Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Xing:2015:CHB,
  author =       "Wanli Xing and Rui Guo and Eva Petakovic and 
                 Sean Goggins",
  title =        "Participation-based student final performance
                 prediction model through interpretable Genetic
                 Programming: Integrating learning analytics,
                 educational data mining and theory",
  journal =      "Computers in Human Behavior",
  volume =       "47",
  pages =        "168--181",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming, Learning
                 analytics, Educational data mining, Prediction, CSCL,
                 Activity theory",
  ISSN =         "0747-5632",
  DOI =          "doi:10.1016/j.chb.2014.09.034",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0747563214004865",
  size =         "14 pages",
  abstract =     "Building a student performance prediction model that
                 is both practical and understandable for users is a
                 challenging task fraught with confounding factors to
                 collect and measure. Most current prediction models are
                 difficult for teachers to interpret. This poses
                 significant problems for model use (e.g. personalising
                 education and intervention) as well as model
                 evaluation. In this paper, we synthesise learning
                 analytics approaches, educational data mining (EDM) and
                 HCI theory to explore the development of more usable
                 prediction models and prediction model representations
                 using data from a collaborative geometry problem
                 solving environment: Virtual Math Teams with Geogebra
                 (VMTwG). First, based on theory proposed by Hrastinski
                 (2009) establishing online learning as online
                 participation, we activity theory to holistically
                 quantify students' participation in the CSCL
                 (Computer-supported Collaborative Learning) course. As
                 a result, 6 variables, Subject, Rules, Tools, Division
                 of Labour, Community, and Object, are constructed. This
                 analysis of variables prior to the application of a
                 model distinguishes our approach from prior approaches
                 (feature selection, Ad-hoc guesswork etc.). The
                 approach described diminishes data dimensionality and
                 systematically contextualises data in a semantic
                 background. Secondly, an advanced modelling technique,
                 Genetic Programming (GP), underlies the developed
                 prediction model. We demonstrate how connecting the
                 structure of VMTwG trace data to a theoretical
                 framework and processing that data using the GP
                 algorithmic approach outperforms traditional models in
                 prediction rate and interpretability. Theoretical and
                 practical implications are then discussed.",
  notes =        "Learning Analytics, Educational Data Mining and
                 data-driven Educational Decision Making",
}

Genetic Programming entries for Wanli Xing Rui Guo Eva Petakovic Sean Goggins

Citations