Course Syllabus

Data and the Brain: Understanding Human Behavior through Big Data

Semester & Location:	Fall 2025 - DIS Copenhagen
Type & Credits:	Core Course - 3 credits
Major Disciplines:	Psychology, Neuroscience, Data Science
Related Disciplines:	Computer Science, Pre-Medicine/Health Science
Prerequisite(s):	One course in neuroscience or psychology + one course in research methods or data science at university level.
Faculty Members:	Dan-Anders Jirenhed, PhD (current students please use the Canvas Inbox)
Program Contact:	psy.cns@dis.dk
Time & Place:	See Course Schedule Classroom: TBA

 

Course Description

Technological advances present us with a unique opportunity to dramatically enhance our understanding of human behavior. How do people describe their thoughts, feelings, and their lives when unencumbered by the measurement biases and sampling issues inherent in psychological surveys? How can we study behaviors with precision outside to the controlled setting of a lab? What can we learn by exploring the nature of the human condition, from personality, to political attitudes, to cognitive functions, to healthy or unhealthy behaviors, through the lens of Big Data – i.e. large-scale data sets, in some cases built up through continuous sampling using technologies like smartphones and wearable devices? In this course, we will discuss cutting-edge psychology research in the intersection where data science meets the human mind. We will explore theory and research underlying models of psychological processes, big data analytics, machine learning techniques for handling psychological data, and the social, cultural, and ethical implications of human-computer interactions. There will be no programming in the course so prior experience is not required.

 

Learning Objectives

By the end of this course, students should be able to:

1. Understand the nature, scope and history of psychology research using Big Data sampling and analytics
2. Discuss how new research findings based on Big Data have broadened our understanding of human behavior
3. Describe various methodologies that are used in psychology and Big Data research, including use of Artificial Intelligence (AI) technologies for data processing
4. Understand the methodological strengths, weaknesses and pitfalls related to studies using Big Data and AI
5. Critically evaluate the social, cultural, and ethical implications of using Big Data and AI for research in psychology
6. Propose future directions for research in the intersection where data science and psychology meet.

 

Faculty

Dan-Anders Jirenhed, PhD. Dan is a full-time faculty member in Cognitive Neuroscience at DIS Copenhagen. He completed his MSc in Cognitive Science from Linköping University and PhD in Neurophysiology from Lund University. Prior to DIS, Dan worked as a Postdoctoral research fellow at Stanford University in the US and as a researcher at Lund University in Sweden.

Readings

Required readings will be listed for each individual class, so please check the calendar to identify what you should read before class. Note that the reading list may be edited and updated closer to the start date for the course.

• Adjerid, I. & Kelley, K. (2018). Big data in psychology: A framework for research advancement. American Psychologist, 73(7):899
• Back, M. D., Küfner, A. C., & Egloff, B. (2011). "Automatic or the people?": Anger on September 11, 2001, and lessons learned for the analysis of large digital data sets. Psychological Science, 22(6),
• Bhatia, S., Goodwin, G.P., & Walasek, L. (2018). Trait associations for Hillary Clinton and Donald trump in news media: A computational analysis. Social Psychological and Personality Science, 9(2):123–130
• Chen, E.E. & Wojcik, S.P. (2016). A practical guide to big data research in psychology. Psychological Methods, 21(4):458
• Montag C, Duke É, & Markowetz A. (2016). Toward Psychoinformatics: Computer Science Meets Psychology. Computational and Mathematical Methods in Medicine, doi: 10.1155/2016/2983685.
• Ruths, D. & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213): 10631064
• Yarkoni, T. (2012). Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 21(6):391–397
• Bhatt et al. (2023). Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions. Brain Informatics 10:18.
• Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755-759.
• Wang, J., Ouyang, H., Jiao, R., Cheng, S., Zhang, H., Shang, Z., ... & Liu, W. (2024). The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis. NPJ Digital Medicine, 7(1), 121.
• Newson, J. J., Bala, J., Giedd, J. N., Maxwell, B., & Thiagarajan, T. C. (2024). Leveraging big data for causal understanding in mental health: a research framework. Frontiers in Psychiatry, 15, 1337740.
• Hicks, J. L., Althoff, T., Sosic, R., Kuhar, P., Bostjancic, B., & King, A. C. Best practices for analyzing large-scale health data from wearables and smartphone apps. NPJ Digit Med. 2019; 2 (1): 45.
• Cao, Y., Kuai, H., Liang, P., Pan, J. S., Yan, J., & Zhong, N. (2023). BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease. Frontiers in Neuroscience, 17, 1202382.
• Vijay, V., Field, C. R., Gollnow, F., & Jones, K. K. (2021). Using internet search data to understand information seeking behavior for health and conservation topics during the COVID-19 pandemic. Biological Conservation, 257, 109078.
• Smith, K. A., Blease, C., Faurholt-Jepsen, M., Firth, J., Van Daele, T., Moreno, C., ... & Cipriani, A. (2023). Digital mental health: challenges and next steps. BMJ Ment Health, 26(1).
• Marsch, L. A. (2021). Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology, 46(1), 191-196.
  
