust and sulfur, as well as soot from wildfires, on the post-impact climate. The measured volumetric size distribution of silicate dust suggests a larger contribution of fine dust (~0.8–8.0 μm) than previously appreciated. Our simulations of the atmospheric injection of such a plume of micrometre-sized silicate dust suggest a long atmospheric lifetime of 15yr, contributing to a global-average surface temperature falling by as much as 15°C. Simulated changes in photosynthetic active solar radiation support a dust-induced photosynthetic shut-down for almost 2 yr post-impact. We suggest that, together with additional cooling contributions from soot and sulfur, this is consistent with the catastrophic collapse of primary productivity in the aftermath of the Chicxulub impact.

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Data availability

The palaeoclimate general circulation model (GCM) output data as well as the silicate dust grain-size data from the Tanis K-Pg site that support the findings of this study are publicly available in the OSF repository via https://doi.org/10.17605/OSF.IO/2CDQG . The proxy-based latest Cretaceous temperature reconstruction data are available online in the PANGEA repository: https://doi.org/10.1594/PANGAEA.879763 .

Code availability

The Python and Matlab source codes developed for reproducing the figures in this study are publicly available at the GitHub repository via github.com/cem-berk-senel/naturegeoscience-chicxulub/ . The PlanetWRF model is available upon request from https://planetwrf.com/ .

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Acknowledgements

This research is supported by the Belgian Federal Science Policy (BELSPO) through the Chicxulub BRAIN-be (Belgian Research Action through Interdisciplinary Networks) project (to P.C. & Ö.K.) and FED-tWIN project Prf-2020-038 (to J.V.), as well as the Research Foundation-Flanders (FWO; project G0A6517N, grant 12AM624N to C.B.S., grant 11E6621N to P.K., 12Z6621N to J.V., 12ZZL20N to O.T.). S.G. and P.C. acknowledge support of the VUB strategic programme. Ö.K. acknowledges the support of BELSPO through the ESA/PRODEX programme. M. Hagen and U. van Buuren (VU Amsterdam) are thanked for their assistance during the laser-diffraction particle-size analyses.

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These authors contributed equally: Pim Kaskes, Orkun Temel.

Authors and Affiliations

Reference Systems and Planetology Department, Royal Observatory of Belgium, Brussels, Belgium

Cem Berk Senel, Orkun Temel & Özgür Karatekin

Archaeology, Environmental Changes & Geo-chemistry (AMGC), Vrije Universiteit Brussel, Brussels, Belgium

Cem Berk Senel, Pim Kaskes, Steven Goderis & Philippe Claeys

Institute of Astronomy, KU Leuven, Leuven, Belgium

Orkun Temel

Division of Geology, KU Leuven, Leuven, Belgium

Johan Vellekoop

Operational Directorate Earth and History of Life, Royal Belgian Institute for Natural Sciences, Brussels, Belgium

Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK

Robert DePalma

Department of Geosciences, Florida Atlantic University, Boca Raton, FL, USA

Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Maarten A. Prins

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Contributions

C.B.S. and P.K. led the writing of the paper. C.B.S., P.K., O.T., J.V., S.G., P.C. and Ö.K. built the conceptualization of study and wrote the original text. C.B.S., P.K., O.T., J.V., S.G., R.D., M.A.P., P.C. and Ö.K. commented on and edited the original and revised manuscripts. C.B.S., O.T. and Ö.K. developed the general circulation model, implemented microphysics and radiation models and performed palaeoclimate simulations and post-processing of the results. P.K. and R.D. collected sediment samples during fieldwork at the Tanis K-Pg site in August 2017. P.K. carried out laser-diffraction grain-size analyses, with lab supervision of M.A.P. P.K. created Figs. 1 and 2 and Extended Data Fig. 1 . C.B.S. created all other figures. All authors approved the final draft of the manuscript.

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Correspondence to Cem Berk Senel .

