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1. Quantifying the Relationship Between Projects, Assignments and Grade in Computer Science I

   Kumar, Amruth N. ( October 2019 , Frontiers in Education (FIE 2019))

   We analyzed the data collected in a Computer Science I course to quantify the relationship between programming projects, code-tracing assignments and course grade when online tests and closed lab instruction were used in the course. We found that completion of programming projects was positively and moderately correlated with course grade; each completed project contributed nearly one sign grade to the course grade; the grade of students who had completed at least a given number of projects was four sign grades better than of those who had not and the difference was statistically significant; the mean course grade ranged from F for those who had completed 1 or fewer projects to A- for those who had completed 9 or more projects; and completion of later projects was indicative of higher grade in the course. Similarly, completion of code-tracing assignments was positively, but weakly correlated with course grade; the grade of students who had completed at least a given number of assignments was one letter grade better than of those who had not, and the difference was statistically significant; and the mean course grade ranged from C- for those who had completed 6 or fewer assignments to B+ for those who had completed 11 or more assignments. Concurrence among the course objectives, classroom instruction, assessment techniques, programming projects and assignments may be a pre-requisite for obtaining the results of this study. 

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2. Comprehension First: Evaluating a Novel Pedagogy and Tutoring System for Program Tracing in CS1

   https://doi.org/10.1145/3105726.3106178  

   Nelson, Greg L. ; Xie, Benjamin ; Ko, Andrew J. ( January 2017 , ACM International Computing Education Research Conference)

   What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures. 

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3. Investigating the Use of Planning Sheets in Young Learners’ Open-Ended Scratch Projects

   https://doi.org/10.1145/3501385.3543972  

   Gonzalez-Maldonado, David ; Pugnali, Alex ; Tsan, Jennifer ; Eatinger, Donna ; Franklin, Diana ; Weintrop, David ( August 2022 , ICER 2022)

   Open-ended tasks can be both beneficial and challenging to students learning to program. Such tasks allow students to be more creative and feel ownership over their work, but some students struggle with unstructured tasks and, without proper scaffolds, this can lead to negative learning experiences. Scratch is a widely used coding platform to teach computer science in classrooms and is designed to support learner creativity and expression. With its open-ended nature, Scratch can be used in various ways in the classroom to meet the needs of schools and districts. One challenge of using Scratch in classrooms is supporting learners in exploring their interests and fostering creativity while still meeting the instructional goals of a lesson and ensuring all students are engaged with, and understand, focal concepts and practices. In this paper, we investigate the use of planning sheets to fa- cilitate novice programmers designing and implementing Scratch programs based on open-ended prompts. To evaluate the plan- ning sheets, we look at how closely students’ implemented Scratch projects match their plans and whether the implemented Scratch projects met the technical requirements for the given lesson. We analyzed 303 Scratch projects from 155 middle grade students (ages 10-14) who were introduced to programming via the Scratch Encore Curriculum. Completed Scratch projects that used planning sheets (202) were qualitatively coded to evaluate how closely they matched the initial plan, and Scratch programs (303) were analyzed with an automated grader to check if technical project requirements were met. Our results reveal that students that used planning sheets met significantly more technical project requirements and had more complex structures than those that did not have planning sheets. Results differ based on teacher and type of planning sheet used (physical vs. virtual). This work suggests that planning sheets are a helpful tool for young learners when completing open-ended coding projects. 

