Machine Learning Techniques for Knowledge Tracing: A Systematic Literature Review (original) (raw)
Related papers
Journal of Information Technology and Computer Science, 2021
Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the d...
Intelligent Knowledge Tracing: More Like a Real Learning Process of a Student
ArXiv, 2018
Knowledge tracing (KT) refers to a machine learning technique to assess a student's level of understanding (so-called knowledge state) of a certain concept based on the student performance on problem solving. KT accepts a series of question-answer pairs as an input and iteratively updates the knowledge state of the student, eventually returning the probability of the student solving an unseen question. From the viewpoint of neuroeducation (the field of applying neuroscience, cognitive science, and psychology to education), however, KT leaves much room for improvement in terms of explaining the complex process of human learning. In this paper, we identify three problems of KT (namely non adaptive knowledge growth, neglected latent information, and unintended negative influence) and propose a memory-network-based technique named intelligent knowledge tracing (IKT) to address them, thus approaching one step closer to understanding the complex mechanism underlying human learning. In...
2013
Recent interest in online education, such as Massively Open Online Courses and intelligent tutoring systems, promises large amounts of data from students solving items at different levels of proficiency over time. Existing approaches for inferring students’ knowledge from data require a cognitive model – a mapping between the tutor problems and the set of skills they require. This is a very expensive requirement, since it depends on expert domain knowledge. The success of previous methods in using student performance data to construct this mapping automatically has been limited in that they cannot handle data collected over time, or that they require expensive expert domain knowledge. This dissertation studies how to model students’ time varying knowledge, without requiring expert domain knowledge. We introduce four novel methods: • Dynamic Cognitive Tracing: an easily implemented prototype that jointly estimates cognitive and student models. • Automatic Knowledge Tracing: a method ...
International Journal of Electrical and Computer Engineering (IJECE), 2025
With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling
IEEE Transactions on Learning Technologies, 2021
Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability. Index Terms-Educational technology, computer-aided instruction, learning management systems, models of learning, knowledge tracing, model comparison. I. CREATING AND USING LOGISTIC REGRESSION LEARNER MODELS TO INFORM LEARNING TECHNOLOGY PEDAGOGY Logistic regression is a statistical method that has been used by many investigators to characterize student performance for various learning tasks. In this paper, we explain a formalized approach to creating logistic regression models that subsumes other methods and provides flexibility that allows better determination of an accurate model than off-the-shelf approaches like the Additive Factors Model (AFM), Instructional Factors Analysis (IFA), Performance Factors Analysis (PFA), PFA-Decay or Recent-PFA (R-PFA) [1]-[7]. We focus this work on building models that generalize to new learners for which there is no data, which is a typical situation when attempting to optimize adaptive instruction in a learning system.
Response Tabling – A simple and practical complement to Knowledge Tracing
2011
In this paper we introduce a method of predicting student performance by simply calculating the expected outcome of students with the same sequence or subsequence of responses. This expected outcome, which is simply the percent correct, can be calculated for each response subsequence. The combination of expected outcomes for each subsequence can then be combined for a final prediction of a particular student response. Using skill builder problem sets from the ASSISTments Platform we tested this algorithm against an established model of learning called Knowledge Tracing. Both methods utilized the same data which was only student response data. We found that the Tabling method slightly exceeded knowledge tracing in prediction accuracy. The tabling method training time was minimal, taking only a few seconds to train compared to the 30 minute training time of knowledge tracing. We believe this work offers a valuable alternative to knowledge tracing for use with prediction tasks when inf...
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
2018 IEEE International Conference on Data Mining (ICDM), 2018
In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.
A review of machine learning methods used for educational data
Education and Information Technologies, 2024
Integrating machine learning (ML) methods in educational research has the potential to greatly impact upon research, teaching, learning and assessment by enabling personalised learning, adaptive assessment and providing insights into student performance, progress and learning patterns. To reveal more about this notion, we investigated ML approaches used for educational data analysis in the last decade and provided recommendations for further research. Using a systematic literature review (SLR), we examined 77 publications from two large and high-impact databases for educational research using bibliometric mapping and evaluative review analysis. Our results suggest that the top five most frequently used keywords were similar in both databases. The majority of the publications (88%) utilised supervised ML approaches for predicting students' performances and finding learning patterns. These methods include decision trees, support vector machines, random forests, and logistic regression. Semi-supervised learning methods were less frequently used, but also demonstrated promising results in predicting students' performance. Finally, we discuss the implications of these results for statisticians, researchers, and policymakers in education.
Do we need to go Deep? Knowledge Tracing with Big Data
2021
Interactive Educational Systems (IES) enabled researchers to trace student knowledge in different skills and provide recommendations for a better learning path. To estimate the student knowledge and further predict their future performance, the interests in utilizing the student interaction data captured by IES to develop learner performance models is increasing rapidly. Moreover, with the advances in computing systems, the amount of data captured by these IES systems is also increasing that enables deep learning models to compete with traditional logistic models and Markov processes. However, it is still not empirically evident if these deep models outperform traditional models on the current scale of datasets with millions of student interactions. In this work, we adopt EdNet, the largest student interaction dataset publicly available in the education domain, to understand how accurately both deep and traditional models predict future student performances. Our work observes that l...