Identifying relevant user behavior, predicting learning, and persistence in an ITS-based afterschool program

Scotty Craig, Xudong Huang, Jun Xie, Ying Fang, Xiangen Hu

Research output: Contribution to conferencePaper

Abstract

ALEKS (Assessment and Learning in Knowledge Spaces) has recently shown promise for effectively training mathematics at equivalent levels to human teachers. However, not much is known about how the system accomplished this. In this paper, we describe the use of three data mining techniques used to analyze student data from an afterschool program with ALEKS. Our first analysis used DMM modeling and k-clustering to identify important groups of behaviors within ALEKS users and to show the importance of context for elements. Our second analysis focused on identifying learner behaviors that predict student learning during the program. The final analysis presents a method for determine learner persistence within the afterschool program.

Original languageEnglish (US)
Pages581-582
Number of pages2
StatePublished - Jan 1 2016
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States
Duration: Jun 29 2016Jul 2 2016

Conference

Conference9th International Conference on Educational Data Mining, EDM 2016
CountryUnited States
CityRaleigh
Period6/29/167/2/16

Keywords

  • Afterschool programs
  • ALEKS
  • Help seeking
  • Learning strategies
  • Persistence

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Craig, S., Huang, X., Xie, J., Fang, Y., & Hu, X. (2016). Identifying relevant user behavior, predicting learning, and persistence in an ITS-based afterschool program. 581-582. Paper presented at 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, United States.