Using self-regulated learning theory and the SOLO taxonomy to evaluate an online professional development nanotechnology course
Webinar Dr. Yael Feldman-Maggor (Weizmann-Institute of Science)
Why managing AI risk presents new challenges
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The difficult of using AI to improve risk management
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How to bring AI into managing risk
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Pros and cons of using AI to manage risks
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Benefits and opportunities for risk managers applying AI
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Abstract
Professional development (PD) courses can help teachers integrate new content knowledge into their teaching curriculum. Designing online PD courses for this purpose is challenging, particularly in emerging fields such as nanotechnology. In this lecture, I present findings from an evaluation study of an online nanotechnology PD course for secondary chemistry teachers using two approaches. The first evaluation approach uses self-regulated learning (SRL) theory. SRL describes the ability of learners to act independently and actively manage their learning. This ability is essential in any learning setting, but its importance increases in online learning environments that offer greater flexibility in terms of the learning activity timing and place. While most research on SRL focuses on students, few studies address teachers’ SRL as learners, and only a handful do so in the context of online learning. This paper fills this gap and advances scholarship by examining the SRL of teachers in an online PD course. An additional evaluation tool we use is the structure of the observed learning outcome (SOLO) taxonomy which classifies learning outcomes in terms of their complexity. Our methodological approach combined qualitative and quantitative content analysis methods, case studies, and learning analytics. Three cohorts comprised of 96 teachers participated in this PD course. Using the SOLO taxonomy, we found that teachers could connect the advanced content they learned to the high-school chemistry curriculum at different levels of complexity. Analyzing teachers learning patterns using SRL theory helped us to identify five learning patterns in an online, self-paced environment, including 1) learning on track, 2) lesson skipping, 3) collaborative learning, 4) interval learning, and 5) “binging” the course. We also found that these learning patterns are strongly associated with course completion rates. Finally, we found that teachers’ previous knowledge assisted them in acquiring new scientific knowledge. Understanding teachers’ SRL is essential since they should be able to support their students’ SRL. These findings contribute to the future design of online PD science courses by explicating the intended learning outcomes as a phase in the design. In addition, the evaluation we developed can inform future teaching intervention strategies.
Dr. Yael Feldman-Maggor is a postdoctoral fellow in the Department of Science Teaching at the Weizman Institute of Science in Israel. She conducts research in the field of Computational Approaches in Science Education (CASEd).
See also: https://www.weizmann.ac.il/ScienceTeaching/research-and-development/approaches/staff
The talk will be held online via Zoom in English.
Zoom-Link: https://hu-berlin.zoom.us/j/67159030389
Meeting-ID: 671 5903 0389
24.05.2023, 15.00 - 17.00 Uhr
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