A Comprehensive Digital Solution for Identifying and Addressing Academic Risk in Middle Education
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 15347 LNCS, Page: 529-540
2025
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Smart schooling seeks to enhance the educational experience through technology. In this effort, a digital educational platform has been developed and empirically tested to identify students at risk of academic failure and dropout, while also promoting effective study and learning habits. Machine learning algorithms are employed to assess academic failure risk based on students’ responses to a questionnaire created by educational psychologists. The platform predicts students’ academic performance by analyzing factors related to their school and home environments, as well as their motivation to learn. Additionally, a decision support system is integrated to alert the class director and recommend preventive actions when risks or unusual behaviors are identified. A decision support system is incorporated to alert the class director and suggest preventive measures after risk or unusual behaviour is detected.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85210871890&origin=inward; http://dx.doi.org/10.1007/978-3-031-77738-7_45; https://link.springer.com/10.1007/978-3-031-77738-7_45; https://dx.doi.org/10.1007/978-3-031-77738-7_45; https://link.springer.com/chapter/10.1007/978-3-031-77738-7_45
Springer Science and Business Media LLC
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