Dissertation Structure & Layout 101: How to structure your dissertation, thesis or research project.
By: Derek Jansen (MBA) Reviewed By: David Phair (PhD) | July 2019
So, you’ve got a decent understanding of what a dissertation is , you’ve chosen your topic and hopefully you’ve received approval for your research proposal . Awesome! Now its time to start the actual dissertation or thesis writing journey.
To craft a high-quality document, the very first thing you need to understand is dissertation structure . In this post, we’ll walk you through the generic dissertation structure and layout, step by step. We’ll start with the big picture, and then zoom into each chapter to briefly discuss the core contents. If you’re just starting out on your research journey, you should start with this post, which covers the big-picture process of how to write a dissertation or thesis .
*The Caveat *
In this post, we’ll be discussing a traditional dissertation/thesis structure and layout, which is generally used for social science research across universities, whether in the US, UK, Europe or Australia. However, some universities may have small variations on this structure (extra chapters, merged chapters, slightly different ordering, etc).
So, always check with your university if they have a prescribed structure or layout that they expect you to work with. If not, it’s safe to assume the structure we’ll discuss here is suitable. And even if they do have a prescribed structure, you’ll still get value from this post as we’ll explain the core contents of each section.
Overview: S tructuring a dissertation or thesis
- Acknowledgements page
- Abstract (or executive summary)
- Table of contents , list of figures and tables
- Chapter 1: Introduction
- Chapter 2: Literature review
- Chapter 3: Methodology
- Chapter 4: Results
- Chapter 5: Discussion
- Chapter 6: Conclusion
- Reference list
As I mentioned, some universities will have slight variations on this structure. For example, they want an additional “personal reflection chapter”, or they might prefer the results and discussion chapter to be merged into one. Regardless, the overarching flow will always be the same, as this flow reflects the research process , which we discussed here – i.e.:
- The introduction chapter presents the core research question and aims .
- The literature review chapter assesses what the current research says about this question.
- The methodology, results and discussion chapters go about undertaking new research about this question.
- The conclusion chapter (attempts to) answer the core research question .
In other words, the dissertation structure and layout reflect the research process of asking a well-defined question(s), investigating, and then answering the question – see below.
To restate that – the structure and layout of a dissertation reflect the flow of the overall research process . This is essential to understand, as each chapter will make a lot more sense if you “get” this concept. If you’re not familiar with the research process, read this post before going further.
Right. Now that we’ve covered the big picture, let’s dive a little deeper into the details of each section and chapter. Oh and by the way, you can also grab our free dissertation/thesis template here to help speed things up.
The title page of your dissertation is the very first impression the marker will get of your work, so it pays to invest some time thinking about your title. But what makes for a good title? A strong title needs to be 3 things:
- Succinct (not overly lengthy or verbose)
- Specific (not vague or ambiguous)
- Representative of the research you’re undertaking (clearly linked to your research questions)
Typically, a good title includes mention of the following:
- The broader area of the research (i.e. the overarching topic)
- The specific focus of your research (i.e. your specific context)
- Indication of research design (e.g. quantitative , qualitative , or mixed methods ).
A quantitative investigation [research design] into the antecedents of organisational trust [broader area] in the UK retail forex trading market [specific context/area of focus].
Again, some universities may have specific requirements regarding the format and structure of the title, so it’s worth double-checking expectations with your institution (if there’s no mention in the brief or study material).
This page provides you with an opportunity to say thank you to those who helped you along your research journey. Generally, it’s optional (and won’t count towards your marks), but it is academic best practice to include this.
So, who do you say thanks to? Well, there’s no prescribed requirements, but it’s common to mention the following people:
- Your dissertation supervisor or committee.
- Any professors, lecturers or academics that helped you understand the topic or methodologies.
- Any tutors, mentors or advisors.
- Your family and friends, especially spouse (for adult learners studying part-time).
There’s no need for lengthy rambling. Just state who you’re thankful to and for what (e.g. thank you to my supervisor, John Doe, for his endless patience and attentiveness) – be sincere. In terms of length, you should keep this to a page or less.
Abstract or executive summary
The dissertation abstract (or executive summary for some degrees) serves to provide the first-time reader (and marker or moderator) with a big-picture view of your research project. It should give them an understanding of the key insights and findings from the research, without them needing to read the rest of the report – in other words, it should be able to stand alone .
For it to stand alone, your abstract should cover the following key points (at a minimum):
- Your research questions and aims – what key question(s) did your research aim to answer?
