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This chapter focuses on accessible active learning (AL) strategies that promote equitable and effective student-centered instruction for higher education. Although there is not a consensus definition of AL across disciplines, principles of AL include attention to student engagement with content, peer-to-peer interactions, instructor uses of student thinking, and instructor attention to equity. A variety of AL strategies vary in complexity, time, and resources, and instructors can build up repertoires of such teaching practices. The field needs cultural change that moves away from lecture and toward AL and student engagement as the norm for equitable and effective teaching. Although such cultural change needs to include instructor professional learning about AL strategies, it also needs attention to collective beliefs, power dynamics, and structures that support (or inhibit) equitable AL implementation. This chapter provides frameworks for sustainable change to using AL in higher education, as well as research-based findings around which AL strategies are easy on-ramps for novice instructors. This chapter also provides a few specific examples of structures that support AL—course coordination and peer mentoring—and provides questions one may pose in attempting to spur cultural change that centers AL. 

Background and Context: Professional development (PD) programs for K-12 computer science teachers use surveys to measure teachers’ knowledge and attitudes while recognizing daily sentiment and emotion changes can be crucial for providing timely teacher support. Objective: We investigate approaches to compute sentiment and emotion scores automatically and identify associations between the scores and teachers’ performance. Method: We compute the scores from teachers’ assignments using a machine-assisted tool and measure score changes with standard deviation and linear regression slopes. Further, we compare the scores to teachers’ performance and post-PD qualitative survey results. Findings: We find significant associations between teachers’ sentiment and emotion scores and their performance across demographics. Additionally, we find significant associations that are not captured by post-PD qualitative surveys. Implications: The sentiment and emotion scores can viably reflect teachers’ performance and enrich our understanding of teachers’ learning behaviors. Further, the sentiment and emotion scores can complement conventional surveys with additional insights related to teachers’ learning performance. 

This study provides practical suggestions for the features to be prioritized in spending limited resources to create and improve educational technology like Cell Collective. The results suggest a need to prioritize features improving the learning rather than the teaching side to motivate instructors more effectively to adopt and use the technology.