Literaturnachweis - Detailanzeige
| Autor/inn/en | Cui, Ying; Guo, Qi; Leighton, Jacqueline P.; Chu, Man-Wai |
|---|---|
| Titel | Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach |
| Quelle | In: International Journal of Testing, 20 (2020) 1, S.78-96 (19 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Zusatzinformation | ORCID (Leighton, Jacqueline P.) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1530-5058 |
| DOI | 10.1080/15305058.2018.1551225 |
| Schlagwörter | Inferences; Artificial Intelligence; Data Analysis; Computer Assisted Testing; Problem Solving; Student Reaction; Science Tests; Simulation; Foreign Countries; Grade 8; Junior High School Students; Task Analysis; Canada |
| Abstract | This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research. (As Provided). |
| Anmerkungen | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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