Visualizing Historical Patterns in Large Educational Datasets
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| Title: | Visualizing Historical Patterns in Large Educational Datasets |
|---|---|
| Authors: | Tiago Martins, Daniel Gonçalves, Sandra Gama |
| Source: | International Journal of Creative Interfaces and Computer Graphics. 9:32-48 |
| Publisher Information: | IGI Global, 2018. |
| Publication Year: | 2018 |
| Subject Terms: | 03 medical and health sciences, 0302 clinical medicine, 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology |
| Description: | With the increase in the number of students worldwide, it has become difficult for teachers to track their students or even for institutions themselves to identify anomalies in degrees and courses. The sheer amount of data makes such an analysis a daunting task. A possible solution to overcome this problem is the use of interactive information visualization. In this article, the authors developed a visualization that allows users to explore and analyze large datasets of academic performance data allowing the analysis and discovery of temporal evolution patterns for courses, degrees and professors. The authors applied the techniques to fourteen years of data for all students, courses, and degrees of a Portuguese engineering college. The system's usability and usefulness were tested, confirming its ability to allow analysts to efficiently and effectively understand patterns in the data. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1947-3125 1947-3117 |
| DOI: | 10.4018/ijcicg.2018010103 |
| Access URL: | https://www.igi-global.com/article/visualizing-historical-patterns-in-large-educational-datasets/210549 https://dblp.uni-trier.de/db/journals/ijcicg/ijcicg9.html#MartinsGG18 |
| Accession Number: | edsair.doi.dedup.....18bb9a84563a0f7587aa75023f518cea |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | With the increase in the number of students worldwide, it has become difficult for teachers to track their students or even for institutions themselves to identify anomalies in degrees and courses. The sheer amount of data makes such an analysis a daunting task. A possible solution to overcome this problem is the use of interactive information visualization. In this article, the authors developed a visualization that allows users to explore and analyze large datasets of academic performance data allowing the analysis and discovery of temporal evolution patterns for courses, degrees and professors. The authors applied the techniques to fourteen years of data for all students, courses, and degrees of a Portuguese engineering college. The system's usability and usefulness were tested, confirming its ability to allow analysts to efficiently and effectively understand patterns in the data. |
|---|---|
| ISSN: | 19473125 19473117 |
| DOI: | 10.4018/ijcicg.2018010103 |