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1. Where do I Start?

   https://doi.org/10.13023/8QHC-3S89  

   Crystelle, Andrea; Schultz, Lori (April 2024, University of Kentucky Libraries)

   In this session, we will start at the beginning and talk about what you need to think about in setting up research analytics capabilities for your organization. There are so many options for tools and other resources to get started, but what if you only have Excel? How do you get reporting off the ground when it hasn’t been done (or done consistently) at your workplace? We’ll talk about ways to get started, including who you need to talk with, what you need to consider, and how to move forward with basic reporting. Presented at the 2024 Research Analytics Summit in Albuquerque NM 

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2. OPRA: An Open-Source Online Preference Reporting and Aggregation System

   https://doi.org/10.1609/aaai.v35i18.17996  

   Chen, Yiwei; Qian, Jingwen; Wang, Junming; Xia, Lirong; Zahavi, Gavriel (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   We introduce the Online Preference Reporting and Aggregation (OPRA) system, an open-source online system that aims at providing support for group decision-making. We illustrate OPRA's distinctive features: UI for reporting rankings with ties, comprehensive analytics of preferences, and group decision-making in combinatorial domains. We also discuss our work in an automatic mentor matching system. We hope that the open-source nature of OPRA will foster development of computerized group decision support systems. 

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3. Students' Experiences of Unfairness in Graduate Engineering Education

   Bahnson, M.; Satterfield, D.; Wyer, M.; Kirn, A. (January 2021, The Collaborative Network for Engineering and Computing Diversity)

   null (Ed.)

   A pilot inventory to develop measures of bias and discrimination experienced by engineering doctoral students asked if they have been treated unfairly by their primary advisor, secondary advisor, and other faculty. Analyses of pilot data (n = 250) revealed Women, Students of Color, and sexual minorities perceived experiences of unfair treatment in intricate patterns. Post hoc analyses show that Women experience more incidences of unfair treatment than men. Race/ethnicity identity groups report a different number of unfair treatment incidences, with Students of Color generally reporting more experiences than white students. Being a sexual minority contributed to reporting more incidences of unfair treatment. Unfair treatment from faculty significantly predicted students changing and considering changing research labs when controlling for gender, race/ethnicity, and sexuality. Unfair treatment from faculty significantly impacted engineering identity when controlling for gender, race/ethnicity, sexuality, lab changers, and change considerers. Analyses of pilot data demonstrated the negative impact of unfair treatment on students and their development as engineers. 

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4. Exploring college faculty development in 21 ^st -century skill instruction: an analysis of teaching-focused personal networks

   https://doi.org/10.1080/0309877X.2020.1826032  

   Benbow, Ross J; Lee, Changhee; Hora, Matthew T (July 2021, Journal of Further and Higher Education)

   While educators and policymakers increasingly link the ‘21st-century’ skills of communication, teamwork, problem-solving, and self-directed learning to graduate success in important high-technology industries, few studies look at how technological college faculty – who are expected to help instil these important skills in students – learn to better teach such skills. Faculty development research shows that feedback- and reflection-oriented social learning improves instruction, but has not typically investigated the full scope of beneficial teaching-focused interactions, formal and informal, in which faculty engage. Using a social network perspective, which focuses on the empirical contours of relationships across settings, this mixed methods study explores (1) the people with whom technological faculty discuss teaching, referred to as ‘teaching-focused personal networks;’ (2) the comparative contours of these networks by faculty development involvement, teaching experience, institution type, and discipline; and (3) how, if at all, faculty believe these networks influence their communication, teamwork, problem-solving, and self-directed learning instruction. Survey data (n = 192) indicate that most respondents discuss teaching with a core personal network of about four contacts, commonly institutional colleagues, around once a month. Data also show that network size, diversity, and strength – measures connected to actionable, relationship-based information and support, or ‘social capital’ – are broadly similar among faculty of varying subgroups, with one exception: respondents reporting involvement in in-depth faculty development programming have larger and stronger networks. Qualitative results show that most faculty reporting teaching-focused personal networks perceived them to benefit their teaching of communication, teamwork, problem-solving, or self-directed learning through support, reflection, feedback, and sharing new ideas. 

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5. Design and Implementation of an Automated Classroom Analytics System: Stakeholder Engagement and Mapping

   https://doi.org/10.1007/s11528-023-00905-2  

   Baran, Evrim; AlZoubi, Dana; Morales, Anasilvia Salazar (November 2023, TechTrends)

   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. 

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