MxML (Exploring the Relationship between Measurement and Machine Learning): Survey of the Measurement Community

The field of educational measurement (hereafter shortened as measurement) has seen rapid growth in applications of machine learning (ML) recently. However, it is imperative to examine potential gaps between ML and the fundamental principles of measurement. The MxML project seeks to shed light on how to close the gaps between the two to harness the power of ML to serve measurement practices. Phase 1 of the project was a systematic review of the recent 10 years of measurement literature, in which we provided a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles, (3) ML methods discussed, and (4) potential gaps between measurement and ML. This paper reports the findings from Phase 2 of the project, a survey of the international measurement community to understand measurement professionals’ experiences, attitudes, and thoughts toward incorporating ML techniques into measurement.