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1. Designing and implementing an automated grading workflow for providing personalized feedback to open-ended data science assignments

   https://doi.org/10.5070/T5.1886  

   Ricci, Federica Zoe; Medina, Catalina Mari; Dogucu, Mine (January 2024, Technology Innovations in Statistics Education)

   Open-ended assignments - such as lab reports and semester-long projects - provide data science and statistics students with opportunities for developing communication, critical thinking, and creativity skills. However, providing grades and qualitative feedback to open-ended assignments can be very time consuming and difficult to do consistently across students. In this paper, we discuss the steps of a typical grading workflow and highlight which steps can be automated in an approach that we define as an automated grading workflow. We illustrate how gradetools, a new R package, implements this approach within RStudio to facilitate efficient and consistent grading while providing individualized feedback. We hope that this work will help the community of data science and statistics educators use gradetools as their grading workflow assistant or develop their own tools for assisting their grading workflow. 

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2. Computational tools' mediation of argumentation in undergraduate probability and statistics.

   Foster, M; Reed, ML; Cammarota, C; Zwickl, B; Wong, T (March 2025, The Special Interest Group of the Mathematical Association of America (SIGMAA) for Research in Undergraduate Mathematics Education)

   As computation becomes increasingly central to mathematics education, instructors must balance competing forces when choosing which computational tools to use in their courses. This is compounded in probability and statistics where computation is widely used. Grounded in a social constructivist perspective, we believe that tools mediate our activities and that different tools play different mediational roles. As such, this study explores how different computational tools mediate undergraduate students' mathematical activity of argumentation. Using Toulmin’s argument model, this research investigates how two classes in probability and statistics using different computational tools, R or Minitab, performed on a mirrored assignment. Through analysis of students’ assignments, a difference emerged across the classes use of visuals. Our findings suggest Minitab promoted more deliberate consideration and use of visuals than R, leading to a difference in arguments produced by the students. 

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3. Students’ perceptions of their professors’ integration of computation in their probability and statistics courses.

   Foster, M; Mitelman, A; Cammarota, C; Bogstad, B; Zwickl, B; Wong, T (March 2025, Proceedings of the 27th Annual Conference on Research in Undergraduate Mathematics Education)

   Technology has become an integral part of undergraduate mathematics, particularly the use of technology to solve problems (i.e., the use of computation). In probability and statistics, this push has resulted in several projects designing and assessing tools that are conjectured to be advantageous to students and their learning. Despite this trend, minimal research exists on how students perceive the use of computational tools in their courses. As such, we designed a brief survey for students enrolled in introductory probability and statistics at a university in the Northeastern United States. Using thematic analysis, we qualitatively analyzed these survey responses to explore their perceptions of the integration of computation into their courses. Three themes were identified, relating to features of tools, augmentation of actions, and long-term benefits. This exploration of students’ perceptions allows us to better understand their views on computation and the need for professors to make instructional goals explicit. 

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4. Workshops and Co-design Can Help Teachers Integrate Computational Thinking into Their K-12 STEM Classes

   Wu, S.; Peel, A.; Bain, C.; Anton, G.; Horn, M.; Wilensky, U. (April 2020, Proceedings of International Conference on Computational Thinking Education 2020)

   Kong, S.C. (Ed.)

   This work aims to help high school STEM teachers integrate computational thinking (CT) into their classrooms by engaging teachers as curriculum co-designers. K-12 teachers who are not trained in computer science may not see the value of CT in STEM classrooms and how to engage their students in computational practices that reflect the practices of STEM professionals. To this end, we developed a 4-week professional development workshop for eight science and mathematics high school teachers to co-design computationally enhanced curriculum with our team of researchers. The workshop first provided an introduction to computational practices and tools for STEM education. Then, teachers engaged in co-design to enhance their science and mathematics curricula with computational practices in STEM. Data from surveys and interviews showed that teachers learned about computational thinking, computational tools, coding, and the value of collaboration after the professional development. Further, they were able to integrate multiple computational tools that engage their students in CT-STEM practices. These findings suggest that teachers can learn to use computational practices and tools through workshops, and that teachers collaborating with researchers in co-design to develop computational enhanced STEM curriculum may be a powerful way to engage students and teachers with CT in K-12 classrooms. 

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5. Bringing Real-World Data And Visualizations Into Data Structures Courses Using BRIDGES

   https://doi.org/10.1145/2839509.2850512  

   Subramanian, Kalpathi; Payton, Jamie; Burlinson, David; Mehedint, Mihai (January 2016, ACM SIGCSE 2016)

   This demo introduces participants to the concepts and application of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in introductory computer science courses using data from real-world systems that are of interest to students, such as Facebook, Twitter, and Google Maps. BRIDGES provides access to real-world data sets for use in traditional data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also helps the students to explore and understand the use of data structures by providing each student with a visualization of operations performed on the student's own implementation of a data structure. BRIDGES visualizations can be easily shared (via a weblink) with peers, friends, and family. Demo attendees will see (and possibly engage in) hands-on experience with BRIDGES and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. Additionally, the demo will complement our oral presentation of our work at SIGCSE, by providing hands-on demonstrations of BRIDGES. 

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