| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Using metacognitive information and objective features to predict word pair learning success |
| Authors: | Fazlija, Bledar Ibrahim, Mohamed |
| et. al: | No |
| DOI: | 10.1007/978-3-031-11647-6_39 |
| Proceedings: | Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium |
| Page(s): | 222 |
| Pages to: | 226 |
| Conference details: | 23rd International Conference on Artificial Intelligence in Education (AIED), Durham, United Kingdom, 27-31 July 2022 |
| Issue Date: | 2022 |
| Series: | Lecture Notes in Computer Science |
| Series volume: | 13356 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-031-11646-9 978-3-031-11647-6 |
| Language: | English |
| Subjects: | Metacognition; Machine learning; Transfer learning; Word difficulty; Crowed annotation; Feeling of difficulty; Judgment of learning |
| Subject (DDC): | 410.285: Computational linguistics |
| Abstract: | There is a variety of metacognitive information that can be used to model learning success, such as judgements of learning (JOLs) and feeling of difficulty (FOD). The latter is not widely used, and part of this study is to collect FODs through crowed annotation and demonstrate its potential as a predictor of learning success. While objective features related to task difficulty provide valuable information for the modeling task, we show evidence that FOD can provide similar insight. We examine and compare the use of objective and subjective features as predictors for learning success in second language word pair learning. The results indicate that metacognitive information is transferable across different groups of subjects. They also show that crowed annotation is a useful method for enriching datasets with FODs and potentially other metacognitive information. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28308 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Management and Law |
| Organisational Unit: | Institute of Wealth & Asset Management (IWA) |
| Appears in collections: | Publikationen School of Management and Law |
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Fazlija, B., & Ibrahim, M. (2022). Using metacognitive information and objective features to predict word pair learning success [Conference paper]. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 222–226. https://doi.org/10.1007/978-3-031-11647-6_39
Fazlija, B. and Ibrahim, M. (2022) ‘Using metacognitive information and objective features to predict word pair learning success’, in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. Cham: Springer, pp. 222–226. Available at: https://doi.org/10.1007/978-3-031-11647-6_39.
B. Fazlija and M. Ibrahim, “Using metacognitive information and objective features to predict word pair learning success,” in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 2022, pp. 222–226. doi: 10.1007/978-3-031-11647-6_39.
FAZLIJA, Bledar und Mohamed IBRAHIM, 2022. Using metacognitive information and objective features to predict word pair learning success. In: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. Conference paper. Cham: Springer. 2022. S. 222–226. ISBN 978-3-031-11646-9
Fazlija, Bledar, and Mohamed Ibrahim. 2022. “Using Metacognitive Information and Objective Features to Predict Word Pair Learning Success.” Conference paper. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 222–26. Cham: Springer. https://doi.org/10.1007/978-3-031-11647-6_39.
Fazlija, Bledar, and Mohamed Ibrahim. “Using Metacognitive Information and Objective Features to Predict Word Pair Learning Success.” Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, Springer, 2022, pp. 222–26, https://doi.org/10.1007/978-3-031-11647-6_39.
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