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1. Empowering Students to be Adaptive Decision Makers: Finalizing a Multi-dimensional Inventory of Decision-Making Competency

   Orr, Marisa K.; Martin, Baker A.; Ehlert, Katherine M.; Brotherton, Haleh B.; Manning, Jessica A. (July 2021, Proceedings of the American Society for Engineering Education Annual Conference & Exposition)

   The main objective of this project is to help students learn to make decisions that lead to academic success. Our first goal is to map curriculum pathways, which begins by studying overpersistence (when a student persists in a particular major but does not make timely progress toward a degree). We seek to identify curriculum-specific indicators of overpersistence and corresponding alternative paths that could lead to success. Our second goal is to improve the structure of the Decision-Making Competency Inventory (DMCI) so that it can explain student's decision-making competency in more detail and in congruence with the Self-Regulation Model of Decision-Making. This instrument will be used to map decision-making competency to academic choices and outcomes. The third goal is to develop an Academic Dashboard as a means for sharing relevant research results with students. This will allow students to have access to the strategies, information, and stories needed to make and implement adaptive decisions. This paper highlights our progress in the fifth year of the project and our plans going forward. 

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2. Empowering Students to be Adaptive Decision-makers: Progress and Directions

   https://doi.org/10.18260/1-2--32205  

   Orr, Marisa K.; Martin, Baker A.; Rucks, Maya; Ehlert, Katherine M. (June 2019, 2019 ASEE Annual Conference & Exposition Proceedings)

   null (Ed.)

   The objective of this EEC project is to help students learn to make academic decisions that lead to success. The research goals are to: 1) identify curriculum-specific patterns of achievement that eventually lead to dropout and corresponding alternative paths that could lead to success; and 2) advance knowledge of self-regulation patterns and outcomes in engineering students. The education goals are to develop curricula and advising materials that help students learn how to effectively self-regulate their decision processes through contextual activities and story prompting. This poster will present current progress and future directions of the project. We will summarize accomplishments on the development of the Self-Regulated Decision-Making instrument, mapping of pathways, and development of the academic dashboard. 

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3. Gamification of Chemical Engineering Pathways: Evidence from Introductory Courses

   https://doi.org/10.18260/1-2--37217  

   Brown, Michael; Lamm, Monica; Nadolny, Larysa (July 2021, ASEE Conferences)

   The retention of students pursuing chemical engineering degrees is essential to the future science, technology, engineering, and mathematics (STEM) workforce, but failure in introductory chemistry coursework is a barrier to degree persistence and completion. Despite the positive research on impact of gamification on engagement and academic achievement, only a small number of gamification studies focus on large enrollment STEM courses like those taken by chemical engineers early in their major program, and few incorporate robust measures to rigorously and systematically assess students’ behavioral, cognitive, and affective changes. The goal of this study is to establish effective strategies for the application of gamification in courses that appear early in the chemical engineering curriculum, supporting the retention of students in the major and the graduation of chemical engineers. This was achieved through the development of a chemistry and chemical engineering focused dashboard that is integrated within an online learning management system that includes gamification tools (i.e., leaderboard, badges, and rewards). We report the results of a design-based research study of the dashboard in the introductory chemistry sequence for chemical engineers at a large research university. Students were provided access to the dashboard as part of the learning management system. The dashboard was designed to align to course content. As part of ABET accreditation, chemical engineering majors complete a progress assessment in their second year before progressing in the major. The badges provided to students in the system were based on concepts from the course that would appear on a progress assessment, and provide students an indication of their proficiency on the topic based on their performance in the course. Students were also provided a visualization of tasks to complete within each week, a ‘health’ monitor that provides them their average score on recent assignments by type (homework, exam, lab quizzes), and interactive rewards that surprised students based on their performance and engagement. Our analysis involves complementary methods of quantitative evaluation and qualitative description of how dashboard use impacted chemical engineering students’ motivation and performance in introductory chemistry coursework. We conduct a multi-variate linear regression model to predict students’ academic performance given their use of the student facing dashboard (n=548/578). We control for initial motivational beliefs as measured by Perez’s adaptation of Eccles’ expectancy value scale for STEM courses, students’ emerging engineering identity as measured by XXX instrument, and demographic differences. Our findings suggest that students benefit from using the dashboard as part of their engagement with course resources, such that as students’ engagement with the dashboard (as measured in accumulated page views) increases, their end of term grade increases (B=5.8 points on final grade out of 100, p=0.000). We did not observe significant differences by initial motivational beliefs, engineering identity scales, demographics, or students’ estimates on the amount of time spent preparing for class. Additionally, we conducted thematic analyses of students’ qualitative feedback on the dashboard features. Students identified the utility of the visualization for helping them plan their time for the week, and appreciated the feedback from the health monitor. 

