The use of data science for education: The case of social-emotional learning

• Published in: Smart learning environments Vol. 4; no. 1; pp. 1 - 13
• Main Authors: Liu, Ming-Chi, Huang, Yueh-Min
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 13.01.2017; Springer Nature B.V
• Subjects: Academic Achievement; Academic Standards; Case Studies; Cognitive Ability; College Science; Computers and Education; Data Analysis; Data mining; Data science; Decision Making; Discovery Processes; Education; Educational Assessment; Educational Practices; Educational Trends; Evaluative Thinking; Influence of Technology; Literature Reviews; National Curriculum; Outcome Measures; Physical Health; Research Methodology; Review; School Policy; Self Control; Self Efficacy; Social Emotional Learning; Social Problems; Standardized Tests; Statistical Data; Data science; Education; Social-emotional learning
• ISSN: 2196-7091; 2196-7091
• DOI: 10.1186/s40561-016-0040-4

The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.