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Forecasting Academic Outcomes of Students Based on the Fusion of Educational and Social Data via Data Mining: A Case Study of High School Students in Mazandaran Province
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Mohammad Amiri *   |
| Technical and Voctional University |
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Abstract: (278 Views) |
| Educational data mining is an emerging scientific field that focuses on developing methods to explore and discover the unique data of educational environments. These methods are applied to gain a better understanding of students and the educational contexts in which teaching and learning take place. In this project, based on data mining algorithms, students were classified into five groups: those with good progress, normal progress, no progress or decline, mild decline, and severe decline. After grouping the students, using the analysis of questionnaires distributed among a sample of them, the causes of their academic progress or decline were extracted through artificial intelligence algorithms. The statistical population of this study consists of high school students in Mazandaran Province. The data relate to the grades of high school students in theoretical fields from 2016 to 2020, collected from 33 educational districts of Mazandaran Province. A total of 420 samples were gathered for qualitative data. The factor of family social cohesion had the greatest impact on academic progress, or lack thereof. The family environment and psychological well-being ranked next. Parental cooperation during financial difficulties (a sub-component of social capital), along with levels of anxiety, stress, and vitality in life (components of individual psychological well-being), were identified as the most significant sub-components among the eight main factors influencing academic achievement. The results obtained from this method were compared with conventional methods of evaluating academic progress, showing both similarities and differences. |
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| Keywords: Educational data mining, Academic achievement, Educational and social data. |
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Type of Study: Research |
Received: 1400/01/09 | Accepted: 1400/01/09 | Published: 1400/01/09
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