Chasing Two Hares at Once?: Innovating the Classroom with Datafied Performance

• In Event: E/valuative Actions: Exploring the Doings of E/valuation in Evaluative Systems 1

Abstract

Datafication challenges to the long standing trade-off between the scale and efficiency in education. Education technology (EdTech) companies claim that datafication will benefit both students and teachers as data-based personalized or adaptive learning can take care of each student’s need and reduce the teachers’ workload, thereby improving the quality of education in classrooms of all sizes. Elice, an EdTech startup in South Korea for web-based computer programming platform, shares such vision and aims to provide more accessible and efficient education. Utilizing personal learning data, the platform develops machine learning algorithm that produces a score which indicates the individual student’s performance.
Some underestimated conflicts over datafication appear when performance becomes an issue of class, rather than that of individual student. In this study, I will unpack the meaning of “performance” in the context of class management by observing how the data practice reshapes the online classes operated in Elice. Evaluation of performance is usually a social problem which requires consensus between appraiser and appraisee, but the datafied performance excludes human intervention on purpose. While engineers are mostly concerned with the type of data (not) collected, modified, and controlled to make algorithm reflect actual performance of each student as much as possible, both teachers and students are expected to form a new rules and relationship at the online class where they are represented only the datafied performance. I will show that, although datafication dreams of innovating classroom, it finally points up traditional dichotomy between scale and efficiency by provoking strategic choices.

Author

• Hanbyul Jeong, KAIST