Deep Knowledge Tracing for Personalized Adaptive Learning at Historically Black Colleges and Universities
Personalized adaptive learning (PAL) stands out by closely monitoring individual students' progress and tailoring their learning paths to their unique knowledge and needs. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Recent advancements in deep learning have significantly enhanced knowledge tracing through Deep Knowledge Tracing (DKT). However, there is limited research on DKT for Science, Technology, Engineering, and Math (STEM) education at Historically Black Colleges and Universities (HBCUs). This study builds a comprehensive dataset to investigate DKT for implementing PAL in STEM education at HBCUs, utilizing multiple state-of-the-art (SOTA) DKT models to examine knowledge tracing performance. The dataset includes 352,148 learning records for 17,181 undergraduate students across eight colleges at Prairie View A&M University (PVAMU). The SOTA DKT models employed include DKT, DKT+, DKVMN, SAKT, and KQN. Experimental results demonstrate the effectiveness of DKT models in accurately predicting students' academic outcomes. Specifically, the SAKT and KQN models outperform others in terms of accuracy and AUC. These findings have significant implications for faculty members and academic advisors, providing valuable insights for identifying students at risk of academic underperformance before the end of the semester. Furthermore, this allows for proactive interventions to support students' academic progress, potentially enhancing student retention and graduation rates.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge TracingMathSimilar Papers 제목 키워드 기반
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge …
Knowledge TracingLanguage ModelingLanguage ModellingEnhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning
In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique …
Knowledge TracingFuture-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education
Learning to learn is becoming a science, driven by the convergence of knowledge tracing, signal processing, and generative AI to model student learning states and optimize education. We propose CoTutor, an AI-driven mode…
Knowledge TracingDual-State Personalized Knowledge Tracing with Emotional Incorporation
Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and exp…
Knowledge TracingTransfer LearningLANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
In educational applications, Knowledge Tracing (KT), the problem of accurately predicting students' responses to future questions by summarizing their knowledge states, has been widely studied for decades as it is consid…
DecoderKnowledge Tracing