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.
People
Lab Members

Mengyan Wu
Collaborators

Adam Patterson
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
The growing popularity of Data Science courses demands more research on their pedagogy and assessment practices. Based on insights gained from the analysis of students’ programming assignment submissions in a Data Manipulation course (offered in an online Master of Applied Data Science program), this project explores the common errors and misconceptions that learners encounter while engaging in notebook-based introductory data science programming exercises.
We are now collaborating with Microsoft to build a human-AI collaborative tool that generates feedback on students’ data science programming mistakes. A pilot of this tool was deployed in the Fall’22 and Winter’23 offerings of the Data Manipulation course.
Project Leads: Anjali Singh and Christopher Brooks
Collaborators: PROSE team at Microsoft
Learnersourcing, a form of crowdsourcing where learners engage in learning activities while contributing useful inputs for other learners, is emerging as an effective technique for the generation of teaching and learning resources at scale. In this project, we take a student-centric approach to understand how we can design learnersourcing systems where students value the learnersourcing task, learn deeply, and generate high-quality output.
In Fall’20, we conducted a large-scale field experiment in the MOOC Introduction to Data Science in Python to study the effects of learnersourcing on students who create Multiple Choice Questions (MCQs) and their motivations for engaging in question generation.
We are now exploring the use of learnersourcing for generating feedback messages by prompting students to reflect on their mistakes in data science programming assignments.
Project Leads: Anjali Singh and Christopher Brooks
Smart home technologies are transforming childhood profoundly by shifting children’s play, learning, and social interaction to the digital realm. This development introduces privacy and safety risks for children. Smart speakers, wearables, even smart vacuums, capture data about children and their families for powering services and experiences, profiling, analytics, and commercial purposes – often without transparent communication of data practices. Children’s healthy development and self-identity rely on a safe and privacy-friendly (smart) home environment.
This line of research aims to understand children’s understanding of smart home related privacy and safety risks and explore age-appropriate ways to SUPPORT children and parents’ understanding and management of such digital privacy and safety risks.
Project leads: Kaiwen Sun, Chris Brooks, Florian Schaub
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
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
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
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
- replicate.education, where we list upcoming events we have for replication efforts
- the MOOC replication framework (MORF), an open-source docker-based enclave system for replication of educational models
Publications
Can’t see the publication list? Check out our google doc.
Funding provided in part by:






