通过 Apriori 算法和深度神经网络的结合预测大学专业选择和学习成绩

IF 4.8 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Education and Information Technologies Pub Date : 2024-09-05 DOI:10.1007/s10639-024-13022-1

Kheira Ouassif, Benameur Ziani

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引用次数: 0

摘要

将教育数据挖掘与深度神经网络相结合，并采用 Apriori 算法生成关联规则，重点是解决大学中学生的错误导向问题，导致他们失败和辍学。为此，我们开发了一个智能模型，根据每个学生的学术背景、偏好和技能，为他们预测正确的学习路径。我们观察到，社会经济和家庭背景特征对学生的成绩没有影响。这也是本研究论文所包含的内容。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Predicting university major selection and academic performance through the combination of Apriori algorithm and deep neural network

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Predicting university major selection and academic performance through the combination of Apriori algorithm and deep neural network

The integration of educational data mining and deep neural networks, along with the adoption of the Apriori algorithm for generating association rules, focuses to resolve the problem of misdirection of students in the university, leading to their failure and dropout. This is reached through the development of an intelligent model that predicts the right path for each student based on their academic background, preferences and skills. While we observed no impact of the Socio-Economic and Family Background features on the students’ performance. And this is what was included in this research paper.

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来源期刊

Education and Information Technologies EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

10.00

自引率

12.70%

发文量

610

期刊介绍： The Journal of Education and Information Technologies (EAIT) is a platform for the range of debates and issues in the field of Computing Education as well as the many uses of information and communication technology (ICT) across many educational subjects and sectors. It probes the use of computing to improve education and learning in a variety of settings, platforms and environments. The journal aims to provide perspectives at all levels, from the micro level of specific pedagogical approaches in Computing Education and applications or instances of use in classrooms, to macro concerns of national policies and major projects; from pre-school classes to adults in tertiary institutions; from teachers and administrators to researchers and designers; from institutions to online and lifelong learning. The journal is embedded in the research and practice of professionals within the contemporary global context and its breadth and scope encourage debate on fundamental issues at all levels and from different research paradigms and learning theories. The journal does not proselytize on behalf of the technologies (whether they be mobile, desktop, interactive, virtual, games-based or learning management systems) but rather provokes debate on all the complex relationships within and between computing and education, whether they are in informal or formal settings. It probes state of the art technologies in Computing Education and it also considers the design and evaluation of digital educational artefacts. The journal aims to maintain and expand its international standing by careful selection on merit of the papers submitted, thus providing a credible ongoing forum for debate and scholarly discourse. Special Issues are occasionally published to cover particular issues in depth. EAIT invites readers to submit papers that draw inferences, probe theory and create new knowledge that informs practice, policy and scholarship. Readers are also invited to comment and reflect upon the argument and opinions published. EAIT is the official journal of the Technical Committee on Education of the International Federation for Information Processing (IFIP) in partnership with UNESCO.