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Home > CICS > CS > CS_DISS

Computer Science Department Dissertations Collection

Dissertations from 2024 2024.

Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science

Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science

Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science

Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science

Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science

Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science

Dissertations from 2023 2023

An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science

Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science

Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science

IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science

Automating the Formal Verification of Software , Emily First, Computer Science

Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science

Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science

Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science

Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science

Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science

DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science

Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science

Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science

Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science

Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science

LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science

GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science

Dissertations from 2022 2022

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science

MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science

Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science

Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science

REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science

Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science

Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science

Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science

Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science

Probabilistic Commonsense Knowledge , Xiang Li, Computer Science

DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science

Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science

Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science

High-Quality Automatic Program Repair , Manish Motwani, Computer Science

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science

Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science

Decision Making with Limited Data , Kieu My Phan, Computer Science

Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science

Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science

Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science

LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science

Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science

Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science

Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science

Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science

Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science

ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science

Dissertations from 2021 2021

Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science

Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science

Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science

Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science

Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science

Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science

Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science

3D Shape Understanding and Generation , Matheus Gadelha, Computer Science

Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science

Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science

SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science

Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science

Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science

Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science

Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science

History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science

Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science

SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science

Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science

Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science

Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science

Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science

Geometric Representation Learning , Luke Vilnis, Computer Science

Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science

Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science

Audio-driven Character Animation , Yang Zhou, Computer Science

Dissertations from 2020 2020

Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science

Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science

Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science

Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science

Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science

Improving Face Clustering in Videos , SouYoung Jin, Computer Science

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science

Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science

Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science

Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science

Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science

Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science

Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science

The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science

System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science

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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 ).

For example:

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).

Acknowledgements

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!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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36 Comments

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.

Thanks Ade!

Thank you so much sir.. It was really helpful..

You’re welcome!

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?

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Engineering and Technology Management Dissertations and Theses

Theses/dissertations from 2023 2023.

Fintech and Entrepreneurship: An Assessment Model to Evaluate Policy Instruments for Fintech Adoption by Small and Medium Enterprises (SMEs) , Deemah Abdullah Alassaf

Organizational Readiness Assessment for Fraud Detection and Prevention: Case of Airlines Sector and Electronic Payment , Sultan Ayed Alghamdi

An Evaluation of the Effectiveness of Innovation Ecosystems in Facilitating the Adoption of Sustainable Entrepreneurship , Dana Sami Bakry

Assessment of the Omni-Channel Adoption for the Management of Emergence Technologies in Food Retailing During and After the Era of COVID-19 , Esraa Bukhari

A Hierarchical Decision Model to Evaluate Healthcare Organization's Readiness to Implement Clinical Decision Support Systems , Mohammed Oussama Laraichi

Theses/Dissertations from 2022 2022

Developing an Assessment Model for Entrepreneurship Ecosystems Using Hierarchical Decision Model: Case Study of Riyadh City , Mustafa Kamal Almuzel

Scenario Acceleration Through Automated Modelling: A Method and System for Creating Traceable Quantitative Future Scenarios Based on FCM System Modeling and Natural Language Processing , Christopher W.H. Davis

An Assessment Model of the Healthcare Entrepreneurship Ecosystem to Foster Solutions for Underserved Communities , Abdalilah Saleh Owaishiz

Maturity Model for Customer-Centric Approach in Enterprise: The Case of E-commerce and Online Retail Industry , Soheil Zarrin

Theses/Dissertations from 2021 2021

Assessment of the Blockchain Technology Adoption for the Management of the Electronic Health Record Systems , Saeed Mohammed Alzahrani

Information Security Maturity Model for Healthcare Organizations in the United States , Bridget Joan Barnes Page

Perceived Value of Technology Product Features by Crowdfunding Backers: The Case of 3D Printing Technology on Kickstarter Platform , Nina Chaichi

Technology Management Maturity Assessment Model in Healthcare , Amir Shaygan

Theses/Dissertations from 2020 2020

Exploring Policies and Strategies for the Diffusion of Remote Patient Monitoring (RPM) for the Care of Senior Population , Hamad Asri Alanazi

Achieving High Reliability Organizations Using Fuzzy Cognitive Maps - the Case of Offshore Oil and Gas , Ahmed A. Alibage

An Assessment of the Decision-Making Units' Efficiency in Service Systems , Maoloud Yakhlif Dabab

Evaluating R&D Projects in Regulated Utilities: the Case of Power Transmission Utilities , Edwin Garces

A Scoring Model to Evaluate Offshore Oil Projects: Case of Eni and Mellitah Oil & Gas , Abdulhakim Giadedi

Determinants of Green Purchase Intentions of Saudi Consumers , Amani Mohammed Kaadoor

A Market Diffusion Potential (MDP) Assessment Model for Residential Energy Efficient (EE) Technologies in the U.S. , Momtaj Khanam

Exploring the Factors Influencing Big Data Technology Acceptance , Mohammad Nayemur Rahman

Determinants of Student Information Technology Adoption , Hans P. VanDerSchaaf

Narrowing the Cognitive Distance Between Engineers and Customers: a Novel Approach, Based on Fuzzy Cognitive Mapping , Byung Sung Yoon

Theses/Dissertations from 2019 2019

Development of a Readiness Assessment Model for Evaluating Big Data Projects: Case Study of Smart City in Oregon, USA , Husam Ahmad Barham

Evaluating Project Assessment Techniques for High-Profile Transportation Projects Development and Delivery: Case of State Departments of Transportation (DOTs) in the United States , Rafaa Ibrahim Khalifa

A Scoring Model to Assess Organizations' Technology Transfer Capabilities: the Case of a Power Utility in the Northwest USA , João Ricardo Lavoie

Theses/Dissertations from 2018 2018

Achieving Organizational Ambidexterity: an Exploratory Model, Using Fuzzy Cognitive Maps , Yasser Alizadeh

Exploring Technology Forecasting and its Implications for Strategic Technology Planning , Yonghee Cho

Assessment of Technology Adoption Potential of Medical Devices: Case of Wearable Sensor Products for Pervasive Care in Neurosurgery and Orthopedics , Liliya Stepanivna Hogaboam

Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification , Farshad Madani

Developing a Mixed-Methods Method to Model Elderly Health Technology Adoption with Fuzzy Cognitive Map, and its Application in Adoption of Remote Health Monitoring Technologies by Elderly Women , Noshad Rahimi

Assessment of Policy Alternatives for Mitigation of Barriers to EV Adoption , Bilgehan Yildiz

Theses/Dissertations from 2017 2017

Technology Assessment Model of Developing Geothermal Energy Resources for Supporting Electrical System: the Case for Oregon , Ahmed Shehab Alshareef

Technology Planning for Aligning Emerging Business Models and Regulatory Structures: the Case of Electric Vehicle Charging and the Smart Grid , Kelly R. Cowan

Development of a Technology Transfer Score for Evaluating Research Proposals: Case Study of Demand Response Technologies in the Pacific Northwest , Judith Estep

Theses/Dissertations from 2016 2016

Consistency Analysis for Judgment Quantification in Hierarchical Decision Model , Mustafa Sulaiman Abbas

Exploratory Study of the Adoption and Use of the Smartphone Technology in Emerging Regions: Case of Saudi Arabia , Fahad Abdulaziz Aldhaban

A Measurement System for Science and Engineering Research Center Performance Evaluation , Elizabeth Carole Gibson

Developing a Hierarchical Decision Model to Evaluate Nuclear Power Plant Alternative Siting Technologies , Marwan Mossa Lingga

Theses/Dissertations from 2015 2015

Technological Forecasting Based on Segmented Rate of Change , Dong-Joon Lim

Network Structure, Network Flows and the Phenomenon of Influence in Online Social Networks: An Exploratory Empirical Study of Twitter Conversations about YouTube Product Categories , Nitin Venkat Mayande

