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1. ISPeL: A topic dependency-driven system for personalized learning

   https://doi.org/10.1109/ICSC50631.2021.00081  

   Gudivada, Venkat N; Herndon, Nic; Rao, Dhana (January 2021, 2021 IEEE 15th International Conference on Semantic Computing (ICSC))

   In this paper, we describe the design and development of ISPeL - an Interactive System for Personalization of Learning. Central to ISPeL is topic-based authoring. A topic is a small, self-contained, reusable, and context-free content unit. Learners may study a topic provided that they have met its prerequisite dependencies. Pre- and post-tests are associated with topics. Furthermore, topics feature several practice problems to enhance student learning. A pilot implementation of three undergraduate computer science courses currently in ISPeL is also presented. 

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2. 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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3. Introduction to CRISPR-Cas9 Techniques: Strategies for the Laboratory and the Classroom

   Wolyniak, Michael J; Pattison, Donna L; Pieczynski, Jay N; Santisteban, Maria S (February 2025, Springer)

   Wolyniak, Michael J; Pattison, Donna L; Pieczynski, Jay N; Santisteban, Maria S (Ed.)

   This open-access textbook provides an in-depth introduction to CRISPR-Cas9 technology and explores its use across the gamut of biological model systems. This volume is intended for classroom instructors at the high school and undergraduate level. It provides insight into integration of CRISPR-Cas into undergraduate laboratory courses as well as options for dry-bench work and activities to scaffold learning. 

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4. OpenDSA/Mastery Grids Exercise Interchange

   Manzoor, Hamza; Akhuseyinoglu, Kamil; Shaffer, Clifford; Brusilovsky, Peter (February 2019, Fourth SPLICE Workshop "CS Education Infrastructure for All II: Enabling the Change" at ACM SIGCSE 2019)

   OpenDSA is an e-Textbook project developed at Virginia Tech, which provides materials to support courses in a wide variety of Computer Science-related topics. OpenDSA primarily contains Java exercises. ACOS server which is a smart learning content server developed as a joint project of Aalto University and the University of Pittsburgh. It enhances the reusability of online learning activities by decoupling the content and the existing interoperability protocols including LTI. ACOS is capable of serving multiple smart contents including Python animations and exercises. In this project, we present steps toward integrating smart content served through ACOS into OpenDSA. Similarly, CodeWorkout is an open source system which helps students to practice small programming problems. Mastery Grids is also open source progress visualization environment which has open (social) learner model features designed at the University of Pittsburgh. In this project, we extended contents served by Mastery Grids by integrating CodeWorkout exercises and visualize student progress in an Open Learner Model. All of this has been possible because all of these tools support LTI, reiterating the importance of why the utilization of standards while developing applications is important. 

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5. Understanding Data Science Instruction in Multiple STEM Disciplines

   Snyder, C.; Asamen, D. M.; Naseri, M. Y.; Aryal, N.; Biswas, G.; Dubey, A.; Henrick, E.; Hotchkiss, E. R.; Jha, M. K.; Jiang, S. X.; et al (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 an education research and consulting group. 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 has 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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