Proceedings of The 6th International Conference on Future of Teaching and Education
Technology and Business Students’ L2l Expect Fixed Mindsets on Cognitive Computing and Performance
The study reports technology and business undergraduates (n = 76) Learning-to-Learn (L2L) traits using case-specific Barometric (L2LB). The study evaluates the L2LB model’s reliability and validity and discusses results. L2L is a bit like a tide that rises and falls along the sea’s shoreline when ascending, reaching, and wiping the cliff, while descending the rock avoids from water-wear remaining intact-untouched. A strong L2L, like a tidal wave, promotes achievement-engagement in periodic-liveliness. i.e., the potential is to touch the cliff to capture evidence to address features that facilitate daily-water-like learning flow, carving the stone with new memories from which the research questions (RQs) were founded. The RQs are at what level, association, and context of the L2LB-core characters relate with existing validated indices. L2LB’s Endogenous Latent Variables (ELVs) tested construct and composite validity. The Factor Analysis with Principal Component Analysis showed miserable Kaiser-Meyer-Olkin without altering the ELVs-structures. Weaknesses indicated discoveries for further analysis being visible in the cross-correlation (R-structure), in which each Average Variance Extracted (AVE) surpasses comparable values for some R-loadings. RQ1 resolution examined descriptives for L2L-domain in Cognitive Computing & Performance-related Mastery Beliefs (MBs) and Detrimental Beliefs (DBs), leaving RQ2 on the one hand. The comparison showed R (>=.3**) for DBs and (>=.2**) L2L-sum, leaving connections attractive. On the other hand, RQ3 addresses the AVE-thematics by squaring R-loadings. The study reveals weak L2L’s ELVs, which AVE was (<.5*/**). The ELVs were finally elaborated for further research and practice recommendations for the cause-and-effect bias. Stand mastery-avoidance achievement goals forever contaminated?
keywords: Case Studies, Quantitative methods, Student Learning, Learning Approaches, Artificial Intelligence, Achievement, Attitudes and Beliefs, Competencies, Learning, and Developmental Difficulties/Learning Disabilities, Literacy, Self-efficacy, Technology, Social Sciences, Higher Education, Lifelong Learning, Assessment and Evaluation, Cognitive Science, Higher Education.