skip to main content

Explore Research Products in the NSF-PAR

Title: Context-Aware Attentive Knowledge Tracing
Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner’s future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to 6% in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.  more » « less
Award ID(s):
1931523 1724889
NSF-PAR ID:
10191147
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Page Range / eLocation ID:
330–2339
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ( , Proceedings of the Workshop Artificial Intelligence for Education (IJCAI 2021)))
    null (Ed.)
    The knowledge tracing (KT) task consists of predicting students’ future performance on instructional activities given their past performance. Recently, deep learning models used to solve this task yielded relative excellent prediction results relative to prior approaches. Despite this success, the majority of these models ignore relevant information that can be used to enhance the knowledge tracing performance. To overcome these limitations, we propose a generic framework that also accounts for the engagement level of students, the difficulty level of the instructional activities, and the natural language processing embeddings of the text of each concept. Furthermore, to capture the fact that students’ knowledge states evolve over time we employ a LSTM-based model. Then, we pass such sequences of knowledge states to a Temporal Convolutional Network to predict future performance. Several empirical experiments have been conducted to evaluate the effectiveness of our proposed framework for KT using Cognitive Tutor datasets. Experimental results showed the superior performance of our proposed model over many existing deep KT models. And AUC of 96.57% has been achieved on the Algebra 2006-2007 dataset. 
    more » « less
  2. ; ; ( , 2022 IEEE International Conference on Big Data (Big Data))
    Accurate modeling of student knowledge is essential for large-scale online learning systems that are increasingly used for student training. Knowledge tracing aims to model student knowledge state given the student's sequence of learning activities. Modern Knowledge tracing (KT) is usually formulated as a supervised sequence learning problem to predict students' future practice performance according to their past observed practice scores by summarizing student knowledge state as a set of evolving hidden variables. Because of this formulation, many current KT solutions are not fit for modeling student learning from non-assessed learning activities with no explicit feedback or score observation (e.g., watching video lectures that are not graded). Additionally, these models cannot explicitly represent the dynamics of knowledge transfer among different learning activities, particularly between the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning activities. In this paper, we propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT), which models knowledge transfer between learning materials, in addition to student knowledge, when students transition between and within assessed and non-assessed learning materials. TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities. Accordingly, our model allows for representing each material type in a different yet transferrable latent space while maintaining student knowledge in a shared space. We evaluate our model on three real-world publicly available datasets and demonstrate TAMKOT's capability in predicting student performance and modeling knowledge transfer. 
    more » « less
  3. ; ; ( , IEEE Transactions on Cybernetics)
    Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities. 
    more » « less
  4. ; ( , European Conference on Machine Learning and Principles and Practice of Knowledge)
    Knowledge tracing (KT), or modeling student knowledge state given their past activity sequence, is one of the essential tasks in online education systems. Research has demonstrated that students benefit from both assessed (e.g., solving problems, which can be graded) and non-assessed learning activities (e.g., watching video lectures, which cannot be graded), and thus, modeling student knowledge from multiple types of activities with knowledge transfer between them is crucial. However, current approaches to multi-activity knowledge tracing cannot capture coarse-grained between-type associations and are primarily evaluated by predicting student performance on upcoming assessed activities (labeled data). Therefore, they are inadequate in incorporating signals from non-assessed activities (unlabeled data). We propose Graph-enhanced Multi-activity Knowledge Tracing (GMKT) that addresses these challenges by jointly learning a fine-grained recurrent memory-augmented student knowledge model and a coarse-grained graph neural network. In GMKT, we formulate multi-activity knowledge tracing as a semi-supervised sequence learning problem and optimize for accurate student performance and activity type at each time step. We demonstrate the effectiveness of our proposed model by experimenting on three real-world datasets. 
    more » « less
  5. ; ; ; ; ; ( , EDM 2023)
    The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email