Theses/Dissertations from 2014 2014

Evaluation of Energy Policy Instruments for the Adoption of Renewable Energy: Case of Wind Energy in the Pacific Northwest U.S. , Remal Abotah

An Assessment Model for Energy Efficiency Program Planning in Electric Utilities: Case of the Pacific of Northwest U.S.A. , Ibrahim Iskin

Participation in Technology Standards Development: A Decision Model for the Information and Communications Technology Industry , Ramin Neshati

Exploring Capability Maturity Models and Relevant Practices as Solutions Addressing IT Service Offshoring Project Issues , Rosine Hanna Salman

Multi-Perspective Technology Assessment to Improve Decision Making: A Novel Approach Using Fuzzy Cognitive Mapping for a Large-Scale Transmission Line Upgrade , Richard Chad Sperry

Green Index: Integration of Environmental Performance, Green Innovativeness and Financial Performance , Ilknur Mary Joy Nirmala Tekin

Theses/Dissertations from 2013 2013

An Investigation of Fast and Frugal Heuristics for New Product Project Selection , Fatima Mohammed Albar

Extending Technology Roadmap through Fuzzy Cognitive Map-based Scenarios: The Case of the Wind Energy Sector of Pakistan , Muhammad Amer

Developing a Strategic Policy Choice Framework for Technological Innovation: Case of Chinese Pharmaceuticals , Leong Chan

The Impact of Social Capital on Innovation Intermediaries , Songphon Munkongsujarit

Innovation Measurement: a Decision Framework to Determine Innovativeness of a Company , Kenny Phan

The Impact of Knowledge Inflows on the Performance of National Laboratories in Technological Latecomer Countries , Pattravadee Ploykitikoon

Assessment of Solar Photovoltaic Technologies Using Multiple Perspectives and Hierarchical Decision Modeling , Nasir Jamil Sheikh

Bridging the Missing Link between "Top-down" and "Bottom-up": A Strategic Policy Model for International Collaboration in Science and Technology , Pattharaporn Suntharasaj

Strategic Evaluation of University Knowledge and Technology Transfer Effectiveness , Thien Anh Tran

Theses/Dissertations from 2012 2012

Examining Health Information Technology Implementations: Case of the Patient-Centered Medical Home , Nima A. Behkami

Theses/Dissertations from 2011 2011

Study of the Performance and Characteristics of U.S. Academic Research Institution Technology Commercialization (ARITC) , Jisun Kim

Theses/Dissertations from 2009 2009

A Systematic Approach to Developing National Technology Policy and Strategy for Emerging technologies , Pisek Gerdsri

Theses/Dissertations from 2004 2004

Technology Forecasting Using Data Envelopment Analysis , Oliver Lane Inman

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Technology Thesis Statement

The dynamic world of technology continually shapes our daily lives and future. Writing a compelling thesis statement about technology means delving deep into the nuances of innovation, foreseeing its implications, and presenting a clear, concise perspective. Crafting the perfect statement requires a keen understanding of your topic, its relevance, and the message you wish to convey. Below, we will explore examples of technology-related thesis statements, provide tips on how to hone them, and guide you in encapsulating the essence of your research.

What is the Technology Thesis Statement? – Definition

A technology thesis statement is a concise summary or main point of a research paper, essay, or dissertation related to a technology-focused topic. It establishes the central theme, position, or argument that the author intends to communicate, providing readers with a clear overview of what the subsequent content will address. This research paper thesis statement is essential in guiding the flow and coherence of the piece, ensuring that the content remains relevant to the proposed topic.

What is an example of a Technology thesis statement?

“With the rapid evolution of wearable technology, there is a compelling need to address the associated privacy concerns, arguing that without comprehensive regulations, users’ personal data could be at significant risk.”  You should also take a look at our  middle school thesis statement .

100 Technology Statement Examples

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Technology concise thesis statements encapsulate the essence of tech-focused research papers or essays, presenting a concise argument or perspective on a specific technological development, trend, or challenge. These statements guide the reader’s understanding, giving clarity and direction to the narrative.

• Artificial Intelligence : “The integration of AI in healthcare can revolutionize patient diagnosis, but ethical constraints need addressing.”
• Virtual Reality : “Virtual reality’s potential in education extends beyond immersion, offering tailored learning experiences.”
• Blockchain : “Blockchain technology, while disruptive, promises to make financial transactions more transparent and secure.”
• Cybersecurity : “The rise of IoT devices demands stronger cybersecurity measures to prevent unprecedented breaches.”
• Biotechnology : “CRISPR technology might hold the key to genetic disorders, yet its ethical implications are vast.”
• E-Commerce : “The shift to e-commerce has fundamentally changed consumer behavior, prioritizing convenience over brand loyalty.”
• 5G Technology : “The deployment of 5G will enhance IoT capabilities, but infrastructure challenges persist.”
• Green Technology : “Solar panel advancements are crucial for sustainable energy but require policy support for widespread adoption.”
• Robotics : “Robotic automation in manufacturing accelerates production but poses employment challenges.”
• Wearable Tech : “Wearables are transforming health monitoring, but data privacy remains a significant concern.”
• Quantum Computing : “While quantum computers promise to solve complex problems in seconds, they also pose threats to current encryption methods.”
• Space Exploration : “The commercialization of space travel opens new frontiers for tourism but also raises environmental and safety concerns.”
• Augmented Reality : “Augmented reality in retail can enhance customer experience, yet it challenges traditional shopping norms.”
• Drones : “The proliferation of drone technology in delivery services improves efficiency but brings forth airspace regulation issues.”
• Nano-Technology : “Nanotechnology in medicine offers targeted drug delivery but has unexplored long-term effects on human health.”
• Self-Driving Cars : “Autonomous vehicles could drastically reduce traffic accidents, but their integration requires comprehensive legal frameworks.”
• Smart Cities : “Smart cities optimize urban living conditions; however, they highlight disparities in digital access.”
• Edge Computing : “Edge computing decentralizes data processing, enhancing IoT performance, but it raises concerns about localized data breaches.”
• 3D Printing : “3D printing revolutionizes manufacturing and healthcare but challenges intellectual property rights.”
• Digital Assistants : “Voice-activated digital assistants streamline daily tasks but provoke debates on user surveillance and privacy.”
• Telemedicine : “Telemedicine democratizes healthcare access, yet questions arise about its efficacy compared to in-person consultations.”
• Big Data : “Big data analytics can transform industries, but the potential misuse of information is a growing concern.”
• Cloud Computing : “Cloud adoption offers businesses scalability and flexibility, though it introduces unique cybersecurity challenges.”
• Digital Currency : “Cryptocurrencies like Bitcoin could redefine financial systems, but their volatility and regulatory gray areas persist.”
• Gaming Technology : “Esports and gaming technology foster global communities, but they also spotlight issues of digital addiction.”
• Neural Networks : “Neural networks enhance machine learning capabilities but make algorithm decision-making processes more opaque.”
• Mixed Reality : “Mixed reality blends the best of AR and VR, offering innovative solutions in training but requires significant hardware investments.”
• Social Media Algorithms : “Algorithms on social platforms shape user behavior, leading to questions about influence and manipulation.”
• Broadband Technology : “Universal broadband access can bridge educational gaps, but infrastructural and cost barriers remain.”
• Digital Learning Platforms : “Online education platforms democratize learning but challenge traditional educational paradigms.”
• Agricultural Tech : “Smart farming through tech can optimize yields, but its cost can exclude small-scale farmers.”
• Mobile Banking : “Mobile banking boosts financial inclusion in developing nations but raises issues of digital literacy.”
• Chatbots : “Chatbots in customer service optimize responsiveness but can depersonalize the user experience.”
• Facial Recognition : “Facial recognition tech can enhance security measures but has sparked debates on privacy and misuse.”
• Deepfakes : “Deepfake technology, while impressive, poses significant threats to misinformation and trust in media.”
• Health Tech : “Wearable health devices offer real-time monitoring, yet there’s growing concern over data security and interpretation accuracy.”
• Marine Technology : “Underwater drones present opportunities for oceanic exploration, but their use raises environmental concerns.”
• Sustainable Tech : “Technological solutions to waste management are crucial for urban sustainability, but require societal behavior changes for maximum effectiveness.”
• Language Translation : “Real-time translation tools are bridging communication gaps, but can’t replace the nuance of human translators.”
• Online Privacy : “VPN services enhance online privacy, yet they introduce challenges in legal jurisdictions and data accountability.”
• Internet of Things (IoT) : “While IoT connects everyday devices, it also increases potential points of cyber vulnerabilities.”
• Haptic Technology : “Haptic tech holds potential in virtual training environments but demands rigorous testing for consistent real-world replication.”
• Renewable Energy Tech : “Wind energy is a clean alternative, yet its land use and noise pollution issues remain unresolved.”
• Genomic Editing : “While genomic editing can prevent hereditary diseases, its potential misuse in ‘designer babies’ raises ethical debates.”
• E-Learning : “Digital classrooms can provide education continuity during crises, but highlight inequalities in tech accessibility.”
• Wireless Charging : “The evolution of wireless charging technology promotes convenience but necessitates universal standardization.”
• Retail Tech : “Smart mirrors in retail enhance consumer experience but can potentially infringe on privacy rights if misused.”
• Data Storage : “Quantum data storage could revolutionize information keeping, yet the transition from classical methods is fraught with challenges.”
• Livestreaming Tech : “The growth of livestreaming platforms boosts creator economies, but presents issues of content moderation.”
• Digital Twins : “Digital twins in manufacturing optimize production processes, but require significant data management and interpretation efforts.”
• Animal Tech : “RFID tags in wildlife conservation assist in species monitoring but raise concerns about animal welfare and interference.”
• Thermal Imaging : “Thermal imaging in public spaces can enhance security, but its widespread use prompts privacy debates.”
• Financial Tech (FinTech) : “Digital-only banks provide unparalleled convenience, yet face skepticism over their ability to handle financial crises.”
• Audio Tech : “Spatial a in headphones creates immersive experiences, but its effects on auditory health are under-researched.”
• Nano-Biotechnology : “Nano-biotech in targeted drug delivery holds promise, but its long-term interactions with biological systems remain unknown.”
• Location-Based Services : “Geolocation tools in apps enhance user experience, but inadvertently contribute to data surveillance concerns.”
• Human-Machine Interface : “Brain-computer interfaces might redefine communication for the differently-abled, but they also present neuroethical dilemmas.”
• Gig Economy Platforms : “Tech-driven gig economies offer flexible employment, but often at the cost of job security and benefits.”
• Environmental Monitoring : “Satellite technology for environmental monitoring is crucial for climate change mitigation, but depends on international collaboration and data-sharing.”
• Entertainment Tech : “Augmented reality in entertainment redefines audience engagement, but challenges traditional content creation paradigms.”
• Food Technology : “Lab-grown meats could significantly reduce the environmental impact of livestock, but their societal acceptance and taste equivalency remain under scrutiny.”
• Telecommunication : “The transition to satellite-based internet services can enhance global connectivity but introduces space debris management challenges.”
• Digital Art and Media : “Digital art platforms democratize artistic expression, though they raise concerns over copyright and originality.”
• Fitness Tech : “Smart gyms utilize AI to personalize workout regimens, but their reliance on user data raises privacy issues.”
• Medical Imaging : “AI-driven medical imaging can enhance diagnostic precision, yet its integration demands rigorous validation against traditional methods.”
• Urban Mobility : “Electric scooters in urban centers promote green mobility, but their indiscriminate use poses pedestrian safety risks.”
• Adaptive Tech : “Adaptive technologies for the differently-abled democratize access, but their high costs can limit widespread adoption.”
• Cryptographic Tech : “Post-quantum cryptography aims to secure data against future quantum attacks, but its practical implementation remains challenging.”
• Travel and Navigation : “AR-based navigation tools can revolutionize travel experiences, but they demand robust infrastructure to prevent inaccuracies.”
• Event Technology : “Virtual event platforms offer global outreach, but they challenge the conventional understanding of networking and engagement.”
• Consumer Electronics : “Flexible electronics pave the way for innovative gadgets, yet their durability and recyclability are concerns.”
• Space Mining : “Space mining could answer Earth’s resource scarcity, but its feasibility and impact on space ecosystems are contentious.”
• Fashion Tech : “Smart fabrics offer dynamic design possibilities, but their production processes raise environmental questions.”
• Elderly Tech : “Tech solutions for the elderly improve quality of life, but require intuitive designs to ensure ease of use.”
• Cyber Physical Systems : “Integrating physical processes with computer-based algorithms promises efficiency, but challenges real-time adaptability.”
• Rehabilitation Tech : “VR in physical rehabilitation offers immersive therapy, but its long-term efficacy compared to traditional methods is under exploration.”
• Collaborative Platforms : “Cloud-based collaborative tools redefine workplace productivity, but their over-reliance can risk centralizing data control.”
• Quantum Sensing : “Quantum sensors could redefine detection limits in various fields, but their scalability in real-world applications remains a hurdle.”
• Learning Management Systems (LMS) : “LMS platforms facilitate organized e-learning, but their design must prioritize user-friendliness for diverse user groups.”
• Aerospace Tech : “Electric aircraft represent the future of eco-friendly travel, but the transition requires breakthroughs in battery technology.”
• Hydroponic Farming : “Tech-driven hydroponic systems can increase agricultural yield in urban areas, but the initial setup costs and energy consumption are deterrents.”
• Waste Management Tech : “Automated waste sorting can significantly enhance recycling rates, but its success demands public awareness and participation.”
• Digital Publishing : “E-books and digital publications increase accessibility, but they also challenge traditional publishing economics.”
• Therapeutic Tech : “Biofeedback apps promise personalized stress management, but their recommendations need backing by robust clinical research.”
• Molecular Electronics : “Molecular-scale electronics could miniaturize devices further, but their stability and manufacturing pose significant challenges.”
• Industrial IoT : “Integrating IoT in industries optimizes production and maintenance, but its seamless functioning demands strong cybersecurity protocols.”
• Photonics : “Photonics in data transmission offers higher speeds, but its integration into current infrastructure is complex.”
• Marine Energy : “Harnessing oceanic energy can be a renewable power solution, but its impact on marine ecosystems needs careful evaluation.”
• Prosthetics Tech : “Advanced prosthetics with AI integration promise life-changing mobility, but the cost of development and acquisition challenges their accessibility.”
• Resilient Infrastructure : “Smart materials in construction adapt to environmental changes, but the long-term sustainability and economic feasibility remain subjects of research.”
• Optogenetics : “Optogenetics holds transformative potential for neurological disorders, but its ethical application in humans is still debated.”
• Entertainment Streaming : “Streaming platforms are reshaping entertainment consumption, but they also spotlight issues of digital rights and royalties.”
• Water Purification Tech : “Nanotechnology in water purification can address global water crises, but its ecological impact requires close monitoring.”
• Transportation Tech : “Hyperloop transportation promises rapid transits, but the infrastructural and safety challenges are monumental.”
• Pedagogical Tools : “AI-driven pedagogical tools individualize learning, but there’s a risk of over-reliance and diminished human interaction.”
• Remote Work Tech : “Advanced collaborative tools enable effective remote work, but they also blur the lines between professional and personal boundaries.”
• Sensor Technology : “Smart sensors in agriculture optimize irrigation and reduce water wastage, but their implementation costs can be prohibitive for small-scale farmers.”
• Food Preservation : “Innovative food preservation technologies can reduce global food wastage, but their energy consumption and efficiency need optimization.”
• Gaming Interfaces : “Brain-computer interfaces in gaming promise immersive experiences, but their long-term effects on neurological health are underexplored.”
• Material Science : “Meta-materials can revolutionize optics and telecommunications, but their large-scale production and integration pose significant challenges.”

