About Us

The educational technology collective is a group of students, staff, and faculty who explore the intersections of technology with teaching, learning, and education, with a particular focus on learning analytics, educational data mining, and collaborative engagement. We’re interdisciplinary, and include researchers with backgrounds in computer science, information, psychology, statistics, and more.

Interested in hearing more about what we do? Check out the projects listed below to see where you might fit, and contact the lead researcher to see what opportunities might exist for volunteering, part time work, or independent study. Or browse our github for current public codebases, or scholarly publications below, to get a sense of how we’re trying to build better education.

We meet weekly in the School of Information at the North Quadrangle building on the University of Michigan central campus.

People

Faculty

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Christopher Brooks

Assistant Professor
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Quan Nguyen

Postdoc

Graduate Students

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Heeryung Choi

PhD Student
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Warren Li

PhD Student
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April Wang

PhD Student
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Anjali Singh

PhD Student
Kaiwen

Kaiwen Sun

PhD Student
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Zihan Wu

PhD Student

Undergraduate Students

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John Penington

Collaborators

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Nia Dowell

University of California, Irvine Assistant Professor
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Rebecca Quintana

UM Office of Academic Innovation: Learning Experience Design Lead
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In Son Zeng

University of Michigan Master's Student in Applied Statistics
Anshul

Anshul Aggarwal

University of Michigan Master's Student in Data Science
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Wilson

Official Lab Sponsor: Quarantine Support Ball

Alumni

Jing Hu

Kayla Ratliff

Sean Yoon

Steven Olthoff

Pheobe Liang

Adhi Appukutty

Runhe Lu

Tim Van der Zee

Timothy NeCamp

Sagnik Roy

Sansitha Nandakumar

Wenfei Yan

Andrew Godfrey

Yiwen Lin

Yuming Yang

Shi Lu

Josh Gardner

Anant Mittal

Projects


Understanding the nuances of a student’s education requires looking beyond single metrics and datasets. Despite being driven by a common goal of using data-driven inquiry to positively affect teaching and learning in higher education, our methods, tools, and data are different. As a result, we aim to tackle these issues by connecting various forms of knowledge representations from a variety of disciplinary methodologies to construct a holistic model of education.

Some examples of projects we’re working on include: building explanatory models to identify underlying causes of success for next-generation intervention systems and creating infrastructure for querying indicators of student achievement across disparate datasets in order to facilitate research.

Project leads: Christopher Brooks, with SI faculty Stephanie Tealsey and Kevyn Collins-Thompson, LSA faculty Tim McKay and Gus Evrard, and AOS faculty Perry Samson


The Mentor Academy is a data-science learning community of practice where students volunteer their knowledge, skills, and time to give back to new students. Built around the content of the Applied Data Science with Python online course on Coursera, students are able to give back by creating new  culturally relevant problem sets for online learners or engaging in mentorship in discussion forums.

The first cohort of mentors for the Mentor Academy were recruited in October 2017. The next  iteration of the academy is in planning.


Project leads: Anant Mittal and Christopher Brooks

Computational notebooks enable data scientists to document their exploration process through a combination of code, narrative text, visualizations, and other rich media. The rise of big data has increased the demand for data scientists degree programs in colleges, as well as data science MOOCs. In data science classrooms, instructors use computational notebooks to demonstrate code and its output. While learners navigate example notebooks to enhance their perceived knowledge from watching video lectures, or create their own notebooks for assignments or capstone projects. In the Educational Technology Collective, we reflect on the current practice of computational notebooks in data science courses and explore the opportunities and challenges of better adapting computational notebooks for data science education. In particular, we have several projects on redesigning computational notebooks for supporting real-time group collaboration, facilitating joint discourse over shared context, and encouraging active learning.

