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The key innovation of this model is that it uses long short-term memory networks to capture inherent time-series features for each type of behavior, and it takes two-dimensional convolutional networks to extract correlation features among different behaviors. We conducted experiments with four types of daily behavior data from students of the university in Beijing. The experimental results demonstrate that the proposed deep model method outperforms several machine learning algorithms.<\/jats:p>","DOI":"10.1007\/s40747-022-00731-8","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T07:42:41Z","timestamp":1651822961000},"page":"5143-5156","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Student achievement prediction using deep neural network from multi-source campus data"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8450-625X","authenticated-orcid":false,"given":"Xiaoyong","family":"Li","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huimin","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Mengran","family":"Li","sequence":"additional","affiliation":[]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"issue":"9","key":"731_CR1","doi-asserted-by":"publisher","first-page":"454","DOI":"10.20344\/amp.9996","volume":"31","author":"JMD Sousa","year":"2018","unstructured":"Sousa JMD, Moreira CA (2018) Anxiety, depression and academic performance: a study amongst Portuguese medical students versus non-medical students. 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