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1. Using GIFT to Develop an Adaptive Distributed Learning Environment Supporting Data Science Competencies.

   Anaroua, Fadjimata; Liu, Hong; Malone, Naomi (May 2023, The U.S. Army Combat Capabilities Development Command – Soldier Center.)

   Sinatra, Anne M. (Ed.)

   This paper presents our latest research on the efficient support of Data Science (DS) students in higher education, particularly focusing on underserved universities. The need for new graduates and professionals to upskill in DS surpasses the capacity of universities to offer conventional classes, particularly in underserved universities (NASEM, 2018). Our solution provides otherwise unavailable DS courses for all students by implementing the Generalized Intelligent Framework for Tutoring (GIFT, Sottilare et al., 2012) to develop a multi-university adaptive distributed learning (ADL) environment that can share DS courses and facilitate student learning from anywhere at any time. This distributed learning ecosystem using Department of Defense (DOD)-initiated technologies (ADL, 2018) allows students from 11 networked universities to share courses and resources, providing equal access to underserved and better-equipped research universities within the system. Besides GIFT, the ADL environment integrates the learning management system (LMS), Moodle, competency management software such as Competence and Skill System (CaSS, 2021), and Learning Record Stores (LRSs) to collect and analyze data for personalized learning. Our instructional design and course development efficiently align learning objectives, activities, and assessments of DS student competencies based on the Edison DS Competency Framework (Edison, 2017). 

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2. An Empirical Approach to Understanding Data Science and Engineering Education

   https://doi.org/10.1145/3344429.3372503  

   Raj, Rajendra K.; Parrish, Allen; Impagliazzo, John; Romanowski, Carol J.; Aly, Sherif G.; Bennett, Casey C.; Davis, Karen C.; McGettrick, Andrew; Pereira, Teresa Susana; Sundin, Lovisa (July 2019, Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education)

   As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field’s fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis. 

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3. Crowdsourcing Classroom Observations to Identify Misconceptions in Data Science

   https://doi.org/10.18260/1-2--34359  

   Wertz, Ruth; Schmitt, Karl; Clark, Linda; Sandstede, Bjorn; Kinnaird, Katherine (June 2020, 2020 ASEE Virtual Annual Conference)

   Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project addresses the early stages of developing a concept inventory of student difficulty within the newly emerging field of data science. In particular this project will address three primary research objectives: (1) identify student misconceptions in data science courses; (2) document students’ prior knowledge and identify courses that teach early data science concepts; and (3) confirm expert identification of data science concepts, and their importance for introductory-level data science curricula. During the first year of this grant, we have collected approximately 200 responses for a survey to confirm concepts from an existing body of knowledge presented by the Edison Project. Survey respondents are comprised of faculty and industry practitioners within data science and closely related fields. Preliminary analysis of these results will be presented with respect to our third research objective. In addition, we developed and launched a pilot assessment for identifying student difficulties within data science courses. The protocol includes regular responses to reflective questions by faculty, teaching assistants, and students from selected data science courses offered at the three participating institutions. Preliminary analyses will be presented along with implications for future data collection in year two of the project. In addition to the anticipated results, we expect that the data collection and analysis methodologies will be of interest to many scholars who have or will engage in discipline-based educational research. 

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4. Integration of Research-based Strategies and Instructional Design: Creating Significant Learning Experiences in a Chemistry Bridge Course

   Villalta-Cerdas, A. (July 2021, 2021 ASEE Virtual Annual Conference Content Access)

   null (Ed.)

   Bridge courses are often created to provide participants with remediation instruction on discipline-specific content knowledge, like chemistry and mathematics, before enrollment in regular (semester-long) courses. The bridge courses are then designed to impact student’s academic success in the short-term. Also, as a consequence of the bridge course experience, it is often expected that students’ dropout rates on those regular courses will decrease. However, the bridge courses are often short (ten or fewer days) and packed with content, thus creating challenges for helping students sustain their learning gains over time. With the support of the NSF funded (DUE - Division Of Undergraduate Education) STEM Center at Sam Houston State University, we are designing a course for entering chemistry students that consists of a one-week pre-semester intensive bridge component, which then flows into a one-month co-curricular support component at the beginning of the semester. The primary goals of the bridge component of the course are to strengthen student academic preparedness, calibrated-self-efficacy, and to foster networking leading to a strong learning community. The goal of the co-curricular extension is to help students sustain and build upon the learning gains of the initial bridge component. We plan to extend the co-curricular portion of the course in future years. A key measure of success will be improved participant course grades in the introductory chemistry courses for majors. Our design process has been centered on weekly meetings that alternate between literature review and course design. The design process was initiated with backward design principles and continues with ongoing revision. The goals, design strategy, and design process of this new course will be presented along with the achieved student outcomes during the implementation of the past summer 2020. 

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5. Integration of Research-based Strategies and Instructional Design: Creating Significant Learning Experiences in a Chemistry Bridge Course

   Villalta-Cerdas, A. (July 2021, 2021 ASEE Virtual Annual Conference Content Access)

   null (Ed.)

   Bridge courses are often created to provide participants with remediation instruction on discipline-specific content knowledge, like chemistry and mathematics, before enrollment in regular (semester-long) courses. The bridge courses are then designed to impact student’s academic success in the short-term. Also, as a consequence of the bridge course experience, it is often expected that students’ dropout rates on those regular courses will decrease. However, the bridge courses are often short (ten or fewer days) and packed with content, thus creating challenges for helping students sustain their learning gains over time. With the support of the NSF funded (DUE - Division Of Undergraduate Education) STEM Center at Sam Houston State University, we are designing a course for entering chemistry students that consists of a one-week pre-semester intensive bridge component, which then flows into a one-month co-curricular support component at the beginning of the semester. The primary goals of the bridge component of the course are to strengthen student academic preparedness, calibrated-self-efficacy, and to foster networking leading to a strong learning community. The goal of the co-curricular extension is to help students sustain and build upon the learning gains of the initial bridge component. We plan to extend the co-curricular portion of the course in future years. A key measure of success will be improved participant course grades in the introductory chemistry courses for majors. Our design process has been centered on weekly meetings that alternate between literature review and course design. The design process was initiated with backward design principles and continues with ongoing revision. The goals, design strategy, and design process of this new course will be presented along with the achieved student outcomes during the implementation of the past summer 2020. 

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