Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms
Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students’ low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-06-01
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| Series: | Research on Education and Media |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/rem-2023-0014 |
| _version_ | 1811171361972813824 |
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| author | Zanellati Andrea Macauda Anita Panciroli Chiara Gabbrielli Maurizio |
| author_facet | Zanellati Andrea Macauda Anita Panciroli Chiara Gabbrielli Maurizio |
| author_sort | Zanellati Andrea |
| collection | DOAJ |
| description | Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students’ low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students’ learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students’ learning encoding, which is transferable to different students’ cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education. |
| first_indexed | 2024-04-10T17:12:43Z |
| format | Article |
| id | doaj.art-4535dc18e0d2418b9bff17186c51bf6b |
| institution | Directory Open Access Journal |
| issn | 2037-0849 |
| language | English |
| last_indexed | 2024-04-10T17:12:43Z |
| publishDate | 2023-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Research on Education and Media |
| spelling | doaj.art-4535dc18e0d2418b9bff17186c51bf6b2023-02-05T19:46:25ZengSciendoResearch on Education and Media2037-08492023-06-0115110311010.2478/rem-2023-0014Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithmsZanellati Andrea0Macauda Anita1Panciroli Chiara2Gabbrielli Maurizio3University of Bologna, ItalyUniversity of Bologna, ItalyUniversity of Bologna, ItalyUniversity of Bologna, ItalyWithin scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students’ low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students’ learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students’ learning encoding, which is transferable to different students’ cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education.https://doi.org/10.2478/rem-2023-0014representation of learningpost-digitallow achievementartificial intelligence algorithmspredictive model |
| spellingShingle | Zanellati Andrea Macauda Anita Panciroli Chiara Gabbrielli Maurizio Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms Research on Education and Media representation of learning post-digital low achievement artificial intelligence algorithms predictive model |
| title | Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms |
| title_full | Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms |
| title_fullStr | Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms |
| title_full_unstemmed | Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms |
| title_short | Representation of learning in the post-digital: students’ dropout predictive models with artificial intelligence algorithms |
| title_sort | representation of learning in the post digital students dropout predictive models with artificial intelligence algorithms |
| topic | representation of learning post-digital low achievement artificial intelligence algorithms predictive model |
| url | https://doi.org/10.2478/rem-2023-0014 |
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