Multiple Learning Features–Enhanced Knowledge Tracing Based on Learner–Resource Response Channels

Zhifeng Wang (), Yulin Hou, Chunyan Zeng (), Si Zhang and Ruiqiu Ye
Additional contact information
Zhifeng Wang: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Yulin Hou: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Chunyan Zeng: Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
Si Zhang: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Ruiqiu Ye: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China

Sustainability, 2023, vol. 15, issue 12, 1-28

Abstract: Knowledge tracing is a crucial task that involves modeling learners’ knowledge levels and predicting their future learning performance. However, traditional deep knowledge tracing approaches often overlook the intrinsic relationships among learning features, treating them equally and failing to align with real learning scenarios. To address these issues, this paper proposes the multiple learning features, enhanced knowledge tracing (MLFKT) framework. Firstly, we construct learner–resource response (LRR) channels based on psychometric theory, establishing stronger intrinsic connections among learning features and overcoming the limitations of the item response theory. Secondly, we leverage stacked auto-encoders to extract low-dimensional embeddings for different LRR channels with denser representations. Thirdly, considering the varying impact of different LRR channels on learning performance, we introduce an attention mechanism to assign distinct weights to each channel. Finally, to address the challenges of memory retention and forgetting in the learning process and to handle long-term dependency issues, we employ a bidirectional long short-term memory network to model learners’ knowledge states, enabling accurate prediction of learning performance. Through extensive experiments on two real datasets, we demonstrate the effectiveness of our proposed MLFKT approach, which outperforms six traditional methods. The newly proposed method can enhance educational sustainability by improving the diagnosis of learners’ self-cognitive structures and by empowering teachers to intervene and personalize their teaching accordingly.

Keywords: attention mechanisms; bidirectional long short-term memory networks; knowledge tracing; learning performance prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/12/9427/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/12/9427/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:12:p:9427-:d:1169132

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().