Knowledge Tracing Research Articles

Correction: Twenty-Five Years of Bayesian knowledge tracing: a systematic review

Correction: Twenty-Five Years of Bayesian knowledge tracing: a systematic review

Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing

Knowledge Tracing (KT) aims to predict students’ future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g., question-student , question-skill relation), relationship attributes (e.g., correctness indicating a student's performance on a question), and node attributes (e.g., student ability ). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the biased random walk algorithm is designed. In addition, the auxiliary node generation method is proposed to capture high-order question similarity. Finally, extensive experiments conducted on 6 datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity.

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

DKVMN&MRI: A new deep knowledge tracing model based on DKVMN incorporating multi-relational information.

Knowledge tracing is a technology that models students' changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners' knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.

DHKFN

Knowledge tracing is effective in modeling learners' knowledge levels to predict future answering situations based on their past learning history and interaction processes. However, current methods often overlook the impact of knowledge point correlations on answer prediction. This paper design a model based on deep hierarchical knowledge fusion networks (DHKFN). It utilizes statistical approaches to construct correlation matrices to capture the correlations between knowledge points. Subsequently, it constructs a topology graph structure of questions and knowledge points and utilizes a multi-head attention network to learn student interaction information from multiple perspectives, effectively constructing adjacency matrices. Finally, it uses GCN to learn the interaction information representation between deep-level knowledge points from dynamically constructed a topology graph structures. Experimental results on three large public datasets show that DHKFN effectively considers the influence of correlations between different knowledge points, thus showing promising effectiveness.

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective

A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches.

STHKT: Spatiotemporal Knowledge Tracing with Topological Hawkes Process

Knowledge Tracing (KT) is a method that seeks to forecast students’ future performance based on their historical interactions with intelligent tutoring systems. Various KT techniques have been developed from distinct perspectives, such as probability, logic, and deep learning, to scrutinize interaction sequences. However, most current methods predominantly concentrate on temporal or structural information, considering simple question sequences or interaction sequences, but fail to describe the topological dependencies between sequences, which can lead to changes in causal structures,as referred to as dynamic structural information in the paper. The dynamic information includes the clustering effect among concepts and questions, as well as the group effect among students and questions: Questions with the same knowledge concepts tend to present increasingly similar response patterns in the sequence, and concurrently, students who answer similar questions often exhibit closer knowledge states. We introduce the STHKT method, which employs a tripartite heterogeneous graph to capture relationships among concepts, questions, and students. By incorporating temporal information through a topological graph, we employ two Graph Convolution Networks (GCNs) to model the two effects separately. Following this, we apply the Hawkes Process to introduce a spatiotemporal attention mechanism that combines both effects. Through comparative experiments, ablation studies and case studies, we validate the efficacy of key components in the model. Experiment results indicate that the STHKT model can effectively model the two major effects, integrate dynamic structural information, and to some extent improve the accuracy of predicting students’ response probabilities. It is the first time that these two effects have been considered in the process of knowledge tracing, and it is adaptable to large-scale online education platforms.

Personalized process–type learning path recommendation based on process mining and deep knowledge tracing

Personalized learning path recommendation considers learning goals, learning abilities, and other personalized characteristics of learners to generate a suitable learning path. Existing approaches include global optimal and local iterative path recommendation, which recommend a sequence of learning objects. Consequently, the learner can only learn in the order specified by the learning path, which provides limited flexibility for the learner. In addition, existing studies cannot both present the complete path and handle changes in the learner's knowledge state while learning along the path. This study proposes a process-type learning path model and its recommendation approach, which presents a learning path in the form of a flowchart and dynamically recommends path branches according to the knowledge states of the learner during the learning process. Specifically, deep knowledge tracing is used to annotate the knowledge states of learners in historical logs, and process mining is used to generate a personalized process–type learning path that contains sequences, parallel relationships, and selection relationships between learning objects. In addition, the correlation between the knowledge state and the selection of different branches of a learning path in historical logs can be obtained via decision mining. Thus, a branch recommendation model is trained and used to recommend a path branch in a process-type path with the highest probability of mastering the target learning object of the learner based on the learner's knowledge state. The experimental results demonstrate that the learning effectiveness and efficiency of the proposed approach are better than those of the existing approaches.

