Hybrid Analysis of the Learner's Online Behavior Based on Learning Style

Autor/inn/en	Balti, Rihab; Hedhili, Aroua; Chaari, Wided Lejouad; Abed, Mourad
Titel	Hybrid Analysis of the Learner's Online Behavior Based on Learning Style
Quelle	In: Education and Information Technologies, 28 (2023) 10, S.12465-12504 (40 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Balti, Rihab)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11595-x
Schlagwörter	Cognitive Style; Pandemics; COVID-19; Distance Education; Academic Achievement; Learning Experience; Student Attitudes; Learning Theories; Preferences; Classification; Student Behavior; Behavior Patterns; Online Courses; Correlation; Universities; Interpersonal Relationship; College Students + Suchen Sie Ihr Suchwort? Cognitive styles; Kognitiver Stil; Distance study; Distance learning; Fernunterricht; Schulleistung; Lernerfahrung; Schülerverhalten; Learning theory; Lerntheorie; Classification system; Klassifikation; Klassifikationssystem; Student behaviour; Online course; Online-Kurs; Korrelation; University; Universität; Interpersonal relation; Interpersonal relations; Interpersonelle Beziehung; Zwischenmenschliche Beziehung; Collegestudent
Abstract	Since the COVID pandemic, universities propose online education to ensure learning continuity. However, the insufficient preparation led to a major drop in the learner's performance and his/her dissatisfaction with the learning experience. This may be due to several reasons, including the insensitivity of the virtual learning environment to the learner's preferences. We propose to address the issue of student's dissatisfaction and lack of interaction, by integrating learning style theory into the analysis of the learner's online behavior. Our work differentiates itself from the rest of researches that employed learning style theory by its two step process. First, we classify the learning activities into learning categories based on learning style theory. Second, we define behavioral features that quantify the learner's behavior across the learning categories. The analysis of the learner's online behavior based on the behavioral features revealed new aspects of the learner's preferences. We consider these findings to be best useful for developing learning style-sensitive adaptive learning environments. Nevertheless, the behavioral features could be beneficial in different contexts. In fact, when applied to course outcome prediction, the behavioral features enhanced the results by 10%. The latter indicates that behavioral features reflected the correlation between behavior and academic performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01