Using an Institutional Research Perspective to Predict Undergraduate Students' Career Decisions in the Practice of Precision Education

Tzu-Chi Yang, Yih-Lan Liu, Li-Chun Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The recently increased importance of practicing precision education has attracted much attention. To better understand students' learning and the relationship between their individual differences and learning outcomes, the bird-eye view possible for educational policymakers and stakeholders from educational data mining and institutional research has gained importance and momentum. The deployment of specific predictive tasks based on institutional data is the most promising solution for dealing with a variety of issues on precision education. Most research in this field is focused on learning performance and related issues, such as at-risk students and drop-out tendencies. Seldom are the relationships between the learning performance and career decisions of students investigated. However, developing a deep understanding of students' career decisions plays an important role in the practice of precision education. In this vein, this paper provides a comprehensive analysis and comparison of the state of the art of predictive techniques for providing a prediction for students' career decisions. The results indicated that it is possible to perform early detection of students' career decisions. The contributions of this study are discussed in terms of their implications for theory, methodology, and application.

Original languageEnglish
Pages (from-to)280-296
Number of pages17
JournalEducational Technology and Society
Volume24
Issue number1
StatePublished - Jan 2021

Keywords

  • Educational data mining
  • Institutional Research
  • Machine learning
  • Precision education
  • Students’ life planning

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