Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

The idea of clustering students according to their online learning behavior has the potential of providingmoreadaptivescaffoldingbytheintelligenttutoringsystemitselforbyahumanteacher. WiththeaimofidentifyingstudentgroupswhowouldbenefitfromthesameinterventioninACwareTutor, this researchexaminedonline learningbehaviorusing8 trackingvariables: the total numberofcontentpagesseeninthelearningprocess;thetotalnumberofconcepts;thetotalonline score;thetotaltimespentonline;thetotalnumberoflogins;thestereotypeaftertheinitialtest,the finalstereotype,andthemeanstereotypevariability.Thepreviousmeasureswereusedinafour-step analysisthatconsistedofdatapreprocessing,dimensionalityreduction,theclustering,andtheanalysis ofaposttestperformanceonacontentproficiencyexam.Theresultswerealsousedtoconstructthe decisiontreeinordertogetahuman-readabledescriptionofstudentclusters. KEywoRDS Blended Learning, Clustering, Decision Tree, Educational Data Mining, Flipped Classroom, Intelligent Tutoring System, Online Learning Behavior, Principal Component Analysis

[1]  Jacqueline Bourdeau,et al.  Advances in Intelligent Tutoring Systems , 2010 .

[2]  Rebecca Ferguson,et al.  Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs) , 2015, LAK.

[3]  Alberto J. Cañas,et al.  The Origins of the Concept Mapping Tool and the Continuing Evolution of the Tool* , 2006, Inf. Vis..

[4]  Rachael Knight,et al.  Encyclopedia of Distance Learning , 2005 .

[5]  Ani Grubišić,et al.  Stereotype Student Model for an Adaptive e-Learning System , 2013 .

[6]  Zachary A. Pardos,et al.  Using Fine-Grained Skill Models to Fit Student Performance with , 2006 .

[7]  Marco Temperini.  International Journal of Distance Education Technologies (IJDET) , 2016 .

[8]  Emad Abu-Shanab,et al.  Exploring Factors Affecting Users' Satisfaction Toward E-Learning Systems , 2018, Int. J. Inf. Commun. Technol. Educ..

[9]  Cristina Conati,et al.  Combining Unsupervised and Supervised Classification to Build User Models for Exploratory , 2009, EDM 2009.

[10]  Jason M. Harley,et al.  Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning , 2013, EDM 2013.

[11]  Hyacinth S. Nwana,et al.  Intelligent tutoring systems: an overview , 1990, Artificial Intelligence Review.

[12]  Ryan Shaun Joazeiro de Baker,et al.  Data-Driven Learner Profiling Based on Clustering Student Behaviors: Learning Consistency, Pace and Effort , 2018, ITS.

[13]  Bude Su.  Examining Instructional Design and Development of a Web-Based Course: A Case Study , 2005, Int. J. Distance Educ. Technol..

[14]  Susan McRoy,et al.  Links: what is an intelligent tutoring system? , 2000, INTL.

[15]  Geoffrey L. Cohen,et al.  Instructional interventions that motivate classroom learning. , 2016 .

[16]  Matthew D. Pistilli,et al.  Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.

[17]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[18]  Robin Kay.  Educational Mini-Clips in Distance Learning , 2009 .