The Downside of Perseverance –Investigating and Moving Students Beyond Unproductive Persistence
This project investigates the role of persistence in an online math learning platform, ASSISTments. The goal of this project is to develop automated detectors that can differentiate between students’ productive and unproductive struggle, in order to better understand when persistence is beneficial. Findings of this project can help inform classroom practices and the design of educational technologies, towards supporting struggling young learners.
Making Math Tutors More Engaging and Effective through Interaction Design Patterns and Educational Data Mining
Using natural language processing techniques, the goal of this project is to investigate how the semantic and linguistic content of math problems relate to student engagement and math learning. Findings of this project can provide insight for developing effective math content, within and beyond the context of online tutoring systems.
MOOC Replication Framework
The MOOC Replication Framework (MORF) is being developed to enable the investigation of research questions on MOOCs across multiple data sets. MORF tests whether previously published findings on engagement and completion in MOOCs replicate to new data sets. Currently, MORF has access to over 150 MOOC data sets and is able to test 21 previously published findings on new data sets. We are currently working on improving MORF's user interface and increasing the findings and data sets analyzed.
Implementation of Adaptive Learning in MOOCs
We are currently involved in research on the implementation of adaptive learning in MOOCs through the integration of the Army Research Lab's Generalized Intelligent Framework for Tutoring (GIFT), Carnegie Mellon University's Cognitive Tutor Authoring Tools (CTAT), and edX. The pilot run will be conducted through Penn's first installment of Big Data and Education, which began on June 19, 2017. Enroll now!
Natural Language Processing in Digital Learning Spaces
We are researching natural language processing in digital learning spaces - how do teachers author content, and how to students experience and engage with this content? This work centers around studying the features of problems in mathematics contexts, using part-of-speech, bag-of-words, and semantic analysis tools such as WMatrix, CohMetrix, and TAALES, and analyzing the relationships that these features have to student affects, student performance, and student learning outcomes.
Power Statistics for BKT Parameter Estimates
We are also researching knowledge-tracing algorithms such as Bayesian Knowledge Tracing and Performance Factors Analysis. This project consists of calculating power statistics for BKT parameter estimates, to determine the necessary sample sizes to obtain reliable model parametrizations.
Linguistic Analysis and a Hybrid Human-Automatic Coach for Improving Math Identity. (National Science Foundation, Cyberlearning and Future Learning Technologies)
This project attempts to enhance an existing hybrid human-automatic learning system used at scale: the GenieMail system within the Reasoning Mind platform. The improved GenieMail system is expected to provide support and foster math identity for Reasoning Mind students. We are studying how students’ behaviors in the Reasoning Mind system, demographics, and mathematics skill relate to their math identity. This work will enable researchers to develop proxy measures for math identity that can be used to drive interventions.