Technology Thesis Statement Examples for Argumentative Essay

As the digital age progresses, there’s a growing consensus about the pros and cons of technology’s integration into our daily lives. Argumentative essays thesis statement on technology often delve into the ethical and societal implications, pushing the boundaries of the debates even further.

• Social Media’s Impact : “While some argue that social media strengthens interpersonal relationships, it can also be held responsible for eroding face-to-face interactions and deepening feelings of social isolation.”
• Digital Dependency : “The increasing reliance on smartphones has jeopardized our cognitive abilities, leading to diminished memory recall and reduced attention spans.”
• Online Privacy : “In the digital age, online privacy has become an illusion, with corporations and governments frequently infringing upon personal data rights.”
• Virtual Reality : “Despite the immersive experiences offered by virtual reality, its overuse can blur the distinction between the real and virtual worlds, leading to psychological implications.”
• Technological Progress vs. Job Security : “Technological advancements, while driving efficiency and progress, also threaten traditional jobs, potentially leading to economic disparities.”
• Digital Currency : “Cryptocurrencies, despite their volatile nature, represent a significant shift in the financial landscape and have the potential to decentralize traditional banking systems.”
• E-books vs. Traditional Books : “While e-books offer convenience and accessibility, they can never replace the tactile experience and emotional connection readers have with physical books.”
• The Internet and Democracy : “The internet, although hailed as a tool for democratizing information, also presents threats like misinformation campaigns that can undermine democratic processes.”
• Tech Giants and Monopoly : “The unchecked rise of tech giants poses a threat to competition, potentially stifling innovation and enabling monopolistic behaviors.”
• Green Technology : “Investing in green technologies is not merely an environmental imperative but also an economic opportunity that promises both sustainable growth and job creation.”

Thesis Statement Examples for Technology in Education

Education has undergone tremendous transformation thanks to technology. The intersection of technology and education raises questions about equity, effectiveness, and the shaping of future minds.

• Digital Literacy : “Incorporating digital literacy in education is crucial, not just for technological proficiency but for navigating the modern world responsibly and critically.”
• Online Learning : “Online education, while offering flexibility and accessibility, can lack the personal touch and hands-on experiences that traditional classrooms provide.”
• EdTech in Early Childhood : “Introducing technology in early childhood education can foster creativity and adaptability, but it must not overshadow foundational learning experiences.”
• Gamification of Learning : “Gamifying education can increase student engagement, but there’s a risk of prioritizing rewards over actual knowledge acquisition.”
• Tech in Special Education : “Technology has the potential to revolutionize special education, offering tailored learning experiences to cater to individual needs.”
• Digital Distractions : “The integration of technology in classrooms, while beneficial, also brings the challenge of combating digital distractions and ensuring focused learning.”
• Open Source Learning : “Open-source educational resources can democratize education, but there’s a need to ensure the quality and credibility of these materials.”
• AR and VR in Education : “Augmented and virtual reality tools in education can offer immersive learning experiences, but their efficacy compared to traditional methods remains to be thoroughly evaluated.”
• Adaptive Learning Systems : “Adaptive learning technologies promise personalized education, but reliance on them must be balanced with human mentorship.”
• Digital Divide : “The push for technology in education must also address the digital divide, ensuring that students from all socioeconomic backgrounds have equal access.”

Thesis Statement Examples on Technology in Artificial Intelligence

The realm of artificial intelligence is a marvel of modern science and engineering, but it brings forth numerous concerns and speculations. Essays on AI and technology focus on the potential of machines surpassing human intelligence and the societal repercussions of such a possibility.

• Ethical AI : “As AI systems grow in complexity, there’s an urgent necessity to establish ethical guidelines that prioritize human values and safety.”
• AI in Warfare : “The integration of AI in military operations, while enhancing precision, raises alarming concerns about the lack of human judgment in life-and-death decisions.”
• Bias in Machine Learning : “Unchecked, machine learning models can perpetuate and amplify societal biases, necessitating rigorous audit processes before deployment.”
• AI and Employment : “The rise of automation and AI in industries risks a significant displacement of the workforce, highlighting the need for societal adaptation and job retraining.”
• Emotion AI : “Artificial Intelligence designed to recognize and respond to human emotions could revolutionize industries, but also brings concerns about privacy and emotional manipulation.”
• Singularity : “The potential for an AI singularity, where AI surpasses human intelligence, necessitates preemptive safeguards to ensure the alignment of AI goals with humanity’s best interests.”
• AI in Healthcare : “While AI in healthcare can lead to more accurate diagnoses, it must complement, not replace, the critical thinking and empathy of medical professionals.”
• Deepfakes and Reality : “The advent of deepfake technology, driven by AI, challenges our trust in visual content, pressing for the development of verification tools.”
• AI and Creativity : “The surge of AI in creative fields, from art to music, questions the uniqueness of human creativity and the future role of AI as co-creators.”
• General AI vs. Narrow AI : “While narrow AI excels in specific tasks, the pursuit of general AI, mirroring human intelligence, presents unprecedented challenges and ethical dilemmas.”

Thesis Statement Examples on Medical Technology

The medical field has seen rapid technological advancements, leading to breakthroughs in treatment and patient care. Discussing medical technology often centers around its impact on the patient-doctor relationship and health outcomes.

• Telemedicine : “Telemedicine, while increasing healthcare accessibility, requires rigorous regulation to ensure the quality of care and the privacy of patient data.”
• Gene Editing : “CRISPR and other gene-editing technologies hold promise for eradicating genetic diseases, but they also raise ethical concerns about the potential misuse in creating ‘designer babies’.”
• Wearable Health Tech : “Wearable health devices empower individuals to monitor their health, but also bring concerns about data privacy and the accuracy of health information.”
• 3D Printed Organs : “3D printing of organs could revolutionize transplants, but the technology must first overcome challenges in biocompatibility and functionality.”
• Robot-Assisted Surgery : “Robot-assisted surgeries promise precision and minimized invasiveness, yet the high costs and training requirements present hurdles for widespread adoption.”
• Mental Health Apps : “Digital tools for mental health can democratize access to resources, but they cannot replace the nuanced care provided by human professionals.”
• Nanotechnology in Medicine : “The integration of nanotechnology in medicine offers targeted treatments and drug delivery, but long-term effects on the human body remain largely unknown.”
• Virtual Reality in Therapy : “VR therapies hold potential for treating phobias and PTSD, but research must ensure that virtual experiences translate to real-world recovery.”
• EHR (Electronic Health Records) : “While EHRs streamline medical data management, concerns arise about patient data security and system interoperabilities.”
• AI-driven Diagnosis : “AI-driven diagnostic tools can analyze vast data quickly, but they should act as aides to human clinicians, not replacements.”

Thesis Statement Examples for Technology Essay

General technology essays touch on the overarching theme of how technology shapes society, cultures, and personal interactions. These essays dive deep into both the boons and banes of technological innovation.

