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1. Concept Warehouse Remote: Making Student Thinking Visible

   Koretsky, M. ( October 2020 , Advances in engineering education)

   null (Ed.)

   The shift to remote teaching with the COVID-19 pandemic has made delivery of concept- based active learning more challenging, especially in large-enrollment engineering classes. I report here a modification in the Concept Warehouse to support delivery of concept questions. The new feature allows instructors to make students’ reasoning visible to other students by showing selected written explanations to conceptually challenging multiple-choice questions. Data were collected for two large-enrollment engineering classes where examples are shown to illustrate how displaying written explanations can provide a resource for students to develop multi-variate reasoning skills 

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2. WIP: Using Machine Learning to Automate Coding of Student Explanations to Challenging Mechanics Concept Questions

   Harpreet Auby ; Namrata Shivagunde ; Anna Rumshisky ; Milo Koretsky ( June 2022 , ASEE Annual Conference proceedings)

   This work-in-progress paper describes a collaborative effort between engineering education and machine learning researchers to automate analysis of written responses to conceptually challenging questions in mechanics. These qualitative questions are often used in large STEM classes to support active learning pedagogies; they require minimum calculations and focus on the application of underlying physical phenomena to various situations. Active learning pedagogies using this type of questions has been demonstrated to increase student achievement (Freeman et al., 2014; Hake, 1998) and engagement (Deslauriers, et al., 2011) of all students (Haak et al., 2011). To emphasize reasoning and sense-making, we use the Concept Warehouse (Koretsky et al., 2014), an audience response system where students provide written justifications to concept questions. Written justifications better prepare students for discussions with peers and in the whole class and can also improve students’ answer choices (Koretsky et al., 2016a, 2016b). In addition to their use as a tool to foster learning, written explanations can also provide valuable information to concurrently assess that learning (Koretsky and Magana, 2019). However, in practice, there has been limited deployment of written justifications with concept questions, in part, because they provide a daunting amount of information for instructors to process and for researchers to analyze. In this study, we describe the initial evaluation of large pre-trained generative sequence-to-sequence language models (Raffel et al., 2019; Brown et al., 2020) to automate the laborious coding process of student written responses. Adaptation of machine learning algorithms in this context is challenging since each question targets specific concepts which elicit their own unique reasoning processes. This exploratory project seeks to utilize responses collected through the Concept Warehouse to identify viable strategies for adapting machine learning to support instructors and researchers in identifying salient aspects of student thinking and understanding with these conceptually challenging questions. 

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3. Transferring Institutions in Different Modalities: Lessons from Undergraduates Across Stages of the COVID-19 Pandemic

   https://doi.org/10.1177/15210251231179701  

   Stearns, Elizabeth ( June 2023 , Journal of College Student Retention: Research, Theory & Practice)

   The broad-scale challenges that higher education undertook in response to the COVID-19 pandemic changed a great deal about the student experience. Ongoing throughout several semesters, those changes may have affected the ways that transfer students experienced transition to the 4-year university setting, with implications for student retention. Interviews with students who transferred from community colleges to a large research university at three different pandemic stages—in 2019, 2020, and 2021—reveal that some aspects of the transfer experience remained consistent, while others changed. Notably, students experienced university-level coursework as academically challenging regardless of its mode of delivery and reported finding a diversity of academic and social options at the university. Transferring into online coursework helped reduce the shock associated with large-enrollment classes and navigating a large campus. Students reported fewer issues overall with course delivery when taking solely online coursework. The paper closes with recommendations to increase transfer student retention.

    

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4. Changing Homework Achievement with Mechanix Pedagogy

   Bante, Sonali J. ; Hilton, Ethan ; Talley, Kimberly Grau ; Shryock, Kristi J. ; Linsey, Julie S. ; Hammond, Tracy Anne ( June 2019 , 2019 ASEE Annual Conference & Exposition)

   Introductory engineering courses within large universities often have annual enrollments exceeding several hundreds of students, while online classes have even larger enrollments. It is challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. In such classes, professors assess whether students have mastered a concept through multiple-choice questions, marking answers as right or wrong with little feedback, or using online text-only systems. However, in these scenarios the feedback is of a mostly binary nature (right or wrong) with limited constructive feedback to scaffold learning. A growing concern among engineering educators is that students are losing both the critical skill of sketched diagrams and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). A sketch-recognition based tutoring system, called Mechanix, allows students to hand-draw solutions just as they would with pencil and paper, while also providing iterative real-time personalized feedback. Sketch recognition algorithms use artificial intelligence to identify the shapes, their relationships, and other features of the sketched student drawing. Other AI algorithms then determine if and why a student’s work is incorrect, enabling the tutoring system to return immediate and iterative personalized feedback facilitating student learning that is otherwise not possible in large classes. To observe the effectiveness of this system, it has been implemented into various courses at three universities, with two additional universities planning to use the system within the next year. Student knowledge is measured using Concept Inventories based in both Physics and Statics, common exam questions, and assignments turned in for class. Preliminary results using Mechanix, a sketch-based statics tutoring system built at Texas A&M University, suggest that a sketch-based tutoring system increases homework motivation in struggling students and is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis. In focus groups, students believed the system enhanced their learning and increased engagement. Keywords: sketch recognition; intelligent user interfaces; physics education; engineering education 

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5. Feel the Force! An Inquiry-based Approach to Teaching Free-body Diagrams for Rigid-body Analysis

   https://doi.org/10.18260/1-2--34669  

   Davishahl, Eric ; Haskell, Todd ; Singleton, Lee ( June 2020 , 2020 ASEE Virtual Annual Conference Content Access)

   Perusal of any common statics textbook will reveal a reference table of standard supports in the section introducing rigid body equilibrium analysis. Most statics students eventually memorize a heuristic approach to drawing a free-body diagram based on applying the information in this table. First, identify the entry in the table that matches the schematic representation of a connection. Then draw the corresponding force and/or couple moment vectors on the isolated body according to their positive sign conventions. Multiple studies have noted how even high performing students tend to rely on this heuristic rather than conceptual reasoning. Many students struggle when faced with a new engineering connection that does not match an entry in the supports table. In this paper, we describe an inquiry-based approach to introducing support models and free-body diagrams of rigid bodies. In a series of collaborative learning activities, students practice reasoning through the force interactions at example connections such as a bolted flange or a hinge by considering how the support resists translation and rotation in each direction. Each team works with the aid of a physical model to analyze how changes in the applied loads affect the reaction components. A second model of the isolated body provides opportunity to develop a tactile feel for the reaction forces. We emphasize predicting the direction of each reaction component, rather than following a standard sign convention, to provide opportunities for students to practice conceptual application of equilibrium conditions. Students’ also draw detailed diagrams of the force interactions at the mating surfaces in the connection, including distributed loadings when appropriate. We use equivalent systems concepts to relate these detailed force diagrams to conventional reaction components. Targeted assessments explore whether the approach described above might improve learning outcomes and influence how students think about free-body diagrams. Students use an online tool to attempt two multiple-choice concept questions after each activity. The questions represent near and far transfer applications of the concepts emphasized and prompt students for written explanation. Our analysis of the students’ explanations indicates that most students engage in the conceptual reasoning we encourage, though reasoning errors are common. Analysis of final exam work and comparison to an earlier term in which we used a more conventional approach indicate a majority of students incorporate conceptual reasoning practice into their approach to free-body diagrams. This does not come at the expense of problem-solving accuracy. Student feedback on the activities is overwhelmingly positive. 

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