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1. Comparing students’ solutions to an open-ended problem in an introductory programming course with and without explicit modeling interventions

   https://doi.org/10.18260/1-2--34311  

   Rodgers, Kelsey J. ; Verleger, Matthew A. ; Marbouti, Farshid ( June 2020 , ASEE Annual Conference proceedings)

   Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version had two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models. 

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2. Comparing Students’ Solutions to an Open-ended Problem in an Introductory Programming Course with and without Explicit Modeling Interventions

   Rodgers, K ; Verleger, M ; Marbouti, F ( January 2020 , ASEE Annual Conference proceedings)

   Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version had two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models. 

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3. Problem Solving When Using Student-Written YouTube Problems

   https://doi.org/10.18260/1-2--33193  

   Asogwa, Uchenna ; Liberatore, Matthew ; Malefyt, Amanda ; Duckett, Timothy ( January 2019 , ASEE Annual Meeting)

   Problem solving is a signature skill of engineers. Here, problem solving is employed when students apply course concepts to reverse engineer YouTube videos and solve new student-written, homework-style problems (YouTube problems). Replacing textbook problems with YouTube problems, this research focuses on examining the rigor of YouTube problems as well as students’ problem-solving skills on textbook and YouTube problems. A quasi-experimental, treatment/control group design was employed, and data was collected and evaluated using multiple measurement instruments. First, rigor of homework problems was examined using the NASA Task Load Index. Also, problem solving was assessed using a previously-developed rubric called PROCESS: Problem definition, Representing the problem, Organizing the information, Calculations, Evaluating the solution, Solution communication, and Self-assessment. PROCESS was modified to independently measure completeness and accuracy of student responses, as well as identify errors committed in material and energy balances. In the treatment group, students were assigned ten textbook problems and nine YouTube problems. While the control group obtained higher PROCESS scores at the beginning of the study, both groups exhibited similar problem-solving skills near the end. Also, the rigor of student-written YouTube problems was similar to textbook problems related to the same course concepts. 

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4. KC-Finder: Automated Knowledge Component Discovery for Programming Problems

   Shi, Y ; Schmucker, R ; Chi, M ; Barnes, T ; Price, T ( July 2023 , Springer)

   Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for (i) discovering KCs and (ii) demonstrating KCs, using students’ actual code submissions. Our system is based on two expected properties of KCs: (i) generate learning curves following the power law of practice, and (ii) are predictive of response correctness. We train a neural architecture (named KC-Finder) that classifies the correctness of student code submissions and captures problem-KC relationships. Our evaluation on data from 351 students in an introductory Java course shows that the learned KCs can generate reasonable learning curves and predict code submission correctness. At the same time, some KCs can be interpreted to identify programming skills. We compare the learning curves described by our model to four baselines, showing that (i) identifying KCs with naive methods is a difficult task and (ii) our learning curves exhibit a substantially better curve fit. Our work represents a first step in solving the data-driven KC discovery problem in computing education. 

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5. How Do Students Take up Notions of Environmental Racism in an Engineering Computational Methods Course?

   https://doi.org/10.18260/1-2--43362  

   Ozkan, Desen ; Hampton, Cynthia ( June 2023 , Annual ASEE Conference and Exhibition)

   Engineering departments have begun to prioritize more computational methods in their disciplines. Across engineering schools, computational methods are taught differently, but traditionally without context. In this study, we have revised an introductory engineering computing course such that students take up social, economic, and political contexts as they are introduced to coding and statistics. These contextual elements take three forms. The first is a weekly assignment where students read, reflect, and discuss various equity and justice-themed articles. The second is four weeklong projects over the semester that require a sociotechnical perspective to complete. Lastly, students complete an open-ended final project that requires attention to equity dimensions in each project step. This paper will examine the students’ responses to the weekly discussion reading on environmental racism. In this study, we focus on one week in which students read and reflected on two articles. One was an article from The Atlantic, titled “A New EPA Report Shows that Environmental Racism is Real” (Newkirk II, 2018). The other was an article from Vox titled, “There’s a clear fix to helping Black communities fight pollution” (Ramirez, 2021). The majority of students in this study are first-years enrolled in the school of engineering. The study takes place at a medium-sized, private, predominantly white institution in the northeast region of the US. Responses were collected across two years of this sociotechnical engineering revision. This study is not intended to compare the two years but to understand the breadth of ideas and responses students have in response to reading and reflecting on the article. Notably, two class sections of the course were revised in year one of the projects (2021), and all five sections of the course were revised in year two (2022). Each section is taught by a different engineering instructor. This study is not intended to compare students across different sections. Instead, through this qualitative thematic analysis, we attend to the different ways students take up and respond to social, political, and economic dimensions that have to do with the environment. 

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