• Digital Age and Mental Health : “The digital age, while connecting the world, has also escalated mental health issues, prompting a deeper examination of our relationship with technology.”
• Augmented Humanity : “Biohacking and body augmentations, powered by tech, are pushing the boundaries of human capabilities but also raise ethical questions about self-modification and societal implications.”
• Cybersecurity : “In a hyper-connected world, cybersecurity is not just a technical challenge but a fundamental aspect of ensuring personal rights and national security.”
• Sustainable Technologies : “The rise of sustainable technologies is not a mere trend but a necessity to ensure the future survival and prosperity of our planet.”
• Digital Nomadism : “The evolution of remote work technologies has birthed the digital nomad culture, reshaping traditional perceptions of work-life balance and productivity.”
• Space Technologies : “Emerging space technologies, from satellite constellations to interplanetary exploration, hold the promise of reshaping our understanding of the universe and our place in it.”
• Tech and Pop Culture : “The infusion of technology into pop culture, from movies to music, reflects society’s struggles, aspirations, and dreams in the digital age.”
• Digital Archiving : “The practice of digital archiving is crucial not just for preserving history but for ensuring accountability in the digital era.”
• The Right to Disconnect : “As work and personal life boundaries blur due to technology, there’s a rising demand for the ‘right to disconnect’, ensuring mental well-being.”
• Tech in Urban Planning : “Smart cities, driven by technology, promise enhanced living experiences, but they also raise concerns about surveillance and the loss of privacy.”

Thesis Statement Examples for Technology in the Classroom

Classroom technology has redefined traditional teaching methodologies, leading to a new age of learning. Essays in this category often grapple with the balance between technology and traditional pedagogies.

• Digital Collaboration : “Collaborative tools in classrooms foster teamwork and communication but necessitate guidelines to ensure productive and respectful engagements.”
• Interactive Learning : “Interactive whiteboards and digital simulations can enhance understanding and retention, but educators must ensure they don’t become mere entertainment.”
• Classroom Analytics : “The use of analytics in classrooms promises personalized feedback and interventions, but raises concerns about student privacy and data misuse.”
• Digital Textbooks : “While digital textbooks offer dynamic content and portability, the potential loss of traditional reading skills and tactile learning must be addressed.”
• Flipped Classrooms : “Flipped classrooms, facilitated by technology, encourage student-centered learning at home, but require a redefinition of classroom roles and responsibilities.”
• Tech and Special Needs : “Assistive technologies in classrooms have democratized education for students with special needs, but teachers need training to utilize them effectively.”
• Student Engagement : “Gamified learning platforms can significantly increase student engagement, but there’s a risk of overemphasis on rewards over actual learning outcomes.”
• Distance Learning : “Technology has made distance learning feasible and expansive, yet the challenges of student isolation and self-regulation need addressing.”
• Digital Citizenship : “Teaching digital citizenship in classrooms is essential in the modern age to ensure students use technology responsibly and ethically.”
• Classroom VR : “Introducing virtual reality in classrooms can offer immersive educational experiences, but its efficacy and potential overstimulation issues need thorough research.”

What is a good thesis statement for technology?

A good thesis statement for technology succinctly captures your main argument or perspective on a specific technological issue. Such a statement should exhibit:

• Precision : Clearly articulate your viewpoint on the technological matter, ensuring it isn’t vague.
• Debate Potential : Present a point open to discussion or counterargument, not just a plain fact.
• Current Relevance : Address up-to-date technological advancements or concerns.
• Conciseness : Stay direct and avoid broad overviews.

Example: “Artificial intelligence in healthcare, while promising enhanced patient care, raises pressing ethical concerns.”

How do you write a Technology Thesis Statement? – Step by Step Guide

• Pinpoint a Specific Tech Area : Instead of a broad area like “technology,” zoom into niches: e.g., “Blockchain’s role in data security” or “Virtual Reality in education.”
• Undertake Preliminary Research : Grasp the current scenario of your selected area. Identify ongoing debates, breakthroughs, and challenges.
• State Your Assertion : Your research will guide you to a specific stance. This becomes your thesis’s foundation.
• Check for Debate Potential : Ensure that your assertion isn’t just stating the obvious but invites discussion.
• Maintain Brevity : Keep it succinct—usually, one to two sentences will suffice.
• Iterate : As your research or essay progresses, you might find the need to fine-tune your statement.

Tips for Writing a Thesis Statement on Technology Topics

• Stay Informed : With technology’s rapid pace, being up-to-date is essential. Your thesis should resonate with current technological dialogues.
• Steer Clear of Jargons : If your audience isn’t tech-centric, simplify or explain tech terms for clarity.
• Dive into Ethical Angles : Tech topics often interweave with ethical considerations. Tackling these adds depth.
• Solicit Feedback : Sharing your thesis with colleagues or mentors can offer new viewpoints or refinements.
• Employ Assertive Language : Words like “should,” “must,” or “will” give your statement authority.
• Remain Adaptable : If new evidence emerges as you write, be open to reworking your thesis slightly.
• Link to Broader Implications : Relating your tech topic to wider societal or global issues can offer added layers of significance.
• Ensure Clarity : Your thesis should have one clear interpretation to avoid reader confusion.

By honing these techniques and tips, you’ll be adept at formulating impactful thesis statements tailored to technology-centric topics. As technology continues to shape our world, the ability to critically and concisely discuss its implications is invaluable.  You may also be interested in our Analytical Essay thesis statement .

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What Is a Dissertation Methodology?

How to choose your methodology, final thoughts, how to write your dissertation methodology.

Updated September 30, 2021

Due to the complexities of the different research methods, writing your dissertation methodology can often be the most challenging and time-consuming part of your postgraduate dissertation .

This article focuses on the importance of writing a good PhD or master's dissertation methodology – and how to achieve this.

A postgraduate dissertation (or thesis) is usually formed of several detailed sections, including:

Abstract – A summary of your research topic.

Introduction – Provides background information on your topic, putting it into context. You will also confirm the main focus of your study, explain why it will add value to your area of interest and specify your key objectives.

Literature Review – A critical review of literature that relates to your chosen research topic. You will also need to identify which gap in the literature your study aims to address.

Methodology – Focuses on the research methods used within your research.

Results – Used to report on your main findings and how these relate to your research question.

Conclusion – Used to confirm the answer to your main research question, reflect on the research process and offer recommendations on future research.

The dissertation methodology forms the skeleton of any research project. It provides the reader with a clear outline of the methods you decided to use when carrying out your research.

By studying your dissertation methodology, the reader will be able to assess your research in terms of its validity and reliability.

In line with the outline given above, the methodology chapter usually appears after the literature review . Your methodology should be closely linked to the research that you conducted as part of this review, as well as the questions you aim to answer through your research and analysis.

Taking the time to find out about the different types of research available to you will allow you to identify any potential drawbacks to the method you have chosen to use. You should then be able to make allowances or adjustments to address these when it comes to carrying out your research.

Choosing your methodology will largely depend on the discipline of the qualification you are studying for and the question your dissertation will seek to answer. In most cases, you will use quantitative or qualitative research methods, although some projects will benefit from using a combination of both.

Quantitative research methods are used to gather numerical information. This research method is particularly useful if you are seeking to count, categorise, measure or identify patterns in data. To collect quantitative data, you might choose to conduct experiments, tests or surveys.

Qualitative research methods are used to gather non-statistical data. Instead of using numbers to create charts or graphs, you will need to categorise the information according to identifiers. This research method is most useful if you are seeking to develop a hypothesis. To collect qualitative data, you might choose to conduct focus groups, interviews or observations.

What to Include in Your Dissertation Methodology

Below is a dissertation methodology example to show you what information to include:

You will need to reiterate your research topic or question and give an overview of how you plan to investigate this. If there were any ethical or philosophical considerations to be made, give details.

For example, you may have sought informed consent from the people taking part in interviews or surveys.

Outline of the Methods Chosen

Confirm whether you have chosen to use quantitative research, qualitative research or a combination of both.

When choosing between qualitative and quantitative research methods, you will need to carry out initial literature and textbook research to establish the standard research methods that are normally used within your chosen area of research.

If you are not sure where to start, you could visit the library at your college or university and ask one of the librarians to help you to identify the most relevant texts.

