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Learning Analytics to Assess Beliefs about Science: Evolution of Expertise as Seen through Biological Inquiry
Melanie E Peffer1,2, Niloofar Ramezani3, David Quigley2
1Institute of Cognitive Science, Molecular, Cellular, and Developmental Biology.
CBE Life Sciences Education
|September 2, 2020
Summary
Learning analytics can assess epistemological beliefs about science (EBAS) by analyzing student practices in simulations. Findings reveal differences in inquiry behaviors across student groups, highlighting the complexity of EBAS development.
Area of Science:
- Science Education
- Learning Analytics
- Cognitive Science
Background:
- Epistemological beliefs about science (EBAS) are crucial for science literacy but challenging to assess.
- Learning analytics offers a promising approach to evaluate EBAS through student engagement in authentic science simulations.
- Prior research identified distinct inquiry practices for experts and novices, suggesting a link to EBAS.
Purpose of the Study:
- To quantitatively examine differences in inquiry practices and EBAS among non-science majors, biology majors, and biology graduates.
- To extend previous qualitative findings on EBAS assessment using learning analytics.
- To investigate the relationship between student practices, EBAS, and cognitive constructs like metacognition.
Main Methods:
- Utilized learning analytics to capture and analyze student practices in simulated authentic science activities.
- Quantitatively compared inquiry behaviors and EBAS across three distinct student groups: non-science majors, biology majors, and biology graduates.
- Correlated observed practices with EBAS and considered the role of metacognition.
Main Results:
- Inquiry practices of non-science majors mirrored novice patterns, while biology graduates resembled expert patterns.
- Biology majors exhibited mixed inquiry behaviors, sometimes aligning with undergraduates and other times with graduates.
- Metacognitive processes were identified as significant factors influencing practices reflective of EBAS.
Conclusions:
- Learning analytics provides a viable method for assessing EBAS through observable student practices in digital environments.
- The study highlights variability in the development of EBAS among aspiring biologists, necessitating further investigation.
- Understanding cognitive constructs is essential for accurately interpreting learning analytics data related to EBAS.
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