Separated by spaces: Undergraduate students re-sort along attitude divides when choosing whether to learn in spaces designed for active learning

Abstract

Institutions of higher education are seeking to support more active learning among faculty, and that support includes the creation of active learning spaces to support more student-centered course activities. However, incremental development of these learning spaces leads to a sorting of students between active and passive learning environments. This study collected repeat-measure data from undergraduate students enrolled in a two-semester course sequence to evaluate factors affecting student enrollment decisions and student attitudes toward learning. We integrated this combination of research tools using a QuantCrit analytic framework, which revealed student attitudes and decision-making processes that were generally stable over the study period for individuals, but very different when examining student enrollment groups over time. Limited access to active learning classrooms forced students to self-sort based on either their social networks or their attitudes toward learning. That, in turn, may create a marginalizing force that pushes out some students—most often women—from undergraduate programs.

Attitudes towards science in higher education: Validation of questionnaire among science teacher candidates and engineering students in Indonesia

“…Supportive environment and cultural perspective are believed to be able to influence persons' attitude [ 56 , 57 ]. Several studies also reported that the attitudes and students’ enjoyment in learning is affected by the learning activities [ [58] , [59] , [60] ]. It implies that students with a high willingness to participate in science learning and activities are those who like science and showed a positive attitude towards science.…”

Perceptions of Gender in Classrooms and Associated Expectations of Belonging

“…With older learners, the marginalizing impacts of gendered environments have been documented in conjunction with attempts to move toward more equitable teaching practices. For example, active learning in undergraduate education has been documented for its wide variety of benefits, particularly for women (Clinton & Wilson, 2019; Freeman et al, 2014; Ralph et al, 2022). However, research has also shown how within these active learning environments there can persist gendered patterns of exclusion that continue to marginalize women (Reinholz et al, 2022) and that the environment can play a role in exerting those marginalizing pressures (Casad et al, 2019; Cheryan et al, 2013).…”

Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts

“…Until recently, most efforts to combine Active Learning (AL) [3] or comparable optimization approaches with materials simulations were confined to density functional theory (DFT) [4] calculations and attributes that could be acquired from very tiny simulation cells without noise. Although density functional theory (DFT) is generally accurate and computationally efficient, it can only be used to describe a subset of material characteristics.…”