Explanation of the Methods Chosen

Explain your rationale for selecting your chosen research methods. You should also give an overview of why these were more appropriate than using another research method.

Think about where and when the research took place and who was involved. For example, this might include information on the venue used for interviews or focus groups, dates and timescales, and whether participants were part of a particular demographic group.

Here are some examples of the type of information you may wish to include:

Qualitative Research Methods

Personal observations – Where and when did you conduct the observations? Who did you observe? Were they part of a particular community or group? How long did each observation take? How did you record your findings – did you collect audio recordings, video footage or written observations?

Focus groups – Where and when did the focus group take place? Who was involved? How were they selected? How many people took part? Were the questions asked structured, unstructured or semi-structured? Remember to include a copy of the questions that were used as an appendix.

Interviews – Where and when did the interviews take place? Who took part? How did you select the participants? What type of questions did you ask? How did you record your findings? Remember to include a copy of the questions that were used as an appendix.

The researcher’s objective was to find out customer perceptions on improving the product range currently offered by Company Y. Semi-structured interviews were held with 15 returning customers from the key target demographic for Company Y (18- to 35-year-olds). For research purposes, a returning customer was defined as somebody who purchased products from Company Y at least two times per week during the past three months. The interviews were held in an office in the staff area of the retail premises. Each interview lasted approximately 25 minutes. Responses were recorded through note-taking as none of the respondents wished to give their consent to be filmed.

Quantitative Research Methods

Existing information or data – What were the sources of the material used? How did you select material? Did you only use data published within a particular time frame?

Experiments – What tools or equipment did you use? What techniques were required? Note that when conducting experiments, it is particularly important to provide enough information to allow another researcher to conduct the experiment and obtain the same results.

Surveys – Were respondents asked to answer multiple-choice questions or complete free-text fields? How many questions were used? How long were people given to answer all of the questions? What were the demographics of the participants? Remember to include a copy of the survey in the appendices.

The survey was made up of 10 multiple-choice questions and 5 questions to be rated using a 5-point Lickert scale. The objective was to have 250 customers of Company Z complete the survey at the Company Z HQ between 1st and 5th February 2019, between the hours of 12 p.m. and 5 p.m. For research purposes, a customer was defined as any person who had purchased a product from Company Z during 2018. Customers completing the survey were allowed a maximum of 10 minutes to answer all of the questions. 200 customers responded, however not all of the surveys were completed in full, so only 150 survey results were able to be used in the data analysis.

How Was the Data Analysed?

If you have chosen to use quantitative research methods, you will need to prepare the data before analysing it – for example, you will need to check for variables, missing data and outliers. If you have used computer software to aid with analysis, information on this should also be included.

For qualitative data, you will need to categorise and code the ideas and themes that are identified from the raw data. You may also need to use techniques such as narrative analysis or discourse analysis to interpret the meaning behind responses given.

What Materials and Equipment Were Used During the Research?

This could include anything from laboratory equipment used in a scientific experiment to computer software used to analyse the results.

Were There Any Hurdles or Difficulties Faced During the Research?

If so, what were they and how did you manage to overcome them? This could be anything from difficulties in finding participants, problems obtaining consent or a shortage of the required resources needed to conduct a scientific experiment.

This paragraph should be used to evaluate the research you have conducted and justify your reasons for choosing this approach.

You do not need to go into great detail, as you will present and discuss your results in-depth within your dissertation’s ‘Results’ section.

You will need to briefly explain whether your results were conclusive, whether there were any variables and whether your choice of methodology was effective in practice.

Tips for Writing Your Dissertation Methodology

The objective for the methodology is not only to describe the methods that you used for your research. You will also need to demonstrate why you chose to use them and how you applied them.

The key point is to show that your research was conducted meticulously.

Try to keep your writing style concise and clear; this will ensure that it is easy for the reader to understand and digest.

Here are five top tips to consider when writing your dissertation methodology:

1. Look at Other Methodology Sections

Ask your supervisor to provide you with a few different examples of previously written dissertations. Reading through methodologies that have been written by past students will give you a good idea of what your finished methodology section should look like.

2. Plan Your Structure

Whichever research methods you have chosen to use, your dissertation methodology should be a clearly structured, well written section that gives a strong and justified argument for your chosen research methods.

You may wish to use headings such as:

• Research methods
• Explanation of research methods chosen
• Data analysis and references

Once you have drafted an outline, ask your supervisor for advice on whether there is anything you have missed and whether your structure looks logical.

3. Consider Your Audience

When writing your methodology, have regard for the people who are likely to be reading it. For example, if you have chosen to use research methods that are commonly chosen within your area of research or discipline, there is no need to give a great deal of justification or background information.

If you decide to use a less popular approach, it is advisable to give much more detailed information on how and why you chose to use this method.

4. Remain Focused on Your Aims and Research Questions

Your dissertation methodology should give a clear indication as to why the research methods you have chosen are suitable for the aims of your research.

When writing your dissertation methodology, ensure that you link your research choices back to the overall aims and objectives of your dissertation. To help you to remain focused, it can be helpful to include a clear definition of the question you are aiming to answer at the start of your methodology section.

5. Refer to Any Obstacles or Difficulties That You Dealt With

If you faced any problems during the data collection or analysis phases, use the methodology section to talk about what you did to address these issues and minimise the impact.

Whether you are completing a PhD or master's degree, writing your thesis or dissertation methodology is often considered to be the most difficult and time-consuming part of completing your major research project.

The key to success when writing a methodology section is to have a clear structure. Remember, the purpose of the methodology section of your research project is to ensure that the reader has a full understanding of the methods you have chosen.

You should use your methodology section to provide clear justification as to why you have chosen a particular research method instead of other potential methods. Avoid referring to your personal opinions, thoughts or interests within your methodology; keep the information that you include factual and ensure that everything is backed up by appropriate academic references.

You might also be interested in these other Wikijob articles:

Or explore the Postgraduate / PHD sections.

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Digital Commons @ USF > College of Engineering > Electrical Engineering > Theses and Dissertations

Electrical Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

On the Performance Enhancement of Beamspace MIMO and Non-orthogonal Multiple Access for Future Cellular Networks , Sinasi Cetinkaya

Enhancing Smart Grid Security and Reliability through Graph Signal Processing and Energy Data Analytics , Md Abul Hasnat

Fabric-Based Organic Electrochemical Transistor Towards Wearable pH Sensing Electronics , Nestor Osvaldo Marquez Rios

Novel Systems Engineering Framework Analysis of Photovoltaic Models and Equations , Peter R. Michael

Deep Learning Enhancement and Privacy-Preserving Deep Learning: A Data-Centric Approach , Hung S. Nguyen

Cyber-Physical Multi-Robot Systems in a Smart Factory: A Networked AI Agents Approach , Zixiang Nie

Multiple Access Techniques Enabling Diverse Wireless Services , Mehmet Mert Şahin

Deep Reinforcement Learning Based Optimization Techniques for Energy and Socioeconomic Systems , Salman Sadiq Shuvo

Process Automation and Robotics Engineering for Industrial Processing Systems , Drake Stimpson

Theses/Dissertations from 2022 2022

Stability and Interaction Analysis of Inverter-Based Resources in Power Grids , Li Bao

Healthcare IoT System and Network Design , Halil Ibrahim Deniz

Video Anomaly Detection: Practical Challenges for Learning Algorithms , Keval Doshi

Data-Driven State Estimation for Improved Wide Area Situational Awareness in Smart Grids , Md Jakir Hossain

Deep Learning and Feature Engineering for Human Activity Recognition: Exploiting Novel Rich Learning Representations and Sub-transfer Learning to Boost Practical Performance , Ria Kanjilal

Assistive Technologies for Independent Navigation for People with Blindness , Howard Kaplan