Project leads: April Wang, Christopher Brooks, and Steve Oney
Collaborators: Anant Mittal and Zihan Wu

Advances in artificial intelligence, coupled with extensive data collection provide unparalleled insights into determinants of every-day activities. In education this has spawned research on predictive modeling of learning success, which are used to power early warning systems. They can be used to identify students who are at-risk of failing or dropping out of a course, and to intervene if necessary.  However, because the effectiveness of these systems rests upon the collection of student data, including sensitive information and confidential records, this creates a tension between developing effective predictive models while supporting learners’ agency and privacy.  

In our research, we are interested in finding out students’ views on the collection and use of their educational records, as well as their overall privacy perceptions and how this, combined with personal background and academic information, may be tied to one’s propensity to opt-in or opt-out of having their data collected for the purposes of predictive modeling. These findings may impact institutions’ abilities to help identify “at-risk” students, suggest possible intervention strategies, and to contextualize opt-in or opt-out, thereby impacting how they choose to interact with students. We also gain a better understanding of whether students are aware about how their personal data is used, how the use of this data should be managed, and how these decisions impact predictive models’ performance. 

Project Leads: Warren Li, Kaiwen Sun, Florian Schaub, and Christopher Brooks

Self-regulated learning (SRL) is the process where a learner monitors and controls metacognition, cognition, motivation, affect, and contextual factors to achieve goals ​(Boekaerts & Niemivirta, 2000; Greene & Schunk, 2017; Pintrich, 2000; Winne, 2001; Zimmerman, 2000)​. SRL process includes numerous goal-oriented actions from building strategies to tackle a given task to evaluate learners’ performance. Researchers who study SRL should carefully align their study design with these common features, regardless of which models they use as a fundamental theoretical base for the study. In particular, alignment between measures and theory is important, because SRL measures indicate how researchers understand and model SRL processes ​(Greene & Azevedo, 2010)​. Because of the importance of alignment between measurements and SRL models, there have been discussions on how to verify and improve the validity of measurements detecting SRL components. Through the study, I will investigate the validity issues of SRL measures used in previous studies and discuss the current approaches to resolving the issues. Furthermore, I will focus on systems for investigating the validity of trace data and self-report data on learners’ help-seeking strategy usages.

Project Leads: Heeryung Choi and Christopher Brooks

Network analysis in educational research has primarily relied on self-report and/or data generated from online learning environments (e.g. discussion forums). However, a large part of social connections amongst students occurs through day-to-day interactions on campus. This research project explores the application of spatial-temporal network data to model social network structure amongst students on campus at scale. Links between individuals were inferred based on their spatial co-occurrences along a similar temporal dimension (i.e. two individuals connected to the same access point at the same time). We propose a potential approach to test the statistical significance of these connections against a null model, in which two individuals might randomly be at the same place at the same time. 

Project Leads: Quan Nguyen, Warren Li and Christopher Brooks

Whether machine-learned, human generated, or a hybrid, models of student success in education need to be replicated in new contexts and datasets in order to ensure generalizability . We’re building the software to do this, and hooking it up to large datasets of hundreds of classes with millions of learners through collaboration between the University of Michigan and the University of Pennsylvania.

Links for more information

Project leads: Josh Gardner and Christopher Brooks

Our work focuses on nurturing the development of self-regulated learning (SRL) skills in students.  Self­regulation is an important feature of engaged learners: those who have the ability, willingness, and experience to reflect on how their behaviors relate to learning outcomes. By predicting the academic success of students (PASS) and revealing how that success correlates with their activities, we aim to help learners engage in conversations with themselves, their peers, and their instructors to improve learning practices and outcomes.

In collaboration with the Information Quest team, we’re building supervised machine learning models and additional infrastructure to achieve these goals and bridge the gap between research and production. We aim to support the work of researchers and tool developers who want to make use of predictive models, and to impact the broader landscape of higher education through partnerships such as the Unizin Consortium.

Project leads: Warren Li and Christopher Brooks

Collaborators: Information Quest (IQ), Josh Gardner

Publications

Can’t see the publication list? Check out our google doc.

Funding provided in part by: