Applying classification techniques on temporal trace data for shaping student behavior models

Differences in learners' behavior have a deep impact on their educational performance. Consequently, there is a need to detect and identify these differences and build suitable learner models accordingly. In this paper, we report on the results from an alternative approach for dynamic student behavioral modeling based on the analysis of time-based student-generated trace data. The goal was to unobtrusively classify students according to their time-spent behavior. We applied 5 different supervised learning classification algorithms on these data, using as target values (class labels) the students' performance score classes during a Computer-Based Assessment (CBA) process, and compared the obtained results. The proposed approach has been explored in a study with 259 undergraduate university participant students. The analysis of the findings revealed that a) the low misclassification rates are indicative of the accuracy of the applied method and b) the ensemble learning (treeBagger) method provides better classification results compared to the others. These preliminary results are encouraging, indicating that a time-spent driven description of the students' behavior could have an added value towards dynamically reshaping the respective models.

[1]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[2]  Riichiro Mizoguchi,et al.  Improving Students' Meta-cognitive Skills within Intelligent Educational Systems: A Review , 2011, HCI.

[3]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[4]  John Self.  Bypassing the intractable problem of student modelling , 1988 .

[5]  Anuj Karpatne,et al.  Introduction to Data Mining (2nd Edition) , 2018 .

[6]  Kenneth R. Koedinger,et al.  A Response Time Model For Bottom-Out Hints as Worked Examples , 2008, EDM.

[7]  Anastasios A. Economides,et al.  Adaptive context-aware pervasive and ubiquitous learning , 2009 .

[8]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[9]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[10]  Dirk T. Tempelaar,et al.  In search for the most informative data for feedback generation: Learning analytics in a data-rich context , 2015, Comput. Hum. Behav..

[11]  Alejandro Peña-Ayala.  Review: Educational data mining: A survey and a data mining-based analysis of recent works , 2014 .

[12]  Anastasios A. Economides,et al.  A temporal estimation of students' on-task mental effort and its effect on students' performance during computer based testing , 2015, 2015 International Conference on Interactive Collaborative Learning (ICL).

[13]  Alejandro Peña Ayala,et al.  Educational data mining: A survey and a data mining-based analysis of recent works , 2014, Expert Syst. Appl..

[14]  George Veletsianos,et al.  Digging deeper into learners' experiences in MOOCs: Participation in social networks outside of MOOCs, notetaking and contexts surrounding content consumption , 2015, Br. J. Educ. Technol..

[15]  Panagiotis Germanakos,et al.  A Personalization Method Based on Human Factors for Improving Usability of User Authentication Tasks , 2014, UMAP.

[16]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[17]  Anastasios A. Economides,et al.  Temporal learning analytics for computer based testing , 2014, LAK.

[18]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[19]  Anastasios A. Economides,et al.  The acceptance and use of computer based assessment , 2011, Comput. Educ..

[20]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[21]  Sidney K. D'Mello,et al.  Toward Fully Automated Person-Independent Detection of Mind Wandering , 2014, UMAP.

[22]  Marek Hatala,et al.  Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions , 2015, Internet High. Educ..

[23]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[24]  Antonija Mitrovic,et al.  Evaluating the Effects of Open Student Models on Learning , 2002, AH.

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Miguel Ángel Conde González,et al.  Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning , 2014, Comput. Hum. Behav..

[27]  Jukka Huhtamäki,et al.  Exploring co-learning behavior of conference participants with visual network analysis of Twitter data , 2015, Comput. Hum. Behav..

[28]  Judy Kay,et al.  Modelling Long Term Goals , 2014, UMAP.

[29]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[30]  Gordon I. McCalla,et al.  The Central Importance of Student Modelling to Intelligent Tutoring , 1992 .

[31]  Anouschka van Leeuwen,et al.  Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics , 2015, Comput. Educ..

[32]  Antonija Mitrovic,et al.  Towards a negotiable student model for constraint-based ITSs , 2009 .

[33]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[34]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[35]  Anastasios A. Economides,et al.  STUDENTS' PERCEPTION OF PERFORMANCE VS. ACTUAL PERFORMANCE DURING COMPUTER BASED TESTING: A TEMPORAL APPROACH , 2014 .