Existing Machine Learning Techniques for Knowledge Tracing: A Review Using the PRISMA Guidelines

Abstract

AbstractNowadays, Machine Learning (ML) techniques play an increasingly important role in educational settings such as behavioral academic pattern recognition, educational resources suggestion, competences and skills prediction, or clustering students with similar learning characteristics, among others. Knowledge Tracing (KT) allows modelling the learner’s mastery of skills and to predict student’s performance by tracking within the Learner Model (LM) the students’ knowledge. Based on the PRISMA method, we survey and describe commonly used ML techniques employed for KT shown in 51 articles on the topic, among 628 publications from 5 renowned academic sources. We identify and review relevant aspects of ML for KT in LM that contribute to a more accurate panorama of the topic and hence, help to choose an appropriate ML technique for KT in LM. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who need an overview of existing ML techniques for KT in LM.KeywordsMachine LearningKnowledge TracingLearner ModelTechnology enhanced learningLiterature reviewPRISMA