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4. Smart Cities Designed by Smart Communities: An Engaged Approach to Develop a Sociotechnical Network

   https://doi.org/10.1061/9780784484852.075  

   Jenewein, Oswald; Hummel, Michelle A; Bezbourah, Karabi; Liu, Yonghe; Masten, Kathryn (May 2023, American Society of Civil Engineers)

   In the process of developing a smart city framework, sensor data are crucial to enable cities and communities to make informed decisions on future plans. Involving community-based organizations and residents is an integral component of this process to ensure equity and accessibility of data. This study aims to develop a sociotechnical network to (1) identify vulnerability zones; (2) measure data on flooding, air, and water quality; and (3) inform community members and decision-makers through a data dashboard. A small coastal town in the Texas Coastal Bend region is utilized as a case study. Methodologically, this study utilizes participatory action research to frame a mixed-methods approach toward developing a data dashboard. This research project is a practical guide for engaged scholars in the social sciences, engineering, and urban design fields. The outcomes include recommendations for the engaged community and provide a data-dashboard targeting academic and non-academic audiences, residents, and decision-makers. 

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5. BOARD # 426: Preliminary results of an interactive dashboard for mentoring NSF S-STEM students

   https://doi.org/10.18260/1-2--55804  

   Silvestri, Fanny; Neal, Nichole; Johnson, Elisabeth (June 2025, ASEE Conferences)

   Introduction: Since the Fall of 2023, the community college has nurtured students through the NSF S-STEM Grant initiative called Scholarships, Mentoring, and Professional Support to Improve Engineering & Artificial Intelligence Student Success at Community Colleges. This grant, also known as Reaching Engineering and Artificial Intelligence Career Heights (REACH), empowers students with scholarships, personalized mentoring, and industry-oriented activities. This study delves into an Individual Development Plan (IDP) interactive dashboard used during the mentoring sessions. Methodology: An interactive dashboard made on Google Spreadsheet was developed to record monthly academic data, contact with industrial, working hours, and key moments, and help the students reflect on their data. Each semester, the students and their mentors filled a different tab. Each month, they filled different columns of data. The data are divided into 3 groups: grade per course, confidence to complete the same course, and outside academic activity: work, industry visits, clubs etc. Three charts illustrate the trend of that information. Results: 20 students (at the date of Fall 2024) are or were enrolled in the program, including 7 in AAS, Emphasis in Artificial Intelligence, and 13 in AS, Emphasis in Engineering, with 7 females, and 13 males. 94% of the IDP dashboard was filled and 100% of the students reported that the IDP tool was extremely useful or useful. The data also show that students completed 94% of the courses and their workload decreased on average from 22 hours to 17 hours per week over the semesters. One mentee reported the IDP as making them “want to continue their progress and keep their grades up and that it gives them (mentee and mentor) something to talk about right away.“ One mentor summarized the IDP tool as allowing them “to consider where they can support their mentee(s).” Conclusion: The IDP dashboard makes the mentoring sessions more efficient, focuses on the challenges faced by the mentee(s) during this specific month, and tracks data. A mentor suggested breaking up the options of extra-academic activities as they are key to keeping students engaged in their academic journey. Metrics such as networking, volunteering, participating in professional organizations, listening to speakers' presentations, and touring university partners and industry companies will be added to the dashboard. Acknowledgment: The authors would like to express their sincere thanks and gratitude to the National Science Foundation (NSF) for the Scholarship in Science, Technology, Engineering, and Mathematics (S-STEM) award No. 2220959. 

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