Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/120152| Title: | Predicting student achievement through peer network analysis for timely personalization via generative AI |
| Author(s): | Pesovski, Ivica Jolakoski, Petar Trajkovik, Vladimir Kubincova, Zusana Herzog, Michael A. |
| Issue Date: | 2025-05-30 |
| Type: | Artikel |
| Language: | English |
| Publisher: | Elsevier, Amsterdam |
| Subjects: | Personalized learning Peer nomination Student network centrality AI for learning |
| Abstract: | Peer influence is a significant determinant in shaping students' academic performance, yet it is often overlooked in traditional educational strategies. The ability to analyze peer influence and collaboration is an important piece in personalizing student educational experiences. |
| URI: | https://opendata.uni-halle.de//handle/1981185920/122111 http://dx.doi.org/10.25673/120152 |
| Open Access: |  Open access publication |
| License: |  (CC BY-NC-ND 4.0) Creative Commons Attribution NonCommercial NoDerivatives 4.0 |
| Sponsor/Funder: | DEAL Elsevier |
| Appears in Collections: | Fachbereich Wirtschaft |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S2666920X25000700-main.pdf | Zweitveröffentlichung | 1.9 MB | Adobe PDF |  View/Open |