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1. Reducing Student Resistance to Active Learning: Applying Research Results to Faculty Development

   https://doi.org/10.18260/1-2--35130  

   Marlor, L. K. ; Finelli, C. J. ; Anderson, M. ; Bermudez, B. ; Borrego, M. ; Carroll, L. ; Derosia, N. ; Graham, M. ; Husman, J. ; Prince, M. ( October 2020 , ASEE Annual Conference proceedings)

   Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors’ adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors’ beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors’ motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments.
2. Reducing Student Resistance to Active Learning: Applying Research Results to Faculty Development

   Marlor, L. K. ; Finelli, C. J. ; Andrews, M. ; Bermudez, B. ; Borrego, M. J. ; Carroll, L. ; DeRosia, N. M. ; Graham, M.C. ; Husman, J. ; Prince, M. J. ( January 2020 , Proceedings of the 2020 Annual ASEE Conference)

   Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors;' adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors' beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors' motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments.
3. Reducing Student Resistance to Active Learning: Applying Research Results to Faculty Development

   Marlor, L. K. ; Finelli, C. J. ; Anderson, M. ; Bermudez, B. ; Borrego, M. ; Carroll, L. ; Derosia, N. ; Graham, M. ; Husman, J. ; Prince, M. ( June 2020 , ASEE Annual Conference proceedings)

   Despite many studies confirming that active learning in STEM classrooms improves student outcomes, instructors’ adoption of active learning has been surprisingly slow. This work-in-progress paper describes our broader research study in which we compare the efficacy of a traditional active learning workshop (AL) and an extended version of this workshop that also specifically highlights instructor strategies to reduce resistance (AL+) on instructors’ beliefs about and actual adoption of active learning in undergraduate STEM classrooms. Through a randomized control trial (RCT), we aim to understand the ways in which these workshops influence instructors’ motivation to adopt and the actual use of active learning. This RCT involves instructors and students at a large number of institutions including two-year college, four-year college, and large research institutions in three regions of the country and strategies to reduce student resistance to active learning. We have developed and piloted three instruments, which allow for triangulation of classroom data: an instructor survey, a student survey, and a classroom observation protocol. This work-in-progress paper will cover the current progress of our research study and present our research instruments.
4. WIP: Using Machine Learning to Automate Coding of Student Explanations to Challenging Mechanics Concept Questions

   Harpreet Auby ; Namrata Shivagunde ; Anna Rumshisky ; Milo Koretsky ( June 2022 , ASEE Annual Conference proceedings)

   This work-in-progress paper describes a collaborative effort between engineering education and machine learning researchers to automate analysis of written responses to conceptually challenging questions in mechanics. These qualitative questions are often used in large STEM classes to support active learning pedagogies; they require minimum calculations and focus on the application of underlying physical phenomena to various situations. Active learning pedagogies using this type of questions has been demonstrated to increase student achievement (Freeman et al., 2014; Hake, 1998) and engagement (Deslauriers, et al., 2011) of all students (Haak et al., 2011). To emphasize reasoning and sense-making, we use the Concept Warehouse (Koretsky et al., 2014), an audience response system where students provide written justifications to concept questions. Written justifications better prepare students for discussions with peers and in the whole class and can also improve students’ answer choices (Koretsky et al., 2016a, 2016b). In addition to their use as a tool to foster learning, written explanations can also provide valuable information to concurrently assess that learning (Koretsky and Magana, 2019). However, in practice, there has been limited deployment of written justifications with concept questions, in part, because they provide a daunting amount of information for instructors to process and formore »researchers to analyze. In this study, we describe the initial evaluation of large pre-trained generative sequence-to-sequence language models (Raffel et al., 2019; Brown et al., 2020) to automate the laborious coding process of student written responses. Adaptation of machine learning algorithms in this context is challenging since each question targets specific concepts which elicit their own unique reasoning processes. This exploratory project seeks to utilize responses collected through the Concept Warehouse to identify viable strategies for adapting machine learning to support instructors and researchers in identifying salient aspects of student thinking and understanding with these conceptually challenging questions.« less
5. The role of leadership in educational innovation: a comparison of two mathematics departments’ initiation, implementation, and sustainment of active learning

   https://doi.org/10.1007/s43545-022-00565-8  

   Funk, Rachel ; Uhing, Karina ; Williams, Molly ; Smith, Wendy M. ( November 2022 , SN Social Sciences)

   Several studies have shown that the use of active learning strategies can help improve student success and persistence in STEM-related fields. Despite this, widespread adoption of active learning strategies is not yet a reality as institutional change can be difficult to enact. Accordingly, it is important to understand how departments in institutions of higher education can initiate and sustain meaningful change. We use interview data collected from two institutions to examine how leaders at two universities contributed to the initiation, implementation, and sustainability of active learning in undergraduate calculus and precalculus courses. At each institution, we spoke to 27 stakeholders involved in changes (including administrators, department chairs, course coordinators, instructors, and students). Our results show that the success of these changes rested on the ability of leaders to stimulate significant cultural shifts within the mathematics department. We use communities of transformation theory and the four-frame model of organization change in STEM departments in order to better understand how leaders enabled such cultural shifts. Our study highlights actions leaders may take to support efforts at improving education by normalizing the use of active learning strategies and provides potential reasons for the efficacy of such actions. These results underscore the importancemore »of establishing flexible, distributed leadership models that attend to the cultural and operational norms of a department. Such results may inform leaders at other institutions looking to improve education in their STEM departments.

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