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Found 10 papers, 4 papers with code
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
Learning gain differences between ChatGPT and human tutor generated algebra hints
Learning gains from human-created hints were substantially and statistically significantly higher than ChatGPT hints in both topic areas, though ChatGPT participants in the Intermediate Algebra experiment were near ceiling and not even with the control at pre-test.
Insights into undergraduate pathways using course load analytics
Course load analytics (CLA) inferred from LMS and enrollment features can offer a more accurate representation of course workload to students than credit hours and potentially aid in their course selection decisions.
Towards Equity and Algorithmic Fairness in Student Grade Prediction
With equity of educational outcome as the aim, we trial strategies for boosting predictive performance on historically underserved groups and find success in sampling those groups in inverse proportion to their historic outcomes.
Learning Skill Equivalencies Across Platform Taxonomies
Assessment and reporting of skills is a central feature of many digital learning platforms.
Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior.
Goal-based Course Recommendation
With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher.
Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance
The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact.
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC
This research lays the ground work for recommendation in a MOOC and other digital learning environments where high volumes of sequential data exist.
The Utility of Clustering in Prediction Tasks
Previous work has hinted at the improvement in prediction accuracy attributed to clustering algorithms if used to pre-process the data.