MLC-DKT: A multi-layer context-aware deep knowledge tracing model

As a fundamental research task for intelligent education, knowledge tracing (KT) aims to trace learners’ dynamic knowledge states and predict their future performance based on their historical response data. However, learners’ cognitive processes cannot be studied in isolation from the context; nevertheless, existing knowledge tracing models mainly use knowledge topology as auxiliary information but do not deeply explore the contextual information of exercises. Thus, an in-depth exploration of multi-layer contextual information in learning scenarios is required, focusing on the cognitive differences among different learners. Furthermore, knowledge tracing models exhibit limited interpretability compared with cognition diagnosis methods integrating educational priors. To address these issues, we propose a multi-layer context-aware deep knowledge tracing (MLC-DKT) model. Specifically, we first present the multi-layer contextual representation method involving knowledge concepts and exercises. In the knowledge state tracing module, we incorporate the learners’ knowledge priors into the attention mechanism to capture the evolution of the learners’ knowledge. Then, we develop a calculation method for the similarity effect among the contextual information, which helps estimate the learners’ knowledge states. In the learning performance prediction module, we introduce the guessing and slipping factors from the cognition diagnosis model to optimize the MLC-DKT model, further improving the performance of the KT model. Finally, we conduct extensive experiments on real-world datasets, demonstrating that the MLC-DKT model provides improved predictions of learner performance. Moreover, the MLC-DKT model realizes contextual awareness from knowledge concepts and exercises, making the predictions more interpretable.

An efficient state-aware Coarse-Fine-Grained model for Knowledge Tracing

Knowledge tracing (KT) is the problem of predicting students’ future performance based on their historical learning records, with primary emphasis on capturing their evolving knowledge states. This predictive capacity enables online education platforms to recommend personalized learning resources and guidance to students who require further attention. By leveraging deep learning techniques, KT models have demonstrated impressive experimental performance in capturing and analyzing the complex interactions between students and exercises. However, most approaches tend to prioritize predictive performance rather than a detailed consideration of how students’ knowledge states evolve over time, resulting in unstable and unexplainable outcomes. To address these limitations, in this paper, we propose an efficient state-aware Coarse-Fine-Grained model for Knowledge Tracing (CFGKT), which aims to facilitate deep tracing of students’ intrinsic knowledge states in their learning processes. Specifically, CFGKT introduces three key innovations: (1) a coarse-grained block that diagnoses students’ proficiency in specific concepts based on their historical performance; (2) a fine-grained block that progressively measures and accumulates students’ experience acquisition from past interactions; and (3) a novel state-fusion attention that selectively integrates both exclusive proficiency and cumulative experience to estimate students’ knowledge states for performance prediction. Numerous experiments on four real-world datasets demonstrate that CFGKT exhibits superior prediction accuracy and interpretability compared to existing KT methods. The code and data are publicly available at https://github.com/hzLu0/CFGKT.

A Genetic Causal Explainer for Deep Knowledge Tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based knowledge tracing (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this paper, we propose a causal explanation method based on the genetic algorithm (GA), named Genetic Causal Explainer(GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multi-strategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.

Explore Bayesian analysis in Cognitive-aware Key–Value Memory Networks for knowledge tracing in online learning

Knowledge Tracing is a crucial aspect of personalized learning that aims to track the evolving knowledge states of students with respect to one or more concepts. However, existing literature either models knowledge state for each predefined concept separately, potentially overlooking the underlying interrelationships between concepts, or lacks the capability to uncover long-term dependencies within a historical sequence of questions. Furthermore, they have limitations in precisely identifying the mastery level of individual learners for specific concepts. In order to address these problems, we propose a novel deep learning-based knowledge tracing model, named Bayesian Cognitive-aware Key–Value Memory Network (BCKVMN). BCKVMN combines the strengths of Dynamic Key–Value Memory Network (DKVMN), Long Short-Term Memory (LSTM), embedding layer, and Bayesian theory. To capture long-term dependencies in question sequences and contextual information, we extend DKVMN by incorporating a standard LSTM module that integrates cognitive processes observed in human learning. Additionally, we employ an embedding layer to uncover the influence of question difficulty on final predictions. Furthermore, we introduce Bayesian theory to enhance parameter interpretation for the model. To investigate complex representation of human learning, our approach considers not only three distinct learning states during the learning process but also the relationships between underlying concepts and students’ mastery levels for each concept. Experimental evaluations are conducted on the three real-world datasets Algebra200, ASSISTMENTS2009 and ASSISTMENTS2017. The results have demonstrated that the proposed model outperforms state-of-the-art methods (achieving nearly 7% improvement in AUC in some cases) while achieving a better balance between performance and interpretability.

A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

Model-agnostic counterfactual reasoning for identifying and mitigating answer bias in knowledge tracing

Knowledge tracing (KT) aims to monitor students’ evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students’ knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students’ responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT. We have released our code at https://github.com/lucky7-code/CORE.

