Literaturnachweis - Detailanzeige
| Autor/inn/en | Kaburlasos, Vassilis G.; Marinagi, Catherine C.; Tsoukalas, Vassilis Th. |
|---|---|
| Titel | Personalized Multi-Student Improvement Based on Bayesian Cybernetics |
| Quelle | In: Computers & Education, 51 (2008) 4, S.1430-1449 (20 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0360-1315 |
| DOI | 10.1016/j.compedu.2008.01.004 |
| Schlagwörter | Feedback (Response); Student Improvement; Computer Science; Bayesian Statistics; Cybernetics; Item Banks; Adaptive Testing; Computer Assisted Testing; Educational Technology; Computer Software; Undergraduate Students; College Instruction; Intelligent Tutoring Systems; Instructional Design; Computer Assisted Instruction |
| Abstract | This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely "Module for Adaptive Assessment of Students" (or, "MAAS" for short), implements the proposed (feedback) techniques. In conclusion, a pilot application to two Computer Science courses during a period of 4 years demonstrates the effectiveness of the proposed techniques. Statistical evidence strongly suggests that the proposed techniques can improve student performance. The benefits of automating a quicker delivery of University quality education to a large body of students can be substantial as discussed here. (Contains 4 tables and 7 figures.) (As Provided). |
| Anmerkungen | Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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