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Cognitive Feedback and Behavioral Feedforward Automation Perspectives for Modeling and Validation in a Learning Context

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Model-Driven Engineering and Software Development (MODELSWARD 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 692))

Abstract

State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavioral observations by exploiting different traces of learning processes stored in information systems. Such learner behavior data makes it possible to observe different aspects of learning processes in which feedback needs of learners (e.g. difficulties, engagement issues, inefficient learning processes, etc.) based on individual learning trajectories can be traced. By identifying problems earlier in a learning process it is possible to deliver individualized feedback helping learners to take control of their own learning, i.e. to become self-regulated learners, and teachers to understand individual feedback needs and/or adapt their teaching strategies. In this work we (i) propose cognitive computer-assisted feedback mechanisms using a combination of MDE based simulation augmented with automated feedback, and (ii) discuss perspectives for behavioral feedback, i.e. feedforward, that can be based on learning process analytics in the context of learning conceptual modeling. Aggregated results of our previous studies assessing the effectiveness of the proposed cognitive feedback method with respect to improved understanding on different dimensions of knowledge, as well as feasibility of behavioral feedforward automation based on learners behavior patterns, are presented. Despite our focus on conceptual modeling and specific diagrams, the principles of the approach presented in this work can be used to support educational feedback automation for a broader spectrum of diagram types beyond the scope of conceptual modeling.

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Authors and Affiliations

  1. Department of Decision Sciences and Information Management, Research Center for Management Informatics, KU Leuven, Leuven, Belgium

    Gayane Sedrakyan & Monique Snoeck

Authors
  1. Gayane Sedrakyan
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  2. Monique Snoeck
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Correspondence to Gayane Sedrakyan .

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Editors and Affiliations

  1. Université d’Angers/ESEO, Angers, France

    Slimane Hammoudi

  2. Faculty of EEMCS, University of Twente, Enschede, The Netherlands

    Luís Ferreira Pires

  3. Malina Software Corp., Nepean, Ontario, Canada

    Bran Selic

  4. SOFTEAM, Paris, France

    Philippe Desfray

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Sedrakyan, G., Snoeck, M. (2017). Cognitive Feedback and Behavioral Feedforward Automation Perspectives for Modeling and Validation in a Learning Context. In: Hammoudi, S., Pires, L., Selic, B., Desfray, P. (eds) Model-Driven Engineering and Software Development. MODELSWARD 2016. Communications in Computer and Information Science, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-319-66302-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-66302-9_4

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