Diagnosis of Neurodegenerative Diseases Using Higher Order Statistical Analysis of Electroencephalography Signals , Seyed Alireza Khoshnevis

Accelerating Multiparametric MRI for Adaptive Radiotherapy , Shraddha Pandey

A Model-Based Fault Diagnosis in Dynamic Systems via Asynchronous Motors System Identification or Testing, and Control Engineering Observers , Kenelt Pierre

Improving Wireless Networking from the Learning and Security Perspectives , Zhe Qu

Improving Robustness of Deep Learning Models and Privacy-Preserving Image Denoising , Hadi Zanddizari

Theses/Dissertations from 2021 2021

A Method for Compact Representation of Heterogenous and Multivariate Time Series for Robust Classification and Visualization , Alla Abdella

Dynamical System and Parameter Identification for Power Systems , Abdullah Abdulrahman Alassaf

Phasor Domain Modeling of Type-III Wind Turbines , Mohammed Alqahtani

An Automated Framework for Connected Speech Evaluation of Neurodegenerative Disease: A Case Study in Parkinson's Disease , Sai Bharadwaj Appakaya

Investigation of CoO ATO for Solar Cells and Infrared Sheaths , Manopriya Devisetty Subramanyam

Thermal Management of Lithium-ion Batteries Using Supercapacitors , Sanskruta Dhotre

Effect of Se Composition in CdSe 1-X T eX /CdTe Solar Cells , Sheikh Tawsif Elahi

Microencapsulation of Thermochromic Materials for Thermal Storage and Energy Efficiency of Buildings , Abdullatif Hakami

Piezoelectrically-Transduced ZnO-on-Diamond Resonators with Enhanced Signal-to-Noise Ratio and Power-handling Capability for Sensing and Wireless Communication Applications , Xu Han

Preparation and Characterization of Single Layer Conducting Polymer Electrochromic and Touchchromic Devices , Sharan Kumar Indrakar

Security Attacks and Defenses in Cyber Systems: From an AI Perspective , Zhengping Luo

Power System Optimization Methods: Convex Relaxation and Benders Decomposition , Minyue Ma

Metal Oxide Sensor Array Test Bed Prototype for Diagnostic Breath Analysis , Tiffany C. Miller

Packaging of Active RF Beamforming IC Utilizing Additive Manufacturing , Ryan Murphy

Adaptive Network Slicing in Fog RAN for IoT with Heterogeneous Latency and Computing Requirements: A Deep Reinforcement Learning Approach , Almuthanna Nassar

Development of a Bipolar Radiofrequency Ablation Device for Renal Denervation , Noel Perez

Copper Electrodeposition Assisted by Hydrogen Evolution for Wearable Electronics: Interconnections and Fiber Metallization , Sabrina M. Rosa Ortiz

Theory and Application of Dielectric Rod Antennas and Arrays , Gabriel Saffold

Advanced Organic Polymers for the Nanoscale Fabrication of Fiber-based Electronics Using the Electrospinning Technique , William Serrano Garcia

Transparent Planar Micro-Electrode Array for In-Vitro Electric Field Mediated Gene Delivery , Raj Himatlal Shah

High Speed Switching for Plasma Based Electroporation , Shivangi Sharma

Development of Small-Scale Power Supplies for Wearable Medical Diagnostic Devices , Donny Stiner

Novel Approach to Integrate CAN Based Vehicle Sensors with GPS Using Adaptive Filters to Improve Localization Precision in Connected Vehicles from a Systems Engineering Perspective , Abhijit Vasili

Modeling, Control and Analysis of Inverter-Based Generators in the Power Grids , Yangkun Xu

Fiber-Based Supercapacitor for Wearable Electronics , Rohit Lallansingh Yadav

Modeling, Identification, and Stability Analysis of Inverter-Based Resources Integrated Systems , Miao Zhang

Data-Oriented Approaches towards Mobile, Network and Secure Systems , Shangqing Zhao

Strategies in Botnet Detection and Privacy Preserving Machine Learning , Di Zhuang

Theses/Dissertations from 2020 2020

Architecture design and optimization of Edge-enabled Smart Grids , Adetola B. Adeniran

Multimodal Data Fusion and Attack Detection in Recommender Systems , Mehmet Aktukmak

Artificial Intelligence Towards the Wireless Channel Modeling Communications in 5G , Saud Mobark Aldossari

Enhancement of 5G Network Performance Using Non-Orthogonal Multiple Access (NOMA) , Faeik Tayseer Al Rabee

Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity , Mohmmed Alrowaily

Comprehensive Optimization Models for Voltage Regulation in PV-rich Multi-phase Distribution Systems , Ibrahim Alsaleh

Design and Implementation of Solid/Solid Phononic Crystal Structures in Lateral Extensional Thin-film Piezoelectric on Silicon Micromechanical Resonators , Abdulrahman Alsolami

Analysis of Computational Modeling Methods as Applied to Single-Crystal Organohalide Perovskites , Jon M. Bebeau

Development of a Monolithic Implantable Neural Interface from Cubic Silicon Carbide and Evaluation of Its MRI Compatibility , Mohammad Beygi

Performance Enhancement Techniques for Next-Generation Multi-Service Communication and Medical Cyber-Physical Systems , Ali Fatih Demir

Microfluidically Reconfigurable Millimeter-Wave Switches, Antenna Arrays and Filters with Fast-Actuation Using Movable Metallized Plates and Integrated Actuation , Enrique J. Gonzalez Carvajal

Multilayered Transmission Lines, Antennas and Phased Arrays with Structurally Integrated Control Electronics Using Additive Manufacturing , Merve Kacar

Cost Efficient Algorithms and Methods for Spectral Efficiency in Future Radio Access , Murat Karabacak

Design of DeLRo Autonomous Delivery Robot and AI Based Localization , Tolga Karakurt

Theory, Fabrication, and Characterization of Perovskite Phototransistor , Fatemeh Khorramshahi

Modeling and Control of Renewable Energy in Grids and Microgrids , Yin Li

Next-Generation Self-Organizing Communications Networks: Synergistic Application of Machine Learning and User-Centric Technologies , Chetana V. Murudkar

Reliability Analysis of Power Grids and its Interdependent Infrastructures: An Interaction Graph-based Approach , Upama Nakarmi

Algorithms Enabling Communications in the Presence of Adjacent Channel Interference , Berker Peköz

Electrospun Nanofibrous Membrane Based Glucose Sensor with Integration of Potentiostat Circuit , Kavyashree Puttananjegowda

Service Provisioning and Security Design in Software Defined Networks , Mohamed Rahouti

Reading and Programming Spintronic Devices for Biomimetic Applications and Fault-tolerant Memory Design , Kawsher Ahmed Roxy

Implementation of SR Flip-Flop Based PUF on FPGA for Hardware Security , Sai Praneeth Sagi

Trauma Detection Personal Locator Beacon System , Sakshi Sharma

Network Function Virtualization In Fog Networks , Nazli Siasi

Socially Aware Network User Mobility Analysis and Novel Approaches on Aerial Mobile Wireless Network Deployment , Ismail Uluturk

Spatial Stereo Sound Source Localization Optimization and CNN Based Source Feature Recognition , Cong Xu

Hybrid RF Acoustic Resonators and Arrays with Integrated Capacitive and Piezoelectric Transducers , Adnan Zaman

Theses/Dissertations from 2019 2019

Fabrication and Characterization of Electrical Energy Storage and Harvesting Energy Devices Using Gel Electrolytes , Belqasem Aljafari

Phasor Measurement Unit Data-Based Steady State and Dynamic Model Estimation , Anas Almunif

Cross Layer-based Intrusion Detection System Using Machine Learning for MANETs , Amar Amouri

