NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1138744
Record Type: Journal
Publication Date: 2017-Jun
Pages: 27
Abstractor: As Provided
Reference Count: 76
ISBN: N/A
ISSN: ISSN-1560-4292
Computing of Learner's Personality Traits Based on Digital Annotations
Omheni, Nizar; Kalboussi, Anis; Mazhoud, Omar; Kacem, Ahmed Hadj
International Journal of Artificial Intelligence in Education, v27 n2 p241-267 Jun 2017
Researchers in education are interested in modeling of learner's profile and adapt their learning experiences accordingly. When learners read and interact with their reading materials, they do unconscious practices like annotations which may be, a key feature of their personalities. Annotation activity requires readers to be active, to think critically and to analyze what has been drawn up, and to make explicit annotations in the margins of the text. Readers make annotation traces through underlining, highlighting, scribbling comments, summarizing, asking questions, expressing confusion or ambiguity, and evaluating the reading content. In this paper, we present a semi-automatic approach to building learners' personality profiles based on their annotation traces yielded during an active reading session. The experimental results show the system's efficiency to measure, with reasonable accuracy, the scores of a learner's conscientiousness and neurotics traits.
Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A