Open Research and Observational Study for 21st Century Learning
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Contemporary research practice unreasonably obscures formative research outcomes from public notice. Indeed, this exclusion – often unintentional – holds true even when the research is publicly funded. Accordingly, the Public must search scholarly channels, such as academic journals, for research information that is not composed for general comprehension. Essentially, a breach in information transmission separates researchers and society at large. In education, a similar communication gap exists between students and instructors, given that instructors rely on traditional assessment activities to measure student performance and rarely realize the corresponding study efforts. Consequently, certain important formative evidences go largely unnoticed. Today, researchers are exploring smart learning processes that exploit opportunities triggered by environmental affordance, personal need, and/or professional expectation, and mitigate various assessment difficulties.
This presentation introduces Open Research in the context of Smart Learning. First, it discusses the advantages of opening the research process to an authorized public, fellow students, educators and policymakers. For example, it argues that greater accessibility can promote research growth and integrity. Second, it uses observational study methods to illustrate the ways students and educators can conduct their own experiments using continuously arriving data. This second section introduces three matching techniques (i.e. Coarsened Exact Matching, Mahalanobis Distance Matching, and Propensity Score Matching) and three data imbalance metrics (i.e. L1 vector norm, Average Mahalanobis Imbalance, and Difference in Means) to assess the level of data imbalance within matched sample datasets. Ultimately, the presentation promotes Smart Learning Environments that incorporate automated tools for opportunistic capture, analysis and remediation of various formative study processes. Such environments can enable students to ethically share and receive study data that help them conduct personal observational studies on individual study related questions. Moreover, it explains key traits of observational studies that are relevant for smart learning environments, considering the comparable traits of blocked randomized experiments. Remarkably, this presentation proposes a novel idea to connect Open Research with Persistent Observational Study methods. It explores how open research can support adaptive and self-regulated learning. It advocates for innovative research practices that can produce better and smarter learning.
Keywordsmatching in smart learning environments propensity score matching randomized experiment interactive analysis observational study learning analytics data imbalance persistent observational study
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