Power Conditioning System on a Micro-Grid System , Tamoghna Banerjee

Thermal Response in a Field Oriented Controlled Three-phase Induction Motor , Niyem Mawenbe Bawana

Design and Development of a Wireless EEG System Integrated into a Football Helmet , Akshay V. Dunakhe

Machine Learning, Game Theory Algorithms, and Medium Access Protocols for 5G and Internet-of-Thing (IoT) Networks , Mohamed Elkourdi

Improving Stability by Enhancing Critical Fault Clearing Time , Ammara M. Ghani

RF Power Circuit Designs for Wi-Fi Applications , Krishna Manasa Gollapudi

Enhancing Secrecy and Capacity of Wireless Systems Using Directive Communications , Mohammed A. Hafez

Statistical Anomaly Detection and Mitigation of Cyber Attacks for Intelligent Transportation Systems , Ammar Haydari

Absorber and Window Study – CdSexTe1-x/CdTe Thin Film Solar Cells , Chih-An Hsu

Methods and Algorithms to Enhance the Security, Increase the Throughput, and Decrease the Synchronization Delay in 5G Networks , Asim Mazin

Piezoelectric ZnO Nanowires as a Tunable Interface Material for Opto-Electronic Applications , Anand Kumar Santhanakrishna

Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning , Farooq Israr Ahmed Shaikh

Diversity and Network Coded 5G Wireless Network Infrastructure for Ultra-Reliable Communications , Nabeel Ibrahim Sulieman

The Design of Passive Networks with Full-Wave Component Models , Eric Valentino

CubeSat Constellation Design for Intersatellite Linking , Michael T. White

Theses/Dissertations from 2018 2018

Design of Micro-Scale Energy Harvesting Systems for Low Power Applications Using Enhanced Power Management System , Majdi M. Ababneh

A Study on the Adaptability of Immune System Principles to Wireless Sensor Network and IoT Security , Vishwa Alaparthy

Validation of Results of Smart Grid Protection through Self-Healing , Felipe Framil Assumpção

A Novel Framework to Determine Physiological Signals From Blood Flow Dynamics , Prashanth Chetlur Adithya

The Effect of Processing Conditions on the Energetic Diagram of CdTe Thin Films Studied by Photoluminescence , Shamara P. Collins

Physical Electronic Properties of Self-Assembled 2D and 3D Surface Mounted Metal-Organic Frameworks , Radwan Elzein

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Home » Blog » Dissertation » Topics » Information Technology » IT Dissertation Topics (26 Examples) For Your Academic Research

IT Dissertation Topics (26 Examples) For Your Academic Research

Mark Dec 28, 2019 Jun 5, 2020 Information Technology No Comments

We aim to offer a vast collection of IT dissertation topics. You can find a suitable topic from the list of IT dissertation topics presented below. Writing a dissertation on information technology topics has its challenges. However, the evolution in the field of information technology offers a wide scope of research. You can start with […]

We aim to offer a vast collection of IT dissertation topics. Writing a dissertation on information technology topics has its challenges. However, the evolution in the field of information technology offers a wide scope of research. You can find suitable ideas from the list of IT research topics for your project as presented below.

Our team of expert writers can provide quality dissertations on the selected topics. We ensure that our work meets the required standards and deadlines.

A list Of IT dissertaton topics

Studying the technology-enhanced development of autonomous learning in tracking the student’s performance.

Role of e-HR systems in managing employee engagement and satisfaction.

Analysing the history and evolution of touch screen technology.

Exploring the concept of text mining and its pros and cons.

The extent to which social media has influenced the lives of humans in the 21st century.

How information systems are increasing the competencies of SMEs around the world?

How is education supporting the incorporation and development of new technologies?

To study the role of technology in controlling addictions.

The evolution of restaurant management systems and how it is helping restaurants in managing their day-to-day tasks?

Studying the importance of data mining and customer relationship management.

Examining the concept of e-commerce and the network security concerns in the context of SMEs.

Exploring the concept of the vehicle registration system and how it is helping in controlling traffic accidents and thefts.

Studying the threats of phishing email attacks on the online banking systems.

Exploring the opportunities of broadband technologies.

To study the creativity and effective electronic brainstorming concepts.

To find out the suitable methods of requirements modelling for business applications.

Investigating the piracy issues on the internet and how it affects the businesses.

Studying the systems dynamics approach in modelling the internet penetration.

The role of customer query tracker in the B2B models.

To study the best practices of a call intensive support desk and network operations centre.

Studying the impact of the information age on the modern-day society.

Analysing the server-based content adaptation architecture and its benefits.

A literature review on the development of interactive software engineering tools.

Conducting an investigation of cyber-crime based on internet piracy.

Examining the pros and cons of e-commerce solutions related to fashion businesses.

To analyse the impact of poor corporate intranet sites and their effects on staff performance in large enterprises.

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Information Technology Dissertation Topics

Published by Owen Ingram at December 29th, 2022 , Revised On March 23, 2023

Information technology stands out as one of the latest discoveries of the twenty-first century. According to researchers, technology is currently undergoing an era of transformation. Yet, despite all the hype, many students struggle to figure out a topic for their degree in Information Technology.

Nonetheless, we are right here to direct our students and show them a ray of hope. A comprehensive list of advanced dissertation topics in the field of information systems is provided below for students to pick a topic that suits their interests and research.

Related Academic Resource: Business Information Technology Topics , Technology Dissertation Topics , Green Technology Dissertation Topics

List of IT Dissertation Topics Having Potential for Research

• A literature analysis on the information quality management framework
• A comprehensive investigation of the information system hierarchy
• Big data and business intelligence are essential for sustainable development in organisations: Discuss a UK-based perspective
• Correlation between Information systems management and risk management infrastructure to achieve business risk resilience
• Impact of the Coronavirus on the management of X country’s information systems
• The function of structured versus unstructured data in managing information systems
• A review of the literature on business intelligence management and information systems
• Pre- and post-COVID analysis of the impact of information systems on organisational performance
• Implementing IT governance and managing information systems
• A descriptive overview of IS strategic planning and management services
• A review of the literature on international information system security
• Information systems management historical analysis focusing on the last three decades
• The part that planning, alignment, and leadership play in information systems management
• A systematic review of the post-COVID era for information systems management research
• Difficulties and possible challenges in the International Management of Information systems
• A thorough analysis of information policy and global information systems management
• How to handle data management in the era of 5G technologies?
• Human-computer interaction’s effect on innovations
• How does machine learning introduce students to more modern career opportunities?
• Consider the use of molecular information systems in biotechnology
• How has information technology aided in the processing of natural language?
• What are the most recent advancements in software engineering and programming languages?
• An examination of new potential in the robotics industry.
• What factors should I take into account while buying a bandwidth monitor?
• How do we develop an efficient clinic management system for intensive care?
• Reasons why e-waste management solutions should be used worldwide ASAP
• Motives for why cyberbullying persists in modern communication technologies
• Interpersonal communication has changed as a result of the development of information technology
• The effect of 3D printing on medical practice
• How well do colleges and universities produce qualified computer scientists using robots in infectious disease units?
• How ethical hacking has become more harmful
• Why having specialised financial systems is important
• What is the best security precaution: A fingerprint or a serial number?
• How to strengthen patent protection for technical advances?
• An overview of the many software security measures.

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Do you have a dissertation topic in the field of information technology? If not, our competent dissertation writers are at your disposal. The importance of technology research cannot be overstated. Several students are required to complete their information technology dissertations.

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Dissertation examples

Listed below are some of the best examples of research projects and dissertations from undergraduate and taught postgraduate students at the University of Leeds We have not been able to gather examples from all schools. The module requirements for research projects may have changed since these examples were written. Refer to your module guidelines to make sure that you address all of the current assessment criteria. Some of the examples below are only available to access on campus.

• Undergraduate examples
• Taught Masters examples