Advancing automatic guidance in virtual science inquiry: from ease of use to personalization

This article is in response to the review entitled Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review (Zacharia et al. in Educ Technol Res Dev 63(2): 257-302). As COVID-19 alerts us to shift science education to digital when in-person schooling is not viable, one approach to facilitate this shift, as reviewed by Zacharia et al. (2015), is to involve students in computer supported inquiry learning (CoSIL) with appropriate guidance. As CoSIL guidance is critical to student success in CoSIL, Zacharia et al. (2015) contribute to our knowledge by systematically reviewing the forms and the efficacy of such guidance tools that are associated with each phase of scientific inquiry. With such knowledge we may develop decent guidance so that students can experience scientific inquiry virtually as they used to do in-person. Zacharia et al. (2015) indicated that the various guidance tools had increased the ease of use of CoSIL but failed in personalizing CoSIL to individual students. I agree with Zacharia et al. that the personalization of CoSIL guidance is vital. Further, I argue that the emergent machine learning may significantly increase the personalization of CoSIL without burdening teachers. I conclude the essay with suggestions to further investigate the cognitive needs of students in CoSIL and integrate the content, CoSIL, and guidance tools, as a way to move forward the personalization of CoSIL.

Automated summary

The article responds to a review on different types of guidance for supporting student inquiry in virtual and remote labs, emphasizing the importance of personalized guidance in computer-supported inquiry learning. It suggests that machine learning could enhance personalization without burdening teachers.