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Abstract Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.
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Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments
Strategies are an important component of self-regulated learning frameworks. However, the characterization of strategies in these frameworks is often incomplete: (1) they lack an operational definition of strategies; (2) there is limited understanding of how students develop and apply strategies; and (3) there is a dearth of systematic and generalizable approaches to measure and evaluate strategies when students’ work in open-ended learning environments (OELEs). This paper develops systematic methods for detecting, interpreting, and analyzing students’ use of strategies in OELEs, and demonstrates how students’ strategies evolve across tasks. We apply this framework in the context of tasks that students perform as they learn science topics by building conceptual and computational models in an OELE. Data from a classroom study, where sixth-grade students (N = 52) worked on science model-building activities in our Computational Thinking using Simulation and Modeling (CTSiM) environment demonstrates how we interpret students’ strategy use, and how strategy use relates to their learning performance. We also demonstrate how students’ strategies evolve as they work on multiple model-building tasks. The results demonstrate the effectiveness of our strategy framework in analyzing students’ behaviors and performance in CTSiM.
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- Award ID(s):
- 2017000
- PAR ID:
- 10298750
- Date Published:
- Journal Name:
- International Journal of Artificial Intelligence in Education
- ISSN:
- 1560-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s40593-021-00275-x
