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Systematic Evaluation of the Effectiveness of TREs through Software Platform Development for Data Mining across Multiple Disciplines and Tracking Changes in Affective and Cognitive Growths



  • Eunice Jang (U. of Toronto, Theme 3)
  • Rafael Calvo (Sydney U., Theme 2)
  • Susanne P. Lajoie (McGill U., Theme 1)
  • Roger Azevedo (North Carolina State U., Theme 1)

Research Assistants:

  • Amanda Jarrell (McGill U.)
  • Lila Lee (McGill U.)


One of the key premises underlying the LEADS research program is that technology-rich learning environments can provide a means to innovating instructional design, contributing to promoting learners across different age spans to be cognitively competent, emotionally engaging, and self-regulating, all crucial. This is an ambitious one which requires a well-conceived theoretical framework that can articulate ecological relationships among human traits, digital mechanisms, and learning environments afforded by technology. Evaluating the viability of the theoretical framework requires sound empirical evidence from triangulated data sources over time. We believe that traditional inquiry approaches are limiting for taking advantages of the data minefield that TREs afford us with. Complex measurement variables from TREs interventions encourage us to attend to new ways of assessing, tracking, and evaluating affective and cognitive changes attributable to TREs’ interventions. For example, systematic data mining across themes in LEADS can help identify key learner/measurement variables (not only explicit but also implicit and covert), which can serve as covariates in longitudinal tracking of changes in emotional, affective/metacognitive, and cognitive growths among distinct latent classes of learners.

The purposes of this proposed collaborative research are:

  • to develop an evaluation framework that can be used for evaluating the effectiveness of TREs interventions, specifically BioWorld and Metatutor, across themes/disciplines;
  • to identify key covariate variables for modelling longitudinal growths in latent class profiles;
  • to evaluate the effectiveness of the TREs on improving the 21st skill competencies (i.e., problem solving, communication, self-regulated learning);

to investigate factors that affect the effectiveness of feedback in order to optimize the TREs feedback system for maximal learning experience;to identify best practices in methods of providing diagnostic feedback to students.