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Extended data

Extended data fig. 1 geological context of the chicxulub impact ejecta stratigraphy at the tanis k-pg boundary site..

a The inset shows a paleogeographic reconstruction and relief map for the latest Cretaceous 42 as used in this modeling study with locations of Chicxulub and Tanis indicated. The base map is based on the latest Cretaceous paleogeographic data 42 . b Stratigraphy of the Tanis K-Pg boundary event deposit highlighting the lithological units (adapted from 41 ; based on sections X-2741-A and X-2761) together with data on grain-size classes (clay, silt, and sand fractions), median grain-size values (in µm) and different types of impact ejecta found within this deposit. HCF = Hell Creek Formation (Upper Cretaceous). FUF = Fort Union Formation (Paleocene). c Representative grain-size distribution curves throughout the section, the colors match the stratigraphic units in b. The uppermost K-Pg boundary claystone (unit P1), indicated with a bold orange line, corresponds to final phases of atmospheric fallout of silicate dust injected by the impact and is used in present GCM simulations.

Extended Data Fig. 2 Mass and number density spectra of the grain-size dataset.

a Mass density spectrum of Tanis sediment sample X-2761-8A displayed by filled orange circles, which corresponds to the uppermost K-Pg claystone interval just below a Paleocene lignite and yields a median grain-size of 2.88 µm. The measured mass density spectrum was fitted by a trimodal lognormal size distribution as depicted by the blue solid line. This fitted model curve is the sum of lognormal size distributions comprising 3 modes. Model parameters are as follows: w 0  = 0.002, D pg,0  = 0.18 μm, lnσg,0 = 0.3087 (mode 1, green solid line), w 0  = 0.125, D pg,0  = 2.6 μm, ln σg,0  = 1.1193 (mode 2, cyan solid line), w 0  = 0.01, D pg,0  = 30 μm, ln σg,0  = 0.7284 (mode 3, magenta solid line), b Converted grain-size distribution into number density spectrum displayed by filled cyan circles. The converted spectrum is fitted by a lognormal size distribution (blue solid line), which is the input parameter for our GCM study. Converted model median grain-size corresponds to 0.125 µm with a logarithmic standard deviation of 0.446.

Extended Data Fig. 3 Number density spectrum of soot along with the particle size from K-Pg boundary layer.

It indicates a median diameter of 0.22 μm (Toon et al., 2016 39 ; Wolbach et al., 1985 20 ). Concerning the aerosol life cycle and processes, coagulation is one of the crucial microphysical mechanisms that might pose as important for the transport of aerosols in the atmosphere. Nanometric particles below 0.1 μm, that is, 0.015 μm < D p  < 0.052 μm 79 , whose range is referred to as the Aitken-mode (within the cyan dashed lines), are formed by two processes: (i) condensational growth on existing aerosol particles, and (ii) coagulation due to the random particle collisions. These nanometric particles can further grow into larger particles or chains, resulting in the so-called accumulation-mode (0.056 μm < D p  < 0.26 μm) 79 (within the red dashed lines) where the coagulation can occur especially at high particle concentrations following the K-Pg impact. The median diameter of soot (0.22 μm 20 , 39 ) in our simulations are prominently larger than the Aitken-mode interval, while lying within the range of accumulation-mode.

Extended Data Fig. 4 Latest Cretaceous surface temperatures from our GCM simulations, one year before the impact, in comparison with proxy observations 43 .

Proxy temperature data are presented as mean values +/- standard error of mean (SEM), displayed by black circles and horizontal error bars. Here, the proxy data consists of N = 66 samples at different latitudes. Green solid and dashed lines display the zonal mean of land temperatures during the boreal summer and winter seasons, from GCM simulations. Blue solid (boreal summer) and dashed (boreal winter) lines indicate the zonal mean of ocean temperatures. Both green (land) and blue (ocean) shaded areas show the region between the mean boreal summer and winter profiles. The black solid line refers to the GCM-based annual average of land and ocean surface temperatures at each latitude.

Extended Data Fig. 5 Global surface temperature reconstructions using the combined fine-grained ejecta scenario.

Results are displayed on a latest Cretaceous paleogeographic map (Extended Data Fig. 1a ) and shown for different time snapshots. a Latest Cretaceous, 1 year before impact (annual mean). b Latest Cretaceous, 1 week before impact. c Impact winter, 1 month after impact. d 6 months after impact. e 2 years after impact. f 10 years after impact (annual-mean). Base maps are based on the latest Cretaceous paleogeographic data 42 .

Extended Data Fig. 6 Impact-generated global surface net radiative responses.

