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1. Choosing your own adventure: Engaging the new learning society through integrative curriculum design

   https://doi.org/10.29007/567f  

   Ryder, Elizabeth; Ruiz, Carolina; Weaver, Shari; Gegear, Robert (February 2020, EPiC Series in Education Science)

   In our increasingly data-driven society, it is critical for high school students to learn to integrate computational thinking with other disciplines in solving real world problems. To address this need for the life sciences in particular, we have developed the Bio-CS Bridge, a modular computational system coupled with curriculum integrating biology and computer science. Our transdisciplinary team comprises university and high school faculty and students with expertise in biology, computer science, and education. Our approach engages students and teachers in scientific practices using biological data that they can collect themselves, and computational tools that they help to design and implement, to address the real-world problem of pollinator decline. Our modular approach to high school curriculum design provides teachers with the educational flexibility to address national and statewide biology and computer science standards for a wide range of learner types. We are using a teacher- leader model to disseminate the Bio-CS Bridge, whose components will be freely available online. 

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2. How students learn functions in an integrated introductory data science module

   Mohamed, Rabab; Mondol, Anupom; Ma, Dexiu; Chao, Jie; Wiedemann, Kenia; Fofang, Janet_Bih; Monteith, Barnas; Xing, Wanli; Li, Linlin; Jo, Yelee; et al (February 2025, EasyChair Preprint)

   According to NASEM (2018), data science has foundations in computing, mathematics, and statistics. However, at the K-12 level, these foundations are usually taught as standalone courses that are unconnected with each other. Students may struggle to see their connections. We proposed a framework unifying those foundations using mathematical logic. A core concept in mathematical logic is function. A general function has one or more possibly non-number inputs and an output. Data science motivates a comprehensive understanding of functions and provides extensive culturally relevant, real-world, and data-rich problems and applications for students to practice their understanding. It is interesting to know how well students understand functions. We developed a six-lesson online module with more than 100 in-lesson questions. Initial analysis of the students’ answers to the questions shows that students can understand the basics of the general functions but have more difficulties in involved applications of functions. 

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3. Understanding Data Science Instruction in Multiple STEM Domains

   Snyder, Caitlin (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   As technology advances, data-driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research-practice partnership that brings together STEM+C instructors and researchers from three universities and education research and consulting groups. We aim to use high-frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings have improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses. 

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4. 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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5. Designing a Collaborative Game-Based Learning Environment for AI-Infused Inquiry Learning in Elementary School Classrooms

   https://doi.org/10.1145/3341525.3393981  

   Lee, Seung; Mott, Bradford; Ottenbriet-Leftwich, Anne; Scribner, Adam; Taylor, Sandra; Glazewski, Krista; Hmelo-Silver, Cindy E.; Lester, James (June 2020, Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education)

   Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence, a field of computer science, has with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore how to introduce AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios that enable students to gain experience with AI applied toward solving real-world life-science problems. 

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