Early Detection of Metacognition Disparity Using a Fuzzy-Logic Based Model

This research to practice work-in-progress discusses how engineering educators continue to look for methods to improve students’ success by improving the learning process. Numerous studies have shown that students’ metacognition provides a reliable indicator of students’ learning effectiveness. Moreover, results have shown a direct proportionality between improved students’ metacognition and improved academic achievement. Therefore, efforts have been reported in the literature aiming at measuring metacognition and employing the results to improve outcomes of the educational experience. However, methods to measure metacognitive abilities and the process to employ relevant interventions aiming at improving the learning process, are still lengthy and cumbersome. This study was initiated to explore building a predictive model that could help expedite the process of identifying students with metacognitive disparities and the corresponding proper interventions needed to improve their learning at early stages of an educational course. The study is divided into two phases where the first phase, reported in this paper, includes exploring metacognition measurement and characterization methods to develop a model that can identify metacognition levels ordinally which can also be used to show changes in metacognition. The second phase includes expanding the first model to identify a targeted intervention with optimized details in order to improve students’ level of learning and performance. For this purpose, concepts of fuzzy set theory were utilized to build the models. The first model produced a Metacognition Fuzzy Indicator (MFI) which identified students’ metacognition levels based on performance assessment data collected early in the semester. A combination of direct and indirect assessment methods of students’ attainment of course learning outcomes in four engineering courses were collected, - before and after interventions, and employed to build and test the model. Results of utilizing the model have shown consistent agreement between MFIs and students’ performance improvement after implementing a variety of interventions in all tested courses. A second model was developed as an expansion of the existing model to start the second phase of this study and provide details of the recommended interventions per student by producing an Intervention Fuzzy Indicator (IFE). Future work will include providing results aiming at optimizing time and effort invested by the instructors to improve students’ learning while increasing students’ motivation and success through increased personalized educational interventions.