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1. Discussion Questions as Metacognitive Exercises

   Kaw, A.; Clark, R.M (March 2024, Proceedings of the ASEE-SE Conference)

   In a core mechanical engineering course on numerical methods at the University of South Florida in the fall of 2022, students were presented with discussion questions to serve as metacognitive activities. The course consisted of eight topics, and after each topic, the students were asked a single discussion question. While answering these questions was optional for the students, it served as 2% extra credit for the eight questions of the course. This initiative was initially taken to offset any occasional missed 30 online homework assignments, which accounted for 15% of the grade for the semester. These questions were designed to elicit thoughtful and unique responses from the students. To promote learning from others, students were allowed to see posted responses from other students only after they had submitted theirs. The questions ranged from making a meme to describing a difficult or intuitive concept. Despite the opportunity for extra credit and the unique prompts, the participation rate was only 59% of the possible submissions, and no clear trend was observed between the participation of high- or low-performing students. 

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2. Using a Planning Prompt Survey to Encourage Early Completion of Homework Assignments

   https://doi.org/10.1145/3491140.3528297  

   Felker, Zachary; Chen, Zhongzhou (June 2022, L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale)

   In an earlier study we showed that small amounts of extra credit offered for early progress on online homework assignments can reduce cramming behavior in introductory physics students. This work expands on the prior study by implementing a planning prompt intervention inspired by Yeomans and Reich's similar treatment. In the prompt we asked students to what degree they intended to earn extra credit offered for early work on the module sequence, and what their plan was to realize their intentions. The survey was assigned for ordinary course credit and due several days before the first extra credit deadline. We found that students who completed the prompt earned on average 0.6 more extra credit points and completed the modules an average of 1.1 days earlier compared to a previous semester. We detect the impact of the survey by creating a multilinear model based on data from students exposed to the intervention as well as students in a previous semester. Data from five homework sequences are included in the model to account for differences between the two semesters that cannot be attributed to the planning prompt intervention. 

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3. PREPARATORY DISCUSSION AND PROJECT AUGMENTED STUDENT LEARNING VIA STUDENT PRESENTATION BASED EFFECTIVE TEACHING (SPET) APPROACH

   Pawan, Tyagi (January 2022, EDUlearn 2022, 14th annual International Conference on Education and New Learning Technologies Palma de Mallorca (Spain). 4th - 6th of July, 2022.)

   Instructor-led presentation-based teaching mainly focuses on delivering content. Whereas student active presentations-based teaching approaches require students to take leadership in learning actions. Many teaching and learning strategies were adopted to foster active student participation during in-class learning activities. We developed the student presentation-based effective teaching (SPET) approach in 2014 to make student presentation activity the central element of learning challenging concepts. We have developed several versions to meet the need for teaching small classes (P. Tyagi, "Student Presentation Based Effective Teaching (SPET) Approach for Advanced Courses," in ASME IMECE 2016-66029, V005T06A026), large enrolment classes (P. Tyagi, "Student Presentation Based Teaching (SPET) Approach for Classes With Higher Enrolment," ASME IMECE 2018-88463, V005T07A035), and online teaching during COVID-19. (P. Tyagi, "Second Modified Student Presentation Based Effective Teaching (SPET) Method Tested in COVID-19 Affected Senior Level Mechanical Engineering Course," in ASME IMECE 2020-23615, V009T09A026). The SPET approach has successfully engaged students with varied interests and competence levels in the learning process. SPET approach has also made it possible to cover new topics such as training engineering students about positive intelligence skills to foster lifelong learning aptitude and doing engineering projects in a group setting. However, it was noted that many students who were overwhelmed with parallel academic demands in other courses and different activities were underperforming via SPET-based learning strategies. SPET core functioning depends on the following steps: Step 1: Provide a set of conceptual and topical questions for students to answer individually after self-education from the recommended textbook or course material, Step-2: Group presentations are prepared by the prepared students for in-class discussion, Step-3: Group makes a presentation in class 1-2 weeks after the day of the assignment to seek instructor feedback and to do peer discussion. The instructor noted that students unfamiliar with the new concepts and terminologies in the SPET assignment struggled to respond to questions individually and contribute to the group discussion based on their presentation. Several motivated students who invested time in familiarizing new concepts and terminologies met or exceeded the expectations. However, a significant student population struggled. To alleviate this issue author has implemented a further improvement in SPET approach. This paper reports teaching experiments conducted in MECH 487 Photovoltaic Cells and Solar Thermal Energy System and MECH 462 Design of Energy Systems course. This improvement requires augmenting SPET with instructor-led concept familiarization discussion on the day of issuing the assignment or close to that; for this step instructor utilized exemplary student work from prior SPET-based teaching of the same course. In the survey, many students expressed their views about the improvement and reported introductory discussions were helpful and addressed several reservations and impediments students encountered. This paper will discuss the structure of the new improvement strategy and outcomes-including student feedback and comments. 

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4. Work in Progress: Assessment of Reflective Thinking in Graduate Engineering Students: Human and Machine Methods

   Taraban, R.; Isserman, M.; Pittman, J.C.; Yeo, N.; Campbell, R.C.; Kim, J.H.; Reible, D.D. (July 2021, 2021 ASEE Annual Conference Proceedings)

   Engineering education is increasingly looking to the liberal arts to broaden and diversify preparation of students for professional careers. The present study involves an elective graduate environmental engineering course that incorporated the arts and humanities. The goal of the course was to develop engineers and technical professionals who would become both more appreciative of and better equipped to address technical, ethical, social, and cultural challenges in engineering through the development of critical and reflective thinking skills and reflective practice in their professional work. A reflective writing assignment was submitted by students following each of fourteen course topics in response to the following question: Reflect on how you might want to apply what you learned to your development as a professional and/or to your daily life. Student responses were classified by human coders using qualitative text analytic methods and their classifications were attempted to be learned by a simple machine classifier. The goal of this analysis was to identify and quantify students’ reflections on prospective behaviors that emerged through participation in the course. The analysis indicated that the primary focus of students’ responses was self-improvement, with additional themes involving reflection, teamwork, and improving the world. The results provide a glimpse into how broadening and diversifying the curriculum might shape students’ thinking in directions that are more considerate of their contributions to their profession and society. In the discussion, we consider the findings from the human and machine assessments and suggest how incorporating AI machine methods into engineering provides new possibilities for engineering pedagogy. 

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5. Strategies for Deploying Unreliable AI Graders in High-Transparency High-Stakes Exams

   https://doi.org/10.1007/978-3-030-52237-7  

   Azad, Sushmita; Chen, Binglin; Fowler, Maxwell; West, Matthew; Zilles, Craig (June 2020, International Conference on Artificial Intelligence in Education)

   null (Ed.)

   We describe the deployment of an imperfect NLP-based automatic short answer grading system on an exam in a large-enrollment introductory college course. We characterize this deployment as both high stakes (the questions were on an mid-term exam worth 10% of students’ final grade) and high transparency (the question was graded interactively during the computer-based exam and correct solutions were shown to students that could be compared to their answer). We study two techniques designed to mitigate the potential student dissatisfaction resulting from students incorrectly not granted credit by the imperfect AI grader. We find (1) that providing multiple attempts can eliminate first-attempt false negatives at the cost of additional false positives, and (2) that students not granted credit from the algorithm cannot reliably determine if their answer was mis-scored. 

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