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Abstract Computational analysis methods and machine learning techniques introduce innovative ways to capture classroom interactions and display data on analytics dashboards. Automated classroom analytics employ advanced data analysis, providing educators with comprehensive insights into student participation, engagement, and behavioral trends within classroom settings. Through the provision of context-sensitive feedback, automated classroom analytics systems can be integrated into the evidence-based pedagogical decision-making and reflective practice processes of faculty members in higher education institutions. This paper presents TEACHActive, an automated classroom analytics system, by detailing its design and implementation. It outlines the processes of stakeholder engagement and mapping, elucidates the benefits and obstacles associated with a comprehensive classroom analytics system design, and concludes by discussing significant implications. These implications propose user-centric design approaches for higher education researchers and practitioners to consider. 

There is a growing interest in the research and use of automated feedback dashboards that display classroom analytics; yet little is known about the detailed processes instructors use to make sense of these tools, and to determine the impact on their teaching practices. This research was conducted at a public Midwestern university within the context of an automated classroom observation and feedback implementation project. Fifteen engineering instructors engaged in this research. The overarching goal was to investigate instructor teaching beliefs, pedagogical practices, and sensemaking processes regarding dashboard use. A grounded theory approach was used to identify categories related to instructor perceptions. Results revealed that instructor experiences inform both their present use of the dashboard and consequential future actions. A model is presented that illustrates categories included in instructor pre-use, use, and post-use of an automated feedback dashboard. An extension to this model is presented and accompanied by recommendations for a more effective future use of automated dashboards. The model’s practical implications inform both instructors and designers on effective design and use of dashboards, ultimately paving a way to improve pedagogical practices and instruction 

The ultimate goal of using learning analytics dashboards is to improve teaching and learning processes. Instructors that use an analytics dashboard are presented with data about their students and/or about their teaching practices. Despite growing research in analytics dashboards, little is known about how instructors make sense of the data they receive and reflect on it. Moreover, there is limited evidence on how instructors who use these dashboards take further actions and improve their pedagogical practices. My dissertation work addresses these issues by examining instructors’ sense making, reflective practice and subsequent actions taken from classroom analytics in three phases: (a) problem analysis from systematic literature review (current), (b) implementation and examination of instructors’ sense-making and reflective practice (current) and (c) human-centered approaches to co-designing instructors’ dashboards with stakeholders (current). The findings will contribute to the conceptual basis of instructors’ change of their pedagogical practices and practical implications of human-centered principles in designing effective dashboards. 

Faculty professional development is known to be a key factor contributing to the effective implementation of evidence-based teaching in STEM classrooms. In this research, we developed TEACHActive, an innovative classroom analytics-driven professional development model that supports the reflective practices of engineering instructors in higher education. TEACHActive uses machine learning techniques within a camera-based classroom sensing system that tracks behavioral features of interest in classrooms. Following design-based implementation research, we rapidly enacted, tested, and revised the TEACHActive model with engineering instructors. This study reports the results of the first iteration completed in the spring semester of 2021. Specifically, we examined the TEACHActive implementation and deployment in engineering classrooms with the analysis of instructors’ perceived successes and challenges. The paper presents implications for using the classroom analytics-driven professional development with educators in higher education.