- Your methodology – how did you go about investigating the topic and finding answers to your research question(s)?
- Your findings – following your own research, what did do you discover?
- Your conclusions – based on your findings, what conclusions did you draw? What answers did you find to your research question(s)?
So, in much the same way the dissertation structure mimics the research process, your abstract or executive summary should reflect the research process, from the initial stage of asking the original question to the final stage of answering that question.
In practical terms, it’s a good idea to write this section up last , once all your core chapters are complete. Otherwise, you’ll end up writing and rewriting this section multiple times (just wasting time). For a step by step guide on how to write a strong executive summary, check out this post .
Need a helping hand?
Table of contents
This section is straightforward. You’ll typically present your table of contents (TOC) first, followed by the two lists – figures and tables. I recommend that you use Microsoft Word’s automatic table of contents generator to generate your TOC. If you’re not familiar with this functionality, the video below explains it simply:
If you find that your table of contents is overly lengthy, consider removing one level of depth. Oftentimes, this can be done without detracting from the usefulness of the TOC.
Right, now that the “admin” sections are out of the way, its time to move on to your core chapters. These chapters are the heart of your dissertation and are where you’ll earn the marks. The first chapter is the introduction chapter – as you would expect, this is the time to introduce your research…
It’s important to understand that even though you’ve provided an overview of your research in your abstract, your introduction needs to be written as if the reader has not read that (remember, the abstract is essentially a standalone document). So, your introduction chapter needs to start from the very beginning, and should address the following questions:
- What will you be investigating (in plain-language, big picture-level)?
- Why is that worth investigating? How is it important to academia or business? How is it sufficiently original?
- What are your research aims and research question(s)? Note that the research questions can sometimes be presented at the end of the literature review (next chapter).
- What is the scope of your study? In other words, what will and won’t you cover ?
- How will you approach your research? In other words, what methodology will you adopt?
- How will you structure your dissertation? What are the core chapters and what will you do in each of them?
These are just the bare basic requirements for your intro chapter. Some universities will want additional bells and whistles in the intro chapter, so be sure to carefully read your brief or consult your research supervisor.
If done right, your introduction chapter will set a clear direction for the rest of your dissertation. Specifically, it will make it clear to the reader (and marker) exactly what you’ll be investigating, why that’s important, and how you’ll be going about the investigation. Conversely, if your introduction chapter leaves a first-time reader wondering what exactly you’ll be researching, you’ve still got some work to do.
Now that you’ve set a clear direction with your introduction chapter, the next step is the literature review . In this section, you will analyse the existing research (typically academic journal articles and high-quality industry publications), with a view to understanding the following questions:
- What does the literature currently say about the topic you’re investigating?
- Is the literature lacking or well established? Is it divided or in disagreement?
- How does your research fit into the bigger picture?
- How does your research contribute something original?
- How does the methodology of previous studies help you develop your own?
Depending on the nature of your study, you may also present a conceptual framework towards the end of your literature review, which you will then test in your actual research.
Again, some universities will want you to focus on some of these areas more than others, some will have additional or fewer requirements, and so on. Therefore, as always, its important to review your brief and/or discuss with your supervisor, so that you know exactly what’s expected of your literature review chapter.
Now that you’ve investigated the current state of knowledge in your literature review chapter and are familiar with the existing key theories, models and frameworks, its time to design your own research. Enter the methodology chapter – the most “science-ey” of the chapters…
In this chapter, you need to address two critical questions:
- Exactly HOW will you carry out your research (i.e. what is your intended research design)?
- Exactly WHY have you chosen to do things this way (i.e. how do you justify your design)?
Remember, the dissertation part of your degree is first and foremost about developing and demonstrating research skills . Therefore, the markers want to see that you know which methods to use, can clearly articulate why you’ve chosen then, and know how to deploy them effectively.
Importantly, this chapter requires detail – don’t hold back on the specifics. State exactly what you’ll be doing, with who, when, for how long, etc. Moreover, for every design choice you make, make sure you justify it.
In practice, you will likely end up coming back to this chapter once you’ve undertaken all your data collection and analysis, and revise it based on changes you made during the analysis phase. This is perfectly fine. Its natural for you to add an additional analysis technique, scrap an old one, etc based on where your data lead you. Of course, I’m talking about small changes here – not a fundamental switch from qualitative to quantitative, which will likely send your supervisor in a spin!