  

Field Studies

Field studies serve to complement your course work by placing you in the professional field. Students will be asked to compare, extend and rethink what we read about and discuss in class. Please be ready for each field study by completing all readings and preparing questions in advance.

We may divide the class into smaller groups, each visiting different sites located in the greater Copenhagen area. Specific field study details are yet to be determined and will be announced closer to the start of the course.

Guest Lecturers

TBD. At certain points in the course, guest lecturers may be invited to provide their experience and expertise on select topics being covered in class.

Approach to Teaching

This course is comprised of both lectures and open discussions.  Students are therefore expected to participate actively in the discussions throughout the course to demonstrate critical thinking regarding the current state of the art in the intersection between psychology and data science.

The schedule will list reading materials for each class meeting. Please be prepared by having read and thought about the material before coming to class. By reading the material beforehand, you will better understand the points I make, you will be better prepared for discussion, and you will be able to ask thoughtful and productive questions.

DIS Accommodations Statement 

Your learning experience in this class is important to me.  If you have approved academic accommodations with DIS, please make sure I receive your DIS accommodations letter within two weeks from the start of classes. If you can think of other ways I can support your learning, please don't hesitate to talk to me. If you have any further questions about your academic accommodations, contact Academic Support acadsupp@dis.dk. 

Expectations of the Students

Class attendance is mandatory. Students are expected to have done the reading for each class and to come with notes and questions. This will give us material to generate conversation. It is also expected that during classes the students are able to discuss and to present topics and to respond to questions providing references to our readings to support their points. Active participation and engagement will account for 20% of your final grade, so it is to be taken seriously. 

Evaluation
To be eligible for a passing grade in this class you must complete all of the assigned work.

You will be evaluated based on your performance on the course assignments as indicated below. Additional details will be provided in class.

 

Grading

Assignment	Percent
Class participation and engagement (including field study participation)	20%
Discussion forum	20%
Practical Big Data exercise	25%
Research snapshot - a mini-TED talk	25%
Concluding reflection paper	10%

 

Participation and Engagement

Preparation, attendance, and engagement in classes and field studies is important because it shows that you are taking responsibility for your own learning. Your participation and engagement grade will be calculated based on your ability to meet the following criteria:

• You attend the class meeting/field study/guest lecture having done the day’s reading.
• You are engaged throughout our class meeting/field study/guest lecture and demonstrate this by prompting discussion and/or responding to your peers by linking comments, asking questions, and drawing connections between readings and themes.
• You listen attentively and respectfully to others (and you avoid dominating or silencing others).
• You offer more than just personal opinion or anecdote – that is, you root your comments in the text we are discussing (e.g., “this reminds me of p. 76 where the authors indicate X”) and link ideas and comments with content from past reading assignments.
• You work collaboratively with people to achieve learning goals when you are placed in a small group.
  

Discussion Forum

The purpose of the Canvas discussion forum is to give you an opportunity to think critically and ask questions about the readings before we meet for class. Each discussion post should be no more than 1 paragraph long and should reflect your own thinking about a particular issue as well as any questions that you have about the reading. Avoid sharing whether you liked the reading or found it to be interesting. Instead, through your comments, you must communicate that you have done each of the following: 

• Read the assignment closely,
• Challenged yourself to think critically about the theory/research findings, and,
• Attempted to draw connections with other class content 

Please proof-read carefully, use standard punctuation, appropriate language, and make sure you provide sufficient context so that your comments and questions make sense. You should be prepared to discuss your discussion post during our class session so please review it before class.

