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Diversity, Equity, and Inclusion (DEI) has become an increasingly important topic in mathematics education and in the professional development (PD) of Mathematics Graduate Teaching Assistants (MGTAs). Currently, little is known about the types of initiatives that are effective in supporting MGTAs’ use of equitable and inclusive teaching practices. Based on data from 45 MGTAs enrolled in our DEI-focused PD program, we propose a preliminary framework to elucidate the characteristics of activities that support MGTAs’ engagement in DEI issues. We illustrate the utility of the framework by highlighting examples of activities in alignment with the framework, as well as the memorable impact of these activities on the MGTAs who completed coursework in our PD program. 

As part of an overall research program investigating the impact of changes in teaching strategies on students' engineering social cognitions (self-efficacy and outcome expectations), this paper investigates students' confidence in their ability to learn and their instructor's ability to teach across 6 engineering courses. A group of 6 faculty formed a learning community focused on improved teaching strategies for their classes. The faculty chose selected strategies and implemented them in their classes. Surveys asked students to rank their confidence level in "their ability to learn" the specific class material and the instructor's "ability to teach" the class material using a sliding bar scale from 0-100. Surveys were conducted before and after the improvements to the teaching strategies at both the beginning and end of the semesters. The results of the surveys are compared before and after the teaching improvements, beginning to end of semester, per course, online to in-person, and per gender. In summary, the study found that while there was no significant difference in the control group, a decrease in students’ confidence to learn and in their confidence in their instructors’ ability to teach was observed in the treatment group. This decrease was observed in specific courses that changed instructional modes due to Covid. Despite teaching improvements, students’ confidence decreased as they moved through the course material. Further research is needed to explore these findings and their implications for teaching strategies. 

We propose a structured extension to bidirectional-context conditional language generation, or “infilling,” inspired by Frame Semantic theory (Fillmore, 1976). Guidance is provided through two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo