External
Personalized Learning Recommendation System
Link: Portal
Description: This platform enhances teaching efficiency while providing students with a structured, interactive learning experience. Key Features:
- Flexible assessment Creation: Multiple ways to compile assessments or exercises.
- LLM-Powered Recommendations: Smart item suggestions.
- Customizable Templates: Save time when selecting items and materials next time.
- Real-Time Feedback: Instant scoring for students.
- Class Management: Organize students efficiently.
- Different interfaces for teachers and students: Provide different functions according to their respective needs.
User Guide: User guide
R Package: Latent (Variable) Analysis With Bayesian Learning (LAWBL)
Link: Github Link
Description: LAWBL represents a partially exploratory-confirmatory approach to model latent variables based on Bayesian learning. Built on the power of statistical learning, it can address psychometric challenges such as parameter specification, local dependence, and factor extraction. Built on the scalability and flexibility of Bayesian inference and resampling techniques, it can accommodate modeling frameworks such as factor analysis, item response theory, cognitive diagnosis modeling and causal or explanatory modeling. The package can also handle different response formats or a mix of them, with or without missingness.