Abstract
This chapter discusses four measurement challenges of data science in educational assessments that are enabled by technology: (1) Dealing with change over time. (2) How a digital performance space’s relationships interact with learner actions, communications and products. (3) How layers of interpretation are formed from translations of atomistic data into meaningful larger units suitable for making inferences about what someone knows and can do. (4) How to represent the dynamics of interactions between and among learners who are being assessed by their interactions with each other as well as with digital resources and agents in digital performance spaces. Because of the movement from paper-based tests to online learning, and in order to make progress on these challenges, the authors advocate the restructuring of training of the next generation of researchers and psychometricians in technology-enabled assessments.
| Original language | English |
|---|---|
| Title of host publication | Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment |
| Publisher | Springer International Publishing |
| Pages | 19-33 |
| Number of pages | 15 |
| ISBN (Electronic) | 9783030151300 |
| ISBN (Print) | 9783030151294 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
Keywords
- Data science
- Educational assessment
- Educational measurement
- Learning analytics
- New psychometrics