Automatic Personality Identification Using Students' Online Learning Behavior

Autor/inn/en	Lai, Song; Sun, Bo; Wu, Fati; Xiao, Rong
Titel	Automatic Personality Identification Using Students' Online Learning Behavior
Quelle	In: IEEE Transactions on Learning Technologies, 13 (2020) 1, S.26-37 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lai, Song) ORCID (Sun, Bo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2019.2924223
Schlagwörter	Personality Traits; Electronic Learning; Individual Differences; Learning Processes; Role; Computer Software; Student Behavior; Extraversion Introversion; High School Seniors; Accuracy; Identification; Classification; Behavior Patterns; Teaching Methods; Telecommunications; Handheld Devices; Personality Measures; NEO Personality Inventory + Suchen Sie Ihr Suchwort? Individual characteristics; Personality characteristic; Persönlichkeitsmerkmal; Individueller Unterschied; Learning process; Lernprozess; Rollen; Student behaviour; Schülerverhalten; Identifikation; Identifizierung; Classification system; Klassifikation; Klassifikationssystem; Teaching method; Lehrmethode; Unterrichtsmethode; Telekommunikationstechnik
Abstract	Adaptive e-learning can be used to personalize learning environment for students to meet their individual demands. Individual differences depend on the students' personality traits. Numerous studies have indicated that understanding the role of personality in the learning process can facilitate learning. Hence, personality identification in e-learning is a critical issue in education. In this study, we propose the enhanced extended nearest neighbor (EENN) algorithm to automatically identify two of the Big Five personality traits from students' behavior in online learning: openness to experience and extraversion. The performance of the proposed method is evaluated using a fivefold cross-validation approach on data from 662 senior high school students. The experimental results indicate that the EENN method can automatically recognize personality at an average accuracy of 0.758. The optimized method that combines EENN with particle swarm optimization significantly improves the identification, resulting in an average accuracy of 0.976. The results can benefit students by increasing the accuracy of personalization based on their personality traits, while simultaneously allowing them to be better understood and possibly allowing their instructors to provide more appropriate learning interventions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01