Technology and Business Students’ L2l Expect Fixed Mindsets on Cognitive Computing and Performance

Proceedings of The 6th International Conference on Future of Teaching and Education

Year: 2022



Technology and Business Students’ L2l Expect Fixed Mindsets on Cognitive Computing and Performance

Janne Heilala



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.