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4. Factors Mediating Learning and Application of Computational Modeling by Life Scientists

   https://doi.org/10.1109/FIE.2018.8659328  

   Madamanchi, Aasakiran ; Cardella, Monica E. ; Glazier, James A. ; Umulis, David M. ( October 2018 , 2018 IEEE Frontiers in Education Conference (FIE))

   This Work-in-Progress paper in the Research Category uses a retrospective mixed-methods study to better understand the factors that mediate learning of computational modeling by life scientists. Key stakeholders, including leading scientists, universities and funding agencies, have promoted computational modeling to enable life sciences research and improve the translation of genetic and molecular biology high- throughput data into clinical results. Software platforms to facilitate computational modeling by biologists who lack advanced mathematical or programming skills have had some success, but none has achieved widespread use among life scientists. Because computational modeling is a core engineering skill of value to other STEM fields, it is critical for engineering and computer science educators to consider how we help students from across STEM disciplines learn computational modeling. Currently we lack sufficient research on how best to help life scientists learn computational modeling. To address this gap, in 2017, we observed a short-format summer course designed for life scientists to learn computational modeling. The course used a simulation environment designed to lower programming barriers. We used semi-structured interviews to understand students' experiences while taking the course and in applying computational modeling after the course. We conducted interviews with graduate students and post- doctoral researchers who had completed the course. We also interviewed students who took the course between 2010 and 2013. Among these past attendees, we selected equal numbers of interview subjects who had and had not successfully published journal articles that incorporated computational modeling. This Work-in-Progress paper applies social cognitive theory to analyze the motivations of life scientists who seek training in computational modeling and their attitudes towards computational modeling. Additionally, we identify important social and environmental variables that influence successful application of computational modeling after course completion. The findings from this study may therefore help us educate biomedical and biological engineering students more effectively. Although this study focuses on life scientists, its findings can inform engineering and computer science education more broadly. Insights from this study may be especially useful in aiding incoming engineering and computer science students who do not have advanced mathematical or programming skills and in preparing undergraduate engineering students for collaborative work with life scientists. 

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5. Work-in-Progress: Enabling Secure Programming in C++ & Java through Practice Oriented Modules

   Kenneth Andrew Guernsey ; Jacob Matthew ; Quamar Niyaz ; Xiaoli Yang ; Ahmad Y Javaid ; Sidike Paheding ( August 2022 , ASEE Annual Conference proceedings)

   Nowadays, cyberattack incidents are happening on a daily basis. As a result, the demand for a larger and more challenging workforce is increasing. To handle this demand, academic institutions offer cybersecurity courses and degree programs into their curricula; however, more efforts are needed to address the high demand of the cybersecurity workforce. This work aims to bridge the gap between workforce shortage and the number of qualified graduates to fill the positions. We approach this by introducing cybersecurity concepts at the early stage of undergraduate curricula of computer science and engineering programs. Secure programming is critical as many cybersecurity incidents happen due to software vulnerabilities. However, most UG-level programming courses pay little attention to secure programming practices. As a result, many students graduate with limited knowledge of security vulnerabilities that might plague the developed software. Our goal in this work is to introduce secure programming at introductory level programming courses so that students should be aware of cybersecurity issues and use this security mindset in advanced level courses and projects in their degree programs. To accomplish this goal, we developed intuitive and interactive modules emphasizing secure programming in C++ and Java courses to help students become secure software developers. These modules will be used alongside the coursework to emphasize certain vulnerabilities within the programming environment of a specific language and allow students to learn cybersecurity topics, enforcing a solid foundation and understanding. We developed cybersecurity educational modules for C++ and Java as they are amongst the popular languages and used in introductory programming courses. While designing these modules, we kept in mind that the topics must be relevant to real-world issues in the software industry. We used a variety of resources and benchmarks to ensure the authenticity of our chosen topics, including Common Weakness Enumeration (CWE) and Common Vulnerability and Exposures (CVE). While choosing module topics to develop, we had some restrictions. For example, the topics must be introductory and easy to understand. These modules are geared towards freshman or sophomore-level UG students who have just started programming. The developed security modules have four components: power-point slides, lab description, code template for the lab, and complete solution. The complete solution for each module will be provided to the instructors to check students’ work if they adopt the modules in their courses. The modules developed for a C++ programming course include labs on input validation, integer overflow, random number generation, function call with incorrect argument type, and dangling pointers. In Java, we developed lab modules for input validation, integer overflow, null object reference, random number generator, and data encapsulation. 

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