Emphasis on Emotions in Student Learning: Analyzing Relationships between Overexcitabilities and the Learning Approach Using Bayesian MIMIC Modeling

Autor/inn/en	De Bondt, Niki; Van Petegem, Peter
Titel	Emphasis on Emotions in Student Learning: Analyzing Relationships between Overexcitabilities and the Learning Approach Using Bayesian MIMIC Modeling
Quelle	In: High Ability Studies, 28 (2017) 2, S.225-248 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (De Bondt, Niki) ORCID (Van Petegem, Peter)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1359-8139
DOI	10.1080/13598139.2017.1292897
Schlagwörter	Psychological Patterns; Structural Equation Models; Bayesian Statistics; College Students; Personality Development; Foreign Countries; Learning; Validity; Maximum Likelihood Statistics; Surveys; Statistical Analysis; Belgium + Suchen Sie Ihr Suchwort? Collegestudent; Personalilty development; Persönlichkeitsbildung; Persönlichkeitsentwicklung; Ausland; Lernen; Gültigkeit; Survey; Umfrage; Befragung; Statistische Analyse; Belgien
Abstract	The aim of this study is to investigate interrelationships between overexcitability and learning patterns from the perspective of personality development according to Dabrowski's theory of positive disintegration. To this end, Bayesian structural equation modeling (BSEM) is applied which allows for the simultaneous inclusion in the measurement model of all, approximate zero cross-loadings and residual covariances based on zero-mean, small-variance priors, and represents substantive theory better. Our BSEM analysis with a sample of 516 students in higher education yields positive results regarding the validity of the model, in contrast to a frequentist approach to validation, and reveals that overexcitability--the degree and nature of which is characteristic of the potential for advanced personality development, according to Dabrowski's theory--is substantially related to the way in which information is processed, as well as to the regulation strategies that are used for this purpose and to study motivation. Overexcitability is able to explain variations in learning patterns to varying degrees, ranging from weakly (3.3% for reproduction-directed learning for the female group) to rather strongly (46.1% for meaning-directed learning for males), with intellectual overexcitability representing the strongest indicator of deep learning. This study further argues for the relevance of including emotion dynamics--taking into account their multilevelness--in the study of the learning process. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01