Augmenting Knowledge Tracing by Considering Forgetting Behavior

Computer-aided education systems are now seeking to provide each student with personalized materials based on a student's individual knowledge. To provide suitable learning materials, tracing each student's knowledge over a period of time is important. However, predicting each student's knowledge is difficult because students tend to forget. The forgetting behavior is mainly because of two reasons: the lag time from the previous interaction, and the number of past trials on a question. Although there are a few studies that consider forgetting while modeling a student's knowledge, some models consider only partial information about forgetting, whereas others consider multiple features about forgetting, ignoring a student's learning sequence. In this paper, we focus on modeling and predicting a student's knowledge by considering their forgetting behavior. We extend the deep knowledge tracing model [17], which is a state-of-the-art sequential model for knowledge tracing, to consider forgetting by incorporating multiple types of information related to forgetting. Experiments on knowledge tracing datasets show that our proposed model improves the predictive performance as compared to baselines. Moreover, we also examine that the combination of multiple types of information that affect the behavior of forgetting results in performance improvement.

[1]  Zachary A. Pardos,et al.  Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing , 2011, EDM.

[2]  Radek Pelánek,et al.  Adaptive Geography Practice Data Set , 2016 .

[3]  Burr Settles,et al.  A Trainable Spaced Repetition Model for Language Learning , 2016, ACL.

[4]  Haiqin Yang,et al.  Heterogeneous Features Integration in Deep Knowledge Tracing , 2017, ICONIP.

[5]  Varun Ganapathi,et al.  GritNet: Student Performance Prediction with Deep Learning , 2018, EDM.

[6]  Neil T. Heffernan,et al.  Addressing the assessment challenge with an online system that tutors as it assesses , 2009, User Modeling and User-Adapted Interaction.

[7]  Tat-Seng Chua,et al.  Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.

[8]  Dit-Yan Yeung,et al.  Dynamic Key-Value Memory Networks for Knowledge Tracing , 2016, WWW.

[9]  Radek Pelánek,et al.  Modeling Students' Memory for Application in Adaptive Educational Systems , 2015, EDM.

[10]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[11]  Michael C. Mozer,et al.  How Deep is Knowledge Tracing? , 2016, EDM.

[12]  Elena Smirnova,et al.  Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks , 2017, DLRS@RecSys.

[13]  Kenneth R. Koedinger,et al.  Performance Factors Analysis - A New Alternative to Knowledge Tracing , 2009, AIED.

[14]  John R. Anderson,et al.  Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.

[15]  Radek Pelánek,et al.  Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques , 2017, User Modeling and User-Adapted Interaction.

[16]  Panagiotis Adamopoulos,et al.  What makes a great MOOC? An interdisciplinary analysis of student retention in online courses , 2013, ICIS.

[17]  Jussi Kasurinen,et al.  Estimating programming knowledge with Bayesian knowledge tracing , 2009, ITiCSE.

[18]  Ya'akov Gal,et al.  Sequencing educational content in classrooms using Bayesian knowledge tracing , 2016, LAK.

[19]  Jonathan P. Rowe,et al.  Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments , 2010, AIIDE.

[20]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  Clara E. Bussenius,et al.  Memory : A Contribution to Experimental Psychology , 2017 .

[22]  Enhong Chen,et al.  Exercise-Enhanced Sequential Modeling for Student Performance Prediction , 2018, AAAI.

[23]  Stefan Kopp,et al.  Adaptive Robot Language Tutoring Based on Bayesian Knowledge Tracing and Predictive Decision-Making , 2017, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.