An enhanced Bayesian model to detect students' learning styles in Web-based courses.
Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; st...
| Publicado en: | Journal of Computer Assisted Learning Vol. 24; no. 4; pp. 305 - 316 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105344557&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105344557 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2008 vid: 24 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105344557 105344557 2010457787 10.1111/j.1365-2729.2007.00262.x 105344557 ppf: 305 ppct: 11 formats: tig: atl: An enhanced Bayesian model to detect students' learning styles in Web-based courses. aug: au: García P Schiaffino S Amandi A sug: subj: Education, Non-Traditional Internet Learning Methods Evaluation Models, Statistical Colleges and Universities Comparative Studies Learning Methods Classification Personality Assessment Methods Personality Classification Questionnaires Students, College Psychosocial Factors Validity Human ab: Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In a previous work, we have presented an approach that uses Bayesian networks to detect a student's learning style in Web-based courses. In this work, we present an enhanced Bayesian model designed after the analysis of the results obtained when evaluating the approach in the context of an Artificial Intelligence course. We evaluated the precision of our Bayesian approach to infer students' learning styles from the observation of their actions with a Web-based education system during three semesters. We show how the results from one semester enabled us to adjust our initial model and helped teachers improve the content of the course for the following semester, enhancing in this way students' learning process. We obtained higher precision values when inferring the learning styles with the enhanced model. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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