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Role of absence in academic success: an analysis using visualization tools

Authors
  • Etemadpour, Ronak1
  • Zhu, Yongcheng1
  • Zhao, Qizhi1
  • Hu, Yilun1
  • Chen, Bohan1
  • Sharier, Mohammed Asif1
  • Zheng, Shirong1
  • S. Paiva, Jose Gustavo2
  • 1 Computer Science, City College of New York, CUNY, 160 Convent Avenue, New York, 10031, USA , New York (United States)
  • 2 Faculty of Computing, Federal University of Uberlandia, UFU, Av. Joao Naves de Avila, 2121, Uberlandia, 38408100, Brazil , Uberlandia (Brazil)
Type
Published Article
Journal
Smart Learning Environments
Publisher
Springer Singapore
Publication Date
Jan 07, 2020
Volume
7
Issue
1
Identifiers
DOI: 10.1186/s40561-019-0112-3
Source
Springer Nature
Keywords
License
Green

Abstract

Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students’ grades/attendances in each course may represent important indicators to measure their performance, among other tasks. The use of data visualization and analytics is expanding in education institutions to perform a variety of tasks related to data processing and gaining into data-informed insights. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students’ data from program courses. Two educational data collections were used to guide the creation of i) predictive models employing a variety of well known machine learning strategies, attempting to predict students’ future grade based on grade and attendance previous semesters and ii) a set interactive layouts that highlight the relationship between grades and attendance, also including additional variables such as gender, parents education level, among others. We performed several experiments, also using these data collections, to evaluate the layouts ability of highlighting interesting patterns, and we obtained promising results, demonstrating that such analysis may help the education experts to understand deficiencies on course structures.

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