Dynamic student classification on memory networks for knowledge tracing

Dynamic student classification on memory networks for knowledge tracing

Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provi...

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Bibliographic Details
Main Authors: MINN, Sein, DESMARAIS, Michel C., ZHU, Feida, XIAO, Jing, WANG, Jianzong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Key-value memory networks
Knowledge tracing
LSTMs
Massive open online courses
Student clustering
OS and Networks
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/4347
https://ink.library.smu.edu.sg/context/sis_research/article/5350/viewcontent/Dynamic_student_classification_on_memory_networks_for_knowledge_tracing.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art KT modelling techniques.

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