Search Results for author:
Found 8 papers, 2 papers with code
On Fixing the Right Problems in Predictive Analytics: AUC Is Not the Problem
Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues (2023), critiquing the use of AUC ROC in predictive analytics in several domains.
Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems
We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
Auditing and Mitigating Cultural Bias in LLMs
Culture fundamentally shapes people's reasoning, behavior, and communication.
Towards Generalizable Detection of Urgency of Discussion Forum Posts
First, we train and cross-validate several models on an original data set of 3, 503 posts from MOOCs at University of Pennsylvania.
Large Language Models (GPT) for automating feedback on programming assignments
Furthermore, when GPT hints were unavailable, students in the experimental condition were initially less likely to solve the assignment correctly.
Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting Post-System Performance
We apply this extension to DKT and DKVMN, resulting in knowledge estimates that correlate better with a posttest than knowledge estimates from Bayesian Knowledge Tracing (BKT), an algorithm designed to infer knowledge, and another classic algorithm, Performance Factors Analysis (PFA).
Automated Identification of Verbally Abusive Behaviors in Online Discussions
Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings.
The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students
One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.