You’ve now collected your data and undertaken your analysis, whether qualitative, quantitative or mixed methods. In this chapter, you’ll present the raw results of your analysis . For example, in the case of a quant study, you’ll present the demographic data, descriptive statistics, inferential statistics , etc.
Typically, Chapter 4 is simply a presentation and description of the data, not a discussion of the meaning of the data. In other words, it’s descriptive, rather than analytical – the meaning is discussed in Chapter 5. However, some universities will want you to combine chapters 4 and 5, so that you both present and interpret the meaning of the data at the same time. Check with your institution what their preference is.
Now that you’ve presented the data analysis results, its time to interpret and analyse them. In other words, its time to discuss what they mean, especially in relation to your research question(s).
What you discuss here will depend largely on your chosen methodology. For example, if you’ve gone the quantitative route, you might discuss the relationships between variables . If you’ve gone the qualitative route, you might discuss key themes and the meanings thereof. It all depends on what your research design choices were.
Most importantly, you need to discuss your results in relation to your research questions and aims, as well as the existing literature. What do the results tell you about your research questions? Are they aligned with the existing research or at odds? If so, why might this be? Dig deep into your findings and explain what the findings suggest, in plain English.
The final chapter – you’ve made it! Now that you’ve discussed your interpretation of the results, its time to bring it back to the beginning with the conclusion chapter . In other words, its time to (attempt to) answer your original research question s (from way back in chapter 1). Clearly state what your conclusions are in terms of your research questions. This might feel a bit repetitive, as you would have touched on this in the previous chapter, but its important to bring the discussion full circle and explicitly state your answer(s) to the research question(s).
Next, you’ll typically discuss the implications of your findings? In other words, you’ve answered your research questions – but what does this mean for the real world (or even for academia)? What should now be done differently, given the new insight you’ve generated?
Lastly, you should discuss the limitations of your research, as well as what this means for future research in the area. No study is perfect, especially not a Masters-level. Discuss the shortcomings of your research. Perhaps your methodology was limited, perhaps your sample size was small or not representative, etc, etc. Don’t be afraid to critique your work – the markers want to see that you can identify the limitations of your work. This is a strength, not a weakness. Be brutal!
This marks the end of your core chapters – woohoo! From here on out, it’s pretty smooth sailing.
The reference list is straightforward. It should contain a list of all resources cited in your dissertation, in the required format, e.g. APA , Harvard, etc.
It’s essential that you use reference management software for your dissertation. Do NOT try handle your referencing manually – its far too error prone. On a reference list of multiple pages, you’re going to make mistake. To this end, I suggest considering either Mendeley or Zotero. Both are free and provide a very straightforward interface to ensure that your referencing is 100% on point. I’ve included a simple how-to video for the Mendeley software (my personal favourite) below:
Some universities may ask you to include a bibliography, as opposed to a reference list. These two things are not the same . A bibliography is similar to a reference list, except that it also includes resources which informed your thinking but were not directly cited in your dissertation. So, double-check your brief and make sure you use the right one.
The very last piece of the puzzle is the appendix or set of appendices. This is where you’ll include any supporting data and evidence. Importantly, supporting is the keyword here.
Your appendices should provide additional “nice to know”, depth-adding information, which is not critical to the core analysis. Appendices should not be used as a way to cut down word count (see this post which covers how to reduce word count ). In other words, don’t place content that is critical to the core analysis here, just to save word count. You will not earn marks on any content in the appendices, so don’t try to play the system!
Time to recap…
And there you have it – the traditional dissertation structure and layout, from A-Z. To recap, the core structure for a dissertation or thesis is (typically) as follows:
- Acknowledgments page
Most importantly, the core chapters should reflect the research process (asking, investigating and answering your research question). Moreover, the research question(s) should form the golden thread throughout your dissertation structure. Everything should revolve around the research questions, and as you’ve seen, they should form both the start point (i.e. introduction chapter) and the endpoint (i.e. conclusion chapter).
I hope this post has provided you with clarity about the traditional dissertation/thesis structure and layout. If you have any questions or comments, please leave a comment below, or feel free to get in touch with us. Also, be sure to check out the rest of the Grad Coach Blog .
Psst… there’s more (for free)
This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project.
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many thanks i found it very useful
Glad to hear that, Arun. Good luck writing your dissertation.
Such clear practical logical advice. I very much needed to read this to keep me focused in stead of fretting.. Perfect now ready to start my research!
what about scientific fields like computer or engineering thesis what is the difference in the structure? thank you very much
Thanks so much this helped me a lot!
