Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments.
Abstract: This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 4; pp. 358 - 366 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2018
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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=130898729&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130898729 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2018 vid: 34 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 130898729 130898729 130898729 10.1111/jcal.12247 130898729 ppf: 358 ppct: 8 formats: tig: atl: Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments. aug: au: Yu, L. C. Lee, C. W. Pan, H. I. Chou, C. Y. Chao, P. Y. Chen, Z. H. Tseng, S. F. Chan, C. L. Lai, K. R. affil: Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan sug: subj: Academic Failure Taiwan Emotions Forecasting Students, College Taiwan Human Taiwan Psychological Tests Academic Performance Experimental Studies Computer Graphics Funding Source Sentiment Analysis ab: Abstract: This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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