Validating the model of predictors of academic self-handicapping behavior

The main aim of the present study is to validate the model of predictors of self-handicapping behavior (POASH) on the data derived from undergraduate students in an ongoing co-curriculum compulsory course. The study adapted and extended the original theory of reciprocal interaction of emotion, cognition and behavior by adding self-handicapping behavior component. In so doing, this study assessed the direct and indirect effects of emotion, cognition and behavior via student engagement on self-handicapping behavior. The second purpose of the study is to evaluate gender and nationality status invariants of the causal structure of POASH. This cross-validation procedure determined whether gender and nationality status moderated the causal structure of the model, and thus the generality of POASH. The data was collected from two self-reported questionnaires administered to 790 undergraduates of an International Islamic University in Malaysia. A confirmatory three-step approach theory testing and development using Maximum Likelihood method was applied. The results of structured equation modeling supported the adequacy of POASH and the causal structure of POASH proved to be applicable to both genders and nationality statuses

Measuring Student Engagement and Commitment on Private Academic Institutions Using Fuzzy Logic Expert System Metrics Applications

© 2020, Springer Nature Switzerland AG. Even though academic knowledge is provided to academic institutions under very specific academic standards in teaching and research, the instruction’s management can impact significantly the student engagement and commitment on receiving and utilizing such knowledge. To analyse this challenge, a Fuzzy Logic, expert system-based software application has been developed and applied on a private academic institution. In this research the institution participated with 40 undergraduate students, from 24 different countries from two different semesters on the same course. The technology measures the student engagement and commitment via the co-evolute methodology for knowledge elicitation. By utilizing this approach, the management of academic institutions can make development analysis based on concrete bottom-up results. The collective analysis of the test results clearly identifies where students see the needs for greatest development and how they view their current state of engagement