You are expected to write 5 discussion posts over the course of the semester - you can choose which discussion posts you wish to contribute to during this time.

Practical Big Data exercise

For this assignment, you will have the opportunity to synthesize and integrate your knowledge over the course of the semester, by analyzing a publicly available Big Data set and sharing your findings with your classmates in a creative format. Further details will be provided in class.

Research snapshot - a mini-TED talk

Given the incredible breadth of emerging research on the topic of the course, we are necessarily limited in our ability to review all the areas of sub-specialization within the research literature. Thus, you will have the opportunity to conduct an in-depth exploration of a topic that is of particular interest to you and share this information with your classmates via a 10-minute research snapshot. You are welcome to present this "live" during class, or, if you prefer to record the talk in advance, you may do so and we will watch the talk together in class (we will still have a brief Q/A session at the end in case there are questions).

The purpose of this activity is not only to allow you to gain in-depth knowledge, but also to educate and stimulate your classmates by presenting new research and insights into a particular area of scholarship. It is recommended that you identify topics that are reasonably focused/finite, non-overlapping with class content/readings, and are either controversial, difficult to understand, or perhaps “hot topics” in the current literature.

To prepare, you are expected to synthesize at least 7-8 recent peer-reviewed journal articles on your topic; these should form a coherent group, and at least one should be a systematic review or a meta-analysis paper (the rest may be empirical articles). You may also consult other scholarly sources (e.g., reputable news media, books) in addition to the required 7-8 journal articles (note that you will likely need to skim more to find the ones that you wish to synthesize for your presentation). You are strongly encouraged to incorporate Scandinavian research and/or perspectives into your presentation. 

In terms of presentation format, try to draw inspiration from TED-talks and share the overall "story" (themes, take-aways, limitations, pending questions...) about the research rather than just presenting a summary of each individual article. Think about what we know and what we don't know - what are the strengths and limitations of the current state of evidence on the topic?

Please submit your presentation slides, video (if pre-recorded), and full list of references in APA format in advance of your presentation. Please be prepared to answer questions from the class on your research topic. 

Concluding reflection paper

The purpose of this brief (1-2 page) final reflection is to give you an opportunity to consider your learning journey in this course and from your time in Denmark. What are some key take-away messages about how we can use Big Data for understanding human behavior? Has the course changed any of your perceptions? Is there anything you might do differently in the future as a result of taking this course? How can you carry your new learning forward? Please note that this final reflection will be graded as complete-incomplete so you should feel free to write authentically.

Course Policies 

Attendance: You are expected to attend all scheduled DIS classes.  If you miss a class for any reason, please contact the faculty no later than the day of the missed class. If you miss multiple classes, Academic Support will be notified and they will follow-up with you to make sure that all is well.  Absences will jeopardize your grade and your standing at DIS.  Allowances will be made in cases of illness or religious holidays.

Academic Honesty, Plagiarism, and Violating the Rules of an Assignment: DIS expects that students abide by the highest standards of intellectual honesty in all academic work. DIS assumes that all students do their own work and credit all work or thought taken from others.  Academic dishonesty will result in a final course grade of “F” and can result in dismissal. The students’ home universities will be notified. DIS reserves the right to request that written student assignments be turned in electronic form for submission to plagiarism detection software. See the Academic Handbook for more information, or ask your instructor if you have questions.

Policy on Late Assignments: Late assignments will be accepted up to 3 days late, but will incur a 10% penalty for each day they are late. They will not be accepted if they are more than 3 days late.

Extensions: You may request an extension for an assignment, but you must ask more than 1 day before the assignment is due. Extension requests on the due date, without an excusable reason, will not be considered.

Policy for Students Who Arrive Late to Class: Please come to classes on time as it is disturbing for the lecturer and other students. Repeated lateness will result in a referral to Academic Support.

Use of Laptops or Phones in Class: Computer use or phone use in class and during field studies is by permission only. If you are given permission to use your computer and/or phone in class, please note that these devices must be used solely for academic purposes (e.g. note-taking, literature searching, data handling purposes). Personal usage of electronic devices during class time (including field studies) is strictly forbidden. If you anticipate an emergency during class time that might require you to use your phone, please let me know in advance so that we can make a temporary exception to this policy for you.

 

Academic Regulations

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:

• Course Enrollment and Grading
• Academic Expectations and Honor Code

 

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