Ouroboros: Early identification of at-risk students without models based on legacy data
This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learn- ing models. This situation typically occurs in new courses. We present the concept of a ”self-learner” that builds the machine learning models from the data generated during the current course. The approach utilises information about al- ready submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models. There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk stu- dents using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the chal- lenge of imbalanced data, which appears both in training and testing data. The results show the comparison with the traditional ap- proach of learning the models from the legacy course data, validating the proposed concept.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding
Speculative decoding is a widely used method that accelerates the generation process of large language models (LLMs) with no compromise in model performance. It achieves this goal by using an existing smaller model for d…
Text GenerationCross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces
STEM dropout rates remain high at universities, particularly in computer science programs with theory-intensive courses. Digital learning environments now capture rich behavioral data that could help identify struggling …
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
Educational data mining (EDM) is a part of applied computing that focuses on automatically analyzing data from learning contexts. Early prediction for identifying at-risk students is a crucial and widely researched topic…
Knowledge DistillationStudent dropoutTransfer LearningEarly Detection of At-Risk Students Using Machine Learning
This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collecte…
Binary ClassificationStudent dropoutAdvanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back stude…