An Engagement-Aware Predictive Model to Evaluate Problem-Solving Performance From Process Data

• In Event: Individual Differences in Technology and Learning

Abstract

his study introduces a novel approach to predictive modeling of the examinee’s performance in sequential, interactive problem-solving tasks. The current methods disambiguate problem-solving behaviors using network analysis to inform the examinee's performance in a series of problem-solving tasks. The unique contribution of this framework lies in the introduction of an “effort-aware” system. The system considers the information regarding the examinee’s task-engagement level to accurately predict their task performance. The study demonstrates the potential to introduce a high-performing deep learning model to learning analytics and examinee performance modeling in a large-scale problem-solving task environment collected from the OECD Programme for the International Assessment of Adult Competencies (PIAAC) test in multiple countries.

Authors

• Jinnie Shin, University of Florida
  Presenting Author

• Wallace N. Pinto, University of Florida
  Non-Presenting Author

• Bowen Wang, University of Florida
  Non-Presenting Author

• Mark J. Gierl, University of Alberta
  Non-Presenting Author