The main goal of interactive educational systems is to design optimized, personalized learning paths for students. A primary limitation of these systems is the lack of a final outcome, such as a final exam grade, in student interaction datasets. To address this issue, assessment modeling is introduced as a class of pre-training tasks. In this approach, a deep bidirectional transformer encoder is utilized as the assessment model to evaluate and predict performance.

Introduction (Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems)

The related works section of the paper *Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems* focuses on two main research areas:

1. **Artificial Intelligence in Education (AIEd):** AIEd applies artificial intelligence and deep learning (DL) to improve educational outcomes. Key applications include knowledge tracing, question analysis, and student grade prediction.

2. **Pre-training Methods in Education:** While pre-training is widely used in natural language processing (NLP), computer vision, and speech recognition, its application in AIEd often focuses on predicting student exam scores (e.g., the SAT) or graduation likelihoods. Early educational pre-training methods were primarily NLP tasks where data was produced from learning materials. Notable prior models include the Test-aware Attention-based Convolutional Neural Network (TACNN), which predicts exam question difficulty, and QuesNet, an embedding model trained on question data that demonstrated superior performance over previous models.

Related Works (Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems)

Assessment modeling is a class of pre-training tasks for interactive educational systems. It predicts a masked assessment feature from the remaining interactions in a student's interaction sequence. The approach described in Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems uses a deep bidirectional transformer encoder.

Assessment Modeling in Interactive Educational Systems

1. Introduction
2. Related Works
3. Assessment Modeling
4. Experiments

San Diego State University

This collection groups two papers relevant to selecting the next learning material or assessment question for a learner: "Adaptive Learning Material Recommendation in Online Language Education" and "Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems."

Research Papers on Selecting the Next Question for a Learner

1. Introduction
2. Related Work
3. Modeling Vocabulary Knowledge
4. Adaptive Learning Material Recommendation
5. Evaluation of adaptive recommendation

Learn Before

Related

Learn After