Very helpful and accessible. What I like most is how practical the advice is along with helpful tools/ links.
Thank you so much sir.. It was really helpful..
Hi! How many words maximum should contain the abstract?
Thank you so much 😊 Find this at the right moment
You’re most welcome. Good luck with your dissertation.
best ever benefit i got on right time thank you
Many times Clarity and vision of destination of dissertation is what makes the difference between good ,average and great researchers the same way a great automobile driver is fast with clarity of address and Clear weather conditions .
I guess Great researcher = great ideas + knowledge + great and fast data collection and modeling + great writing + high clarity on all these
You have given immense clarity from start to end.
Morning. Where will I write the definitions of what I’m referring to in my report?
Thank you so much Derek, I was almost lost! Thanks a tonnnn! Have a great day!
Thanks ! so concise and valuable
This was very helpful. Clear and concise. I know exactly what to do now.
Thank you for allowing me to go through briefly. I hope to find time to continue.
Really useful to me. Thanks a thousand times
Very interesting! It will definitely set me and many more for success. highly recommended.
Thank you soo much sir, for the opportunity to express my skills
Usefull, thanks a lot. Really clear
Very nice and easy to understand. Thank you .
That was incredibly useful. Thanks Grad Coach Crew!
My stress level just dropped at least 15 points after watching this. Just starting my thesis for my grad program and I feel a lot more capable now! Thanks for such a clear and helpful video, Emma and the GradCoach team!
Do we need to mention the number of words the dissertation contains in the main document?
It depends on your university’s requirements, so it would be best to check with them 🙂
Such a helpful post to help me get started with structuring my masters dissertation, thank you!
Great video; I appreciate that helpful information
It is so necessary or avital course
This blog is very informative for my research. Thank you
Doctoral students are required to fill out the National Research Council’s Survey of Earned Doctorates
wow this is an amazing gain in my life
This is so good
How can i arrange my specific objectives in my dissertation?
- What Is A Literature Review (In A Dissertation Or Thesis) - Grad Coach - […] is to write the actual literature review chapter (this is usually the second chapter in a typical dissertation or…
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How to Write a Thesis or Dissertation Introduction
Published on 9 September 2022 by Tegan George and Shona McCombes.
The introduction is the first section of your thesis or dissertation , appearing right after the table of contents . Your introduction draws your reader in, setting the stage for your research with a clear focus, purpose, and direction.
Your introduction should include:
- Your topic, in context: what does your reader need to know to understand your thesis dissertation?
- Your focus and scope: what specific aspect of the topic will you address?
- The relevance of your research: how does your work fit into existing studies on your topic?
- Your questions and objectives: what does your research aim to find out, and how?
- An overview of your structure: what does each section contribute to the overall aim?
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Table of contents
How to start your introduction, topic and context, focus and scope, relevance and importance, questions and objectives, overview of the structure, thesis introduction example, introduction checklist, frequently asked questions about introductions.
Although your introduction kicks off your dissertation, it doesn’t have to be the first thing you write – in fact, it’s often one of the very last parts to be completed (just before your abstract ).
It’s a good idea to write a rough draft of your introduction as you begin your research, to help guide you. If you wrote a research proposal , consider using this as a template, as it contains many of the same elements. However, be sure to revise your introduction throughout the writing process, making sure it matches the content of your ensuing sections.
Prevent plagiarism, run a free check.
Begin by introducing your research topic and giving any necessary background information. It’s important to contextualise your research and generate interest. Aim to show why your topic is timely or important. You may want to mention a relevant news item, academic debate, or practical problem.
After a brief introduction to your general area of interest, narrow your focus and define the scope of your research.
You can narrow this down in many ways, such as by:
- Geographical area
- Time period
- Demographics or communities
- Themes or aspects of the topic
It’s essential to share your motivation for doing this research, as well as how it relates to existing work on your topic. Further, you should also mention what new insights you expect it will contribute.
Start by giving a brief overview of the current state of research. You should definitely cite the most relevant literature, but remember that you will conduct a more in-depth survey of relevant sources in the literature review section, so there’s no need to go too in-depth in the introduction.
Depending on your field, the importance of your research might focus on its practical application (e.g., in policy or management) or on advancing scholarly understanding of the topic (e.g., by developing theories or adding new empirical data). In many cases, it will do both.
Ultimately, your introduction should explain how your thesis or dissertation:
- Helps solve a practical or theoretical problem
- Addresses a gap in the literature
- Builds on existing research
- Proposes a new understanding of your topic
Perhaps the most important part of your introduction is your questions and objectives, as it sets up the expectations for the rest of your thesis or dissertation. How you formulate your research questions and research objectives will depend on your discipline, topic, and focus, but you should always clearly state the central aim of your research.
If your research aims to test hypotheses , you can formulate them here. Your introduction is also a good place for a conceptual framework that suggests relationships between variables .
- Conduct surveys to collect data on students’ levels of knowledge, understanding, and positive/negative perceptions of government policy.
- Determine whether attitudes to climate policy are associated with variables such as age, gender, region, and social class.
- Conduct interviews to gain qualitative insights into students’ perspectives and actions in relation to climate policy.
To help guide your reader, end your introduction with an outline of the structure of the thesis or dissertation to follow. Share a brief summary of each chapter, clearly showing how each contributes to your central aims. However, be careful to keep this overview concise: 1-2 sentences should be enough.
Human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell & Burger, 1997). This pre-verbal message is then grammatically coded, during which a syntactic representation of the utterance is built.
Speech, language, and voice disorders affect the vocal cords, nerves, muscles, and brain structures, which result in a distorted language reception or speech production (Sataloff & Hawkshaw, 2014). The symptoms vary from adding superfluous words and taking pauses to hoarseness of the voice, depending on the type of disorder (Dodd, 2005). However, distortions of the speech may also occur as a result of a disease that seems unrelated to speech, such as multiple sclerosis or chronic obstructive pulmonary disease.
This study aims to determine which acoustic parameters are suitable for the automatic detection of exacerbations in patients suffering from chronic obstructive pulmonary disease (COPD) by investigating which aspects of speech differ between COPD patients and healthy speakers and which aspects differ between COPD patients in exacerbation and stable COPD patients.
I have introduced my research topic in an engaging way.
I have provided necessary context to help the reader understand my topic.
I have clearly specified the focus of my research.
I have shown the relevance and importance of the dissertation topic .
I have clearly stated the problem or question that my research addresses.
I have outlined the specific objectives of the research .
I have provided an overview of the dissertation’s structure .
You've written a strong introduction for your thesis or dissertation. Use the other checklists to continue improving your dissertation.
The introduction of a research paper includes several key elements:
- A hook to catch the reader’s interest
- Relevant background on the topic
- Details of your research problem
- A thesis statement or research question
- Sometimes an outline of the paper
Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.
This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .
Research objectives describe what you intend your research project to accomplish.
They summarise the approach and purpose of the project and help to focus your research.
Your objectives should appear in the introduction of your research paper , at the end of your problem statement .
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- Aims and Objectives – A Guide for Academic Writing
- Doing a PhD
One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.
There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.
This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.
What Are Aims and Objectives?
A research aim describes the main goal or the overarching purpose of your research project.
In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .
A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).
To give an example, we have extracted the following research aim from a real PhD thesis:
Example of a Research Aim
The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.
Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.
Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.
They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.
Following the example of the research aim shared above, here are it’s real research objectives as an example:
Example of a Research Objective
- Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
- Investigate the number, velocity and position of impacts needed to insert a cup.
- Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
- Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
- Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
- Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
- Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
- Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.
It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.
Taking the first three research objectives as an example, they can be restructured into research questions as follows:
Restructuring Research Objectives as Research Questions
- Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
- What is the number, velocity and position of impacts needed to insert a cup?
- What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?
Difference Between Aims and Objectives
Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:
- The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
- Research aims are relatively broad; research objectives are specific.
- Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
- A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.
How to Write Aims and Objectives
Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.
Regardless, there are some basic principles that you should observe for good practice; these principles are described below.
Your aim should be made up of three parts that answer the below questions:
- Why is this research required?
- What is this research about?
- How are you going to do it?
The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.
The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.
Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.
Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.
Each of your research objectives should be SMART :
- Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
- Measurable – how will you measure progress and determine when you have achieved the action?
- Achievable – do you have the support, resources and facilities required to carry out the action?
- Relevant – is the action essential to the achievement of your research aim?
- Timebound – can you realistically complete the action in the available time alongside your other research tasks?
In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:
Table of Research Verbs to Use in Aims and Objectives
Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.
To bring all this together, let’s compare the first research objective in the previous example with the above guidance:
Checking Research Objective Example Against Recommended Approach
1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
Checking Against Recommended Approach:
Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).
Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.
Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.
Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.
Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.
Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.
Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.
Mistakes in Writing Research Aims and Objectives
1. making your research aim too broad.
Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .
Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.
2. Making Your Research Objectives Too Ambitious
Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.
3. Formulating Repetitive Research Objectives
Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.
Fortunately, this oversight can be easily avoided by using SMART objectives.
Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.
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Our next-generation model: Gemini 1.5
Feb 15, 2024
The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.
A note from Google and Alphabet CEO Sundar Pichai:
Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .
Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.
This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.
Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.
Introducing Gemini 1.5
By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team
This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.
Today, we’re announcing our next-generation model: Gemini 1.5.
Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.
The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.
Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.
As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.
These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.
Context lengths of leading foundation models
Highly efficient architecture
Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.
Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.
Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.
Greater context, more helpful capabilities
An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.
Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.
This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.
Complex reasoning about vast amounts of information
1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.
Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.
Better understanding and reasoning across modalities
1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.
Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.
Relevant problem-solving with longer blocks of code
1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.
Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.
When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.
Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.
Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.
As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.
For more details, see our Gemini 1.5 Pro technical report .
Extensive ethics and safety testing
In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.
Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.
In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.
Build and experiment with Gemini models
We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.
Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .
We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.
Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.
Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.
Learn more about Gemini’s capabilities and see how it works .
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How technology is reinventing education
Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.
Image credit: Claire Scully
New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.
“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”
For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.
Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.
AI in the classroom
In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.
AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”
He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”
The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.
The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.
“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”
Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”
Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.
“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”
Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.
Data-gathering and analysis
The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.
But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.
The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.
With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.
Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”
A website from the College of Agricultural and Environmental Sciences
Byrd Presents Dissertation Research at Southern Region AAAE in Atlanta
Allison (Fortner) Byrd
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Allison Byrd presented preliminary findings from her dissertation at the Southern Region meeting of the American Association for Agricultural Education (SR-AAAE), held February 4-6 in Atlanta, Georgia.
With help from her coauthors and dissertation committee members Dr. Alexa Lamm , Dr. Kevan Lamm, Dr. Jessica Holt, and Dr. Rochelle Sapp, Allison surveyed graduate students at the University of Georgia to understand their personal characteristics and the communication tools they used when searching for graduate programs. As part of Allison’s larger dissertation study, the goal of the research presented at SR-AAAE was to help understand prospective graduate students and how to strategically communicate with them based on their specific needs.
The findings revealed that, of the 121 respondents, the communication tools most used were departmental websites, graduate school websites, email communication, and individual faculty members’ lab websites. Respondents were also asked to indicate how much time they would dedicate to utilizing each communication channel when presented with a variety of communication tools and only 8 hours to search for graduate programs. The most frequently used communication channels were departmental websites, graduate school websites, and individual faculty members’ lab websites.
To better understand how graduate students’ personal characteristics were associated with their frequency of communication tool use, researchers ran the tests to determine if there were associations between frequency of use of departmental websites, graduate school websites, and individual faculty members’ lab websites and the following student characteristics:
- Enrollment status: Full-time students vs. part-time students
- Degree level: Master’s students vs. Ph.D. students
- Funding type: Assistantship or fellowship funding vs. Tuition assistance from state (TAP) or self-funding
The findings revealed there were no strong associations between departmental websites and student characteristics. Part-time students and TAP/self-funded students spent more time on graduate school websites. Full-time students, Ph.D. students, and students receiving assistantship or fellowship funding spent more time on individual faculty members’ lab websites.
The findings in this study were preliminary because she surveyed over 1,000 respondents from eight other colleges of agriculture across the United States whose responses will be included in her dissertation findings.
SR-AAAE was held in conjunction with the Southern Association of Agricultural Scientists annual meeting. To learn about additional Lamm Lab presentations held at this convergence of conferences, see our post on the Southern Region meeting of the American Society for Horticultural Sciences , the Southern Rural Sociological Association , and multiple posts about the National Agricultural Communications Symposium including the following:
- Professional Development on Using Art to Communicate Science
- Poster Session and Professional Development on work by the Real Pork Trust Consortium
- Dissertation Research by Kristin Gibson
- Thesis Research by Olivia Erskine
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- Knowledge Base
- Starting the research process
A Beginner's Guide to Starting the Research Process
When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.
The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.
This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.
Table of contents
Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.
First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .
Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.
Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.
>>Read more about narrowing down a research topic
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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .
The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.
Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.
To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.
>>Read more about defining a research problem
Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.
A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.
In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .
>>See research question examples
The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.