The temporal evolution from ~2 years of the latest Cretaceous towards 25 years of post-impact conditions, for the individual silicate dust, sulfur, soot, and combined scenarios. a Global-average surface net shortwave radiation flux. b Global-average surface net longwave radiation flux. Here in x-axis, the year of 0 refers to the start of the year where the impact event occurs. The solid purple dashed line denotes the moment of Chicxulub impact, that is, boreal spring season 38 . Our paleoclimate simulations indicate that the drastic changes in surface net shortwave/longwave radiation stabilize to pre-impact levels within the first 3 years after impact. Accordingly, this timescale, in which large radiative anomalies emerged, determines the timescale of the initial extreme cold (Fig. 4a ).

Extended Data Fig. 7 Global PAR flux reconstructions in the latest Cretaceous.

Land-Ocean PAR flux a 1 day before impact (boreal spring) and b 6 months before impact (austral summer), displayed on a latest Cretaceous paleogeographic map (Extended Data Fig. 1a ). The range of the green and purple colorbar represents the photosynthetically high and low radiative flux, varying between 0-160 W/m 2 . Base maps are based on the latest Cretaceous paleogeographic data 42 .

Extended Data Fig. 8 PAR flux reconstructions following the Chicxulub impact.

Land and ocean PAR flux from 1 day (post-impact state, instantaneous) to 1 and 2 weeks after impact for a silicate dust; b sulfur; and c soot scenarios, displayed on a latest Cretaceous paleogeographic map (Extended Data Fig. 1a ). The range of the green-white colorbar denotes the photosynthetically high and low radiative flux. Base maps are based on the latest Cretaceous paleogeographic data 42 .

Extended Data Fig. 9 Effect of silicate dust particle size on the global column-integrated fine-grained ejecta mass.

Orange line refers to the present study, using the Tanis K-Pg silicate dust. Gray and black lines show GCM results using particle size constraints reported in previous modeling studies 18 , 39 . The gray line displays the response of nanometric sized particles, indicating a deposition rate with an atmospheric lifetime of ~7 years (as a lower threshold). The black line refers to the type 2 spherules 39 , representing microkrystites of 250 µm in diameter prone to very swift gravitational settling within a few days after impact. Cyan dashed line displays the shocked ejected quartz grains (mean diameter of 50 µm) defined as clastic debris 27 . We use the same amount of ejecta release in each GCM simulation, in the order of 2×10 18  g as an upper limit. The optical properties of nanoparticles and type 2 spherules are the same as in the Tanis K-Pg silicate dust simulation, as we compare the microphysical response to the changes in particle size. Here in x-axis, the year of 0 refers to the start of the year where the impact event occurs. The purple dashed line denotes the moment of Chicxulub impact, that is, boreal spring season 38 . Regarding nanoparticles, those nanometric sized particles (median diameter of 20 nm) would grow into larger particles in atmosphere due to the coagulation. Such larger aggregates would have lower deposition rates on land and ocean (Fig. 3 ), hence higher atmospheric lifetimes. To illustrate, the deposition rate of nanoparticles (gray dashed line) would have occasionally shifted rightward through the response of silicate dust (orange dashed line) depending on the rate of coagulation. Therefore, the present simulation of nanoparticles, excluding coagulation, would serve as the minimum threshold for the atmospheric lifetime (t ~ 7 years). The inclusion of coagulation mechanism forming larger aggregates would lead to lower deposition rates on land and ocean (Fig. 3 ) for some fraction of nanoparticles, thus relatively high atmospheric lifetime of more than 7 years. Nevertheless, we do not expect nanoparticles to have an atmospheric lifetime and PAR response as substantial as single soot or micrometer-sized silicate dust.

Extended Data Fig. 10 Global-average surface temperature.

It is same as Fig. 4a , yet the time evolution is shown from 15 years before the Chicxulub impact instead of 2 years, for the individual silicate dust, sulfur, soot, and combined scenarios. The first 15 years correspond to the model initial spin-up simulation of 15-years, in which the latest Cretaceous conditions stabilized.

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Senel, C.B., Kaskes, P., Temel, O. et al. Chicxulub impact winter sustained by fine silicate dust. Nat. Geosci. 16 , 1033–1040 (2023). https://doi.org/10.1038/s41561-023-01290-4

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DOI : https://doi.org/10.1038/s41561-023-01290-4

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