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We use qualitative methods to investigate students’ engagement in an upper-division laboratory. Laboratory activities are recognized as key curricular elements in engineering education. These activities have traditionally been delivered in person, but new laboratory modalities (such as virtual laboratories) have been gaining popularity, boosted by the COVID-19 pandemic. Understanding how laboratory modality influences student learning is important to be able to design and implement effective laboratories. While some educators have investigated if virtual laboratories can replace their analogous physical laboratory counterparts, others have looked at using virtual laboratories in combination with physical laboratories. Taking this latter approach, they argue the two modes have different affordances and therefore could be complementary - meaning that each mode may lend itself to more effectively engaging students in certain productive practices. We have previously reported on the development of two environmental engineering laboratories, one physical and one virtual. Both laboratories address the topic of jar testing, an important process in drinking water treatment, with the design of each mode being based on that mode's affordances. These laboratories were implemented in an upper-level chemical engineering course. Twelve students split into four groups consented to be audio and video recorded during their time in the laboratory and have the work they turn in collected, with most also volunteering to be interviewed about their experiences. A first pass of this data has been completed in which we viewed learning from the lens of participation in disciplinary practice. We applied the theory of engineering epistemic practices, which are the socially organized and interactionally accomplished ways engineers develop, justify, and communicate ideas when completing engineering work. Transcripts of the laboratory observations were coded to identify students’ engagement with specific epistemic practices, which were categorized as either conceptual, material, or social. These codes were then counted and cross-validated with interview responses to draw conclusions about how student's engagement differed in each mode. This prior research has indicated that students engage with each design using different epistemic practices. While the first pass analysis showed differences in counts of epistemic practices between modes, it provided limited insight into how and why the epistemic practices are elicited and coordinated among students. In this paper, we extend the discourse analysis by illustrating our developing methodology for a second pass analysis of the video recordings. We seek to develop a thick description by identifying how particular epistemic practices fit together temporally and serve to promote or hinder students’ progress. Engagement in epistemic practices does not happen in a vacuum and instead happens contextually, influenced by students' previous engagement and the laboratory environment and their social and academic history. This analysis allows a deeper understanding of how students engage in engineering practice while completing laboratories, knowledge that can be applied to enhance engineering physical and virtual laboratory instruction and design. Additionally, this work contributes to the methodological conversation of ways to use interaction analyses to extract understanding from a rich set of qualitative data.
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Markov analysis of students’ professional skills in virtual internships
In this paper, we conduct a Markov analysis of learners’ professional skill development based on their conversations in virtual internships, an emerging category of learning systems characterized by the epistemic frame theory. This theory claims that professionals develop epistemic frames, or the network of skills, knowledge, identity, values, and epistemology (SKIVE) that are unique to that profession. Our goal here is to model individual students’ development of epistemic frames as Markov processes and infer the stationary distribution of this process, i.e. of the SKIVE elements. Our analysis of a dataset from the engineering virtual internship Nephrotex showed that domain specific SKIVE elements have higher probability. Furthermore, while comparing the SKIVE stationary distributions of pairs of individual students and display the results as heat maps, we can identify students that play leadership or coordinator roles.
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- Award ID(s):
- 1418288
- PAR ID:
- 10026172
- Date Published:
- Journal Name:
- Proceedings of the 30th Florida Artificial Intelligence Research Society Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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