There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?
You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.
>>Read more about creating a research design
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Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.
As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.
You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.
>>Read more about writing a research proposal
If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
- Sampling methods
- Simple random sampling
- Stratified sampling
- Cluster sampling
- Likert scales
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- Optimism bias
- Cognitive bias
- Implicit bias
- Hawthorne effect
- Anchoring bias
- Explicit bias
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EMCON alum Dr. Linda Koh '22 publishes dissertation research
Lessons learned from studying remotely accessible nutrition care..
Introduction: Little research has examined how community-engaged and -participatory dietary interventions adapted to remotely-accessible settings during the COVID-19 pandemic.
Objectives: To identify lessons learned in design, implementation, and evaluation of a remotely-accessible, community-based, nurse-led approach of a culturally-tailored whole food plant-based culinary intervention for Latina/o/x adults to reduce type 2 diabetes risk, delivered during a pandemic.
Methods: A mixed methods quasi-experimental design consisting of a pre-post evaluation comprised of questionnaires, culinary classes, biometrics, and focus groups.
Lessons learned: Community partnerships are essential for successful recruitment/retention. To optimally deliver a remotely-accessible intervention, community leadership and study volunteers should be included in every decision (e.g., timeframes, goals). Recommendations include managing recruitment and supply chain disruption of intervention supplies.
Conclusion: Future research should focus on increasing accessibility and engagement in minoritized and/or underserved communities, supply chain including quality assurance and delivery of services/goods, study design for sustainable, remotely-accessible interventions, and health promotion.
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Google’s new Gemini model can analyze an hour-long video — but few people can use it
Last October, a research paper published by a Google data scientist, the CTO of Databricks Matei Zaharia and UC Berkeley professor Pieter Abbeel posited a way to allow GenAI models — i.e. models along the lines of OpenAI’s GPT-4 and ChatGPT — to ingest far more data than was previously possible. In the study, the co-authors demonstrated that, by removing a major memory bottleneck for AI models, they could enable models to process millions of words as opposed to hundreds of thousands — the maximum of the most capable models at the time.
AI research moves fast, it seems.
Today, Google announced the release of Gemini 1.5 Pro, the newest member of its Gemini family of GenAI models. Designed to be a drop-in replacement for Gemini 1.0 Pro (which formerly went by “Gemini Pro 1.0” for reasons known only to Google’s labyrinthine marketing arm), Gemini 1.5 Pro is improved in a number of areas compared with its predecessor, perhaps most significantly in the amount of data that it can process.
Gemini 1.5 Pro can take in ~700,000 words, or ~30,000 lines of code — 35x the amount Gemini 1.0 Pro can handle. And — the model being multimodal — it’s not limited to text. Gemini 1.5 Pro can ingest up to 11 hours of audio or an hour of video in a variety of different languages.
Image Credits: Google
To be clear, that’s an upper bound.
The version of Gemini 1.5 Pro available to most developers and customers starting today (in a limited preview) can only process ~100,000 words at once. Google’s characterizing the large-data-input Gemini 1.5 Pro as “experimental,” allowing only developers approved as part of a private preview to pilot it via the company’s GenAI dev tool AI Studio . Several customers using Google’s Vertex AI platform also have access to the large-data-input Gemini 1.5 Pro — but not all.
Still, VP of research at Google DeepMind Oriol Vinyals heralded it as an achievement.
“When you interact with [GenAI] models, the information you’re inputting and outputting becomes the context, and the longer and more complex your questions and interactions are, the longer the context the model needs to be able to deal with gets,” Vinyals said during a press briefing. “We’ve unlocked long context in a pretty massive way.”
A model’s context, or context window, refers to input data (e.g. text) that the model considers before generating output (e.g. additional text). A simple question — “Who won the 2020 U.S. presidential election?” — can serve as context, as can a movie script, email or e-book.
Models with small context windows tend to “forget” the content of even very recent conversations, leading them to veer off topic — often in problematic ways. This isn’t necessarily so with models with large contexts. As an added upside, large-context models can better grasp the narrative flow of data they take in and generate more contextually rich responses — hypothetically, at least.
There have been other attempts at — and experiments on — models with atypically large context windows.
AI startup Magic claimed last summer to have developed a large language model (LLM) with a 5 million-token context window. Two papers in the past year detail model architectures ostensibly capable of scaling to a million tokens — and beyond. (“Tokens” are subdivided bits of raw data, like the syllables “fan,” “tas” and “tic” in the word “fantastic.”) And recently, a group of scientists hailing from Meta, MIT and Carnegie Mellon developed a technique that they say removes the constraint on model context window size altogether.
