TY - CHAP
T1 - Open research and observational study for 21st Century Learning
AU - Kumar, Vivekanandan S.
AU - Fraser, Shawn
AU - Boulanger, David
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data imbalance
KW - Interactive analysis
KW - Learning analytics
KW - Matching in smart learning environments
KW - Observational study
KW - Persistent observational study
KW - Propensity score matching
KW - Randomized experiment
UR - http://www.scopus.com/inward/record.url?scp=85043786534&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-8743-1_17
DO - 10.1007/978-981-10-8743-1_17
M3 - Chapter
AN - SCOPUS:85043786534
T3 - Lecture Notes in Educational Technology
SP - 121
EP - 126
BT - Lecture Notes in Educational Technology
ER -