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Reducing the Gap Between the Conceptual Models of Students and Experts Using Graph-Based Adaptive Instructional Systems

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HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games (HCII 2020)

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Abstract

Decision-making in complex environments requires a detailed understanding of causality to avoid unintended consequences and consider multiple scenarios. Concept mapping is of the key tools to support such decision-making activities. A system is abstracted as a ‘map’ or graph, in which relevant factors are represented as nodes and connected through causal links. Although studies have shown that concept maps help students recall the evidence and apply the knowledge gained to new cases, barriers such as the difficulty of assessment have prevented a wide adoption of concept maps by instructors. Previous works have partly addressed this concern by using graph algorithms to automatically assess or score a student’s map, but instructors are often more interested in correcting their students’ misconceptions than in only counting mistakes. In this paper, we design and implement a system to automatically guide students in modifying their conceptual models such that they get increasingly similar to the models produced by experts on the same problem. Although a simple automatic feedback may tell students to add/remove concepts or links based on whether they appear in the expert’s map, our approach combines expertise from systems science and participatory modeling to help students better understand why low-level changes are motivated by higher-level structures such as loops and alternative paths.

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Acknowledgment

The authors are indebted to Vishrant Gupta, who implemented the software as research assistant to PJG. The authors have reused parts of an internal report by Russell Lankenau (under the supervision of PJG) in Sects. 2.3 and 3.2. The authors thank the Department of Computer Science & Software Engineering at Miami University for supporting publication costs.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA

    Philippe J. Giabbanelli

  2. Department of Instructional Design and Technology, University of Memphis, Memphis, TN, USA

    Andrew A. Tawfik

Authors
  1. Philippe J. Giabbanelli
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  2. Andrew A. Tawfik
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Corresponding author

Correspondence to Philippe J. Giabbanelli .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece

    Constantine Stephanidis

  2. Coventry University, Coventry, UK

    Don Harris

  3. Cranfield University, Cranfield, UK

    Wen-Chin Li

  4. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  5. Design Interactive, Inc., Orlando, FL, USA

    Cali M. Fidopiastis

  6. Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  7. Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

  8. DePaul University, Chicago, IL, USA

    Xiaowen Fang

  9. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  10. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Giabbanelli, P.J., Tawfik, A.A. (2020). Reducing the Gap Between the Conceptual Models of Students and Experts Using Graph-Based Adaptive Instructional Systems. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games. HCII 2020. Lecture Notes in Computer Science(), vol 12425. Springer, Cham. https://doi.org/10.1007/978-3-030-60128-7_40

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  • DOI: https://doi.org/10.1007/978-3-030-60128-7_40

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