Bayesian Knowledge Tracing Implemented in a Telecommunications Serious Game

The University of Johannesburg has integrated serious games into its teaching, exemplified by Codebreakers, a 2D game teaching information theory. While successful, Codebreakers lacked personalisation and used a criticised assessment method based on answer streaks. Knowledge tracing algorithms, known for their effectiveness in intelligent tutoring systems, were considered to address these limitations. This led to the research question: "Can a new serious game be designed, incorporating knowledge tracing algorithms to deliver personalised learning experiences in telecommunications education?" In response, an escape-themed serious game was developed, integrating Bayesian Knowledge Tracing as a statistical student model for personalised learning. This innovative approach combines free-roam gameplay with tailored educational content, significantly advancing serious game design. While primarily aimed at enhancing Codebreakers, this new game contributes substantially to serious game theory by successfully implementing personalised learning within an engaging format. The project showcases the potential of knowledge tracing algorithms in creating adaptive, student-centered learning experiences within the context of educational games.

Programming knowledge tracing based on heterogeneous graph representation

Existing programming knowledge tracing methods have not deeply explored the relationships between knowledge concepts and programming questions or students’ codes, which leads to insufficient representation of programming questions and students’ codes and insufficient tracing of students’ knowledge states. To address these problems, this study proposes a programming knowledge tracing method based on a heterogeneous graph representation. We constructed a heterogeneous graph, where nodes represent the knowledge concept, programming question, and student code, and edges represent the relationships among knowledge concepts, programming questions, and student code. Subsequently, we leverage a graph structure learning algorithm to exploit the potential connections among them, aiming to enhance the graph structure information. Based on the graph, we further fully integrated the knowledge concept information into the representations of programming questions and student codes through the graph convolutional representation learning algorithm. Moreover, an attention mechanism is utilized to compute the effect of programming questions and student codes on the knowledge state and update the student's knowledge state in conjunction with the corresponding answer results. Furthermore, the proposed method provides an assessment of the mastery level for each knowledge concept rather than only assessing each question using traditional methods. Experiments on two datasets, CodeWorkout and CodeNet, show that the proposed method achieves an average improvement of 4.28% in ACC and 6.48% in AUC compared with the baseline models, validating the effectiveness of the proposed method.

Meta-path structured graph pre-training for improving knowledge tracing in intelligent tutoring

Knowledge tracing (KT) aims to predict students’ future performance by tracking their learning behaviors in intelligent tutoring systems (ITS). In KT, three main types of entities are involved: students, exercises, and knowledge concepts. Graph structures provide an effective framework for establishing relationships between different entities. However, existing KT methods have neglected complex connections and implicit relational features, thus facing challenges in capturing high-order information in educational data. To this end, this paper proposes MPSG, a Meta-Path Structured Graph method designed to harness high-order information between entities to pre-train informative exercise embeddings for improving KT. Technically, structured by different meta-paths, four relation graphs are derived to establish implicit cross-entity relationships. On each graph, the surrounding neighbors of each node are then obtained through an intimacy-based sampling strategy. Subsequently, during the representation learning stage, node features under different meta-path views are generated and then aggregated to obtain the final exercise embeddings. The learned embeddings are optimized through three auxiliary tasks, anchored within a self-supervised learning paradigm. Extensive experiments across four public datasets demonstrate that our method can significantly enhance the predictive performance of downstream KT models.

A study of progressive data flow knowledge tracing based on reconstructed attention mechanism

A study of progressive data flow knowledge tracing based on reconstructed attention mechanism

Knowledge ontology enhanced model for explainable knowledge tracing

Knowledge Tracing (KT) aims to predict learners’ future learning outcomes based on their past learning interactions. Deep Knowledge Tracing (DKT) is a technology developed in recent years that employs deep learning techniques to dynamically track students’ learning progress and offer personalized learning support. Yet, existing research often miss the complex interplay between student characteristics and knowledge components, limiting interpretability and adaptability in assessments. Addressing this gap, we introduce the Ontology Perceptible Knowledge Tracing (OPKT) model, a novel approach that reconceptualizes student-knowledge interaction by integrating both student and knowledge ontologies directly into the tracing process. This integration allows for a more nuanced representation of learning interactions, significantly enhancing the predictive precision and interpretability of the model. The OPKT model extracts and utilizes ontology features—deriving student attributes from behavioral data and aligning them with knowledge aspects through sophisticated relationship mapping. This dual-layer integration captures the evolution of knowledge states more accurately, facilitating targeted educational interventions. The results of prediction on two publicly available datasets demonstrate that our proposed OPKT model achieves at least a 2% improvement in AUC, accurately assessing students’ learning outcomes and knowledge proficiency. Additionally, we conducted dimensionality reduction experiments and ablation studies to validate the interpretability of the model.