But Google is the first to make a model with a context window of this size commercially available, beating the previous leader Anthropic’s 200,000-token context window — if a private preview counts as commercially available.
Gemini 1.5 Pro’s maximum context window is 1 million tokens, and the version of the model more widely available has a 128,000-token context window, the same as OpenAI’s GPT-4 Turbo .
So what can one accomplish with a 1 million-token context window? Lots of things, Google promises — like analyzing a whole code library, “reasoning across” lengthy documents like contracts, holding long conversations with a chatbot and analyzing and comparing content in videos.
During the briefing, Google showed two prerecorded demos of Gemini 1.5 Pro with the 1 million-token context window enabled.
In the first, the demonstrator asked Gemini 1.5 Pro to search the transcript of the Apollo 11 moon landing telecast — which comes to around 402 pages — for quotes containing jokes, and then to find a scene in the telecast that looked similar to a pencil sketch. In the second, the demonstrator told the model to search for scenes in “Sherlock Jr.,” the Buster Keaton film, going by descriptions and another sketch.
Gemini 1.5 Pro successfully completed all the tasks asked of it, but not particularly quickly. Each took between ~20 seconds and a minute to process — far longer than, say, the average ChatGPT query.
Vinyals says that the latency will improve as the model’s optimized. Already, the company’s testing a version of Gemini 1.5 Pro with a 10 million-token context window.
“The latency aspect [is something] we’re … working to optimize — this is still in an experimental stage, in a research stage,” he said. “So these issues I would say are present like with any other model.”
Me, I’m not so sure latency that poor will be attractive to many folks — much less paying customers. Having to wait minutes at a time to search across a video doesn’t sound pleasant — or very scalable in the near term. And I’m concerned how the latency manifests in other applications, like chatbot conversations and analyzing codebases. Vinyals didn’t say — which doesn’t instill much confidence.
My more optimistic colleague Frederic Lardinois pointed out that the overall time savings might just make the thumb twiddling worth it. But I think it’ll depend very much on the use case. For picking out a show’s plot points? Perhaps not. But for finding the right screengrab from a movie scene you only hazily recall? Maybe.
Beyond the expanded context window, Gemini 1.5 Pro brings other, quality-of-life upgrades to the table.
Google’s claiming that — in terms of quality — Gemini 1.5 Pro is “comparable” to the current version of Gemini Ultra, Google’s flagship GenAI model, thanks to a new architecture comprised of smaller, specialized “expert” models. Gemini 1.5 Pro essentially breaks down tasks into multiple subtasks and then delegates them to the appropriate expert models, deciding which task to delegate based on its own predictions.
MoE isn’t novel — it’s been around in some form for years. But its efficiency and flexibility has made it an increasingly popular choice among model vendors (see: the model powering Microsoft’s language translation services).
Now, “comparable quality” is a bit of a nebulous descriptor. Quality where it concerns GenAI models, especially multimodal ones, is hard to quantify — doubly so when the models are gated behind private previews that exclude the press. For what it’s worth, Google claims that Gemini 1.5 Pro performs at a “broadly similar level” compared to Ultra on the benchmarks the company uses to develop LLMs while outperforming Gemini 1.0 Pro on 87% of those benchmarks. ( I’ll note that outperforming Gemini 1.0 Pro is a low bar .)
Pricing is a big question mark.
During the private preview, Gemini 1.5 Pro with the 1 million-token context window will be free to use, Google says. But the company plans to introduce pricing tiers in the near future that start at the standard 128,000 context window and scale up to 1 million tokens.
I have to imagine the larger context window won’t come cheap — and Google didn’t allay fears by opting not to reveal pricing during the briefing. If pricing’s in line with Anthropic’s , it could cost $8 per million prompt tokens and $24 per million generated tokens. But perhaps it’ll be lower; stranger things have happened! We’ll have to wait and see.
I wonder, too, about the implications for the rest of the models in the Gemini family, chiefly Gemini Ultra. Can we expect Ultra model upgrades roughly aligned with Pro upgrades? Or will there always be — as there is now — an awkward period where the available Pro models are superior performance-wise to the Ultra models, which Google’s still marketing as the top of the line in its Gemini portfolio?
Chalk it up to teething issues if you’re feeling charitable. If you’re not, call it like it is: darn confusing.