This Work-in-Progress paper presents WebTA, a code critiquer developed using Java, currently being expanded to MATLAB for first-year engineering students. It outlines the modification process of the existing WebTA software for MATLAB and shares the results of three beta tests conducted with 52 students. The paper highlights the significance of providing an automated tool that helps students identify and improve coding errors is highlighted (addressing the gap between computer science and engineering education). WebTA's feedback-on-demand feature addresses the challenge of providing timely and personalized feedback to a large number of students, contributing to the enhancement of first-year engineering education.

This research is part of a larger development project that is working on a multi-programming language code critiquer called WebTA. The WebTA code-critiquing software is designed to be used in courses for novice programmers, e.g., CS1 a first engineering course. The authors report on a component of the project that makes initial steps towards a automating the identification of common student mistakes, or antipatterns in code. Antipatterns can be errors, inefficiencies, or incorrect style choices in the code. This works is aimed at Python and uses the machine learning algorithm, Random Forests, to identify a stylistic antipattern of crowded operators.

The current research follows our work to validate the original, 26-item MUSIC Model of Motivation Inventory with Engineering students (presented in our 2022 FIE paper), where we validated the MUSIC inventory except for one of the MUSIC factors (Interest) which had several items cross-load onto other factors. Since then, the original survey authors have published a 19-item short form of the MUSIC Inventory. Our work seeks to validate the MUSIC survey instrument in the first-year engineering program, Michigan Technological University. We believe the MUSIC inventory could be a valuable tool for engineering education. To date, utilization of the MUSIC inventory has included few studies in an engineering learning context. This lack of uptake may be due to a lack of validation studies for the MUSIC Inventory in engineering classrooms. We report our early steps to validate the MUSIC Inventory for engineering programs. Our sample differed from the original validation work in that our sample consisted of mostly first-year students, while Jones sampled across class standing. All of our participants were engineering majors enrolled in a common first-year program of required coursework. In contrast, Jones sampled students in general education courses from various disciplines. We followed Jones and Wilkens' methodology for validation, which included validating items, scoring, and factors using a variety of analyses. Results revealed small differences in means between the long and short-form factors, but effect sizes indicate that they are negligible. Independence between the 5-factor scores from the 19-item version were gauged by examining correlations among the factors scores. Our correlations were higher than those Jones and Wilkens reported, leading us to question the independence between the Usefulness and Interest scale scores. Finally, a confirmatory factor analysis revealed high correlations between some factor scores and a lower-than-desired GFI. Again, problems appeared to stem from the Interest factor of the MUSIC Inventory. To better understand the validity of the inventory within engineering education, we conducted a 4-factor confirmatory factor analysis removing the Interest factor items. All fit indices improved, with the GFI approaching the desired value (0.90) at 0.879, and all correlations fell below the desired r = 0.71 (except for the Empowerment/Usefulness correlation of r = 0.776). In summary, the problems we reported between the Interest and Usefulness scores when validating the 26-item MUSIC Inventory for engineering students in an earlier study continue to exist in the more recent 19-item form. Possible reasons are discussed. We recommend caution when interpreting the Interest factor using the MUSIC inventory within engineering classes or programs and suggest future research.

Several rule-based code critiquing systems have been developed to support programmers. However, these systems often are targeted toward experienced learners. Novice learners often lack self-efficacy in programming [1]-code critiquers targeted at novice programmers to promote student learning and enrich the overall education system are essential. Students' self-efficacy for programming is the perception of students' competence concerning programming [2], an essential attribute of computer science education. This paper examines existing literature on the impact of self-efficacy on students in programming. This work focuses on empirical work in programming education that independently addresses and develops theories specific to student programming-at the same time, addressing the existing gap in understanding the impact of code critiquers on student self-efficacy. The systematic review followed guidelines proposed by Kitchenham methodology. Findings revealed various factors that improve self-efficacy and provide evidence of significant sources of self-efficacy in programming. Moreover, the investigation guides further research in designing code critiquers to enhance the self-efficacy of novice learners. This work is part of a more significant effort to investigate the use of antipatterns (common programming mistakes) in novice programmers' coding. This sub-project aims to determine if the use of a code critiquer by first-year engineering students will improve student self-efficacy regarding programming.

Expanding autocritiquers to other languages requires the difficult task of obtaining and representing antipat-terns. Under the current framework, each language requires a unique Abstract Syntax Tree (AST) and error solution such that both the resulting AST and error summaries can be searched and presented to students. Often similar antipatterns arise that have to be rewritten in each language's own context. This paper proposes the creation of a generalized AST structure for an auto-critiquer under development, that can unify common code structures across similar languages while capturing differences in the languages. This work also proposes a standard for error message representation that can capture similar errors across language specific error messages. There are two motivations to standardizing AST and error message formats. Firstly, our solution allows for a standardized User Interface (UI) and easy integration of new languages with an identical on-boarding process for both professors and students. Secondly, reuse of antipatterns will allow us to skip the time-consuming step of constructing near-identical structural and logical antipattern queries as well as unifying novice based error feedback.

One of the "Grand Challenges in Engineering Education" is to engage students in their own learning. Student engagement is widely seen as a necessary component driving the success of active learning methodologies. The Music Model of academic motivation was developed as a means to make the human motivation literature accessible to instructors interested in improving courses to increase student motivation and engagement. The model has a reliable and validated survey instrument that assesses 5 components of academic motivation. The model has been applied in two contexts relevant to our current project: in course design and improvement to assess the impact of changes on student motivation and learning, and second, it is used to examine students’ motivational perceptions and their relationship to other learning-related constructs. MUSIC has been used in K12 through higher education, and across a variety of fields.In this Work in Progress report, we had two purposes: First, we sought to test the use of the Music Model in an engineering course, since little research has been conducted in engineering courses to date. Second, we sought set the stage for developing a community of practice focused on student engagement with a common and straightforward assessment methodology for the first-year engineering community. Our broad goal is thus to leverage the MUSIC components as one metric for gauging improvement of student engagement for our own first-year engineering program, then eventually a community wide tool for first-year engineering programs broadly. The MUSIC scale inventory data (n=221) was collected electronically in 3 sections of a first-year engineering course at a mid-western technological university. A confirmatory factor analysis replicated the 5-factor MUSIC Model. An ANOVA revealed no differences in student motivations between our three-course sections. This result validates our ability to offer similar experiences across sections and instructors within our first-year course. Multiple comparisons between factor scores demonstrated significantly higher motivation reported on both the caring and success factors as compared to the others. In addition, the interest motivation factor was significantly lower than all other factors. These findings demonstrate the utility of the Music Model within engineering education. We discuss future research to develop a process for instructors to understand the results and make formative decisions for future course iterations. Further, we suggest future research re-establishing the link between the various motivational factors and educational outcomes such as GPA, course grades, retention in STEM, etc. We propose that global events, such as the pandemic, may have resulted in changes in students’ priorities regarding education, thereby altering previous findings regarding the importance of specific motivational factors on educational outcomes.

This full paper, in the research to practice category, focuses on student preferences for online versus face-to-face instruction. Spring Semester, 2020 started as usual but proved to be anything but usual. Instead, in a seven-day turnaround, the first-year engineering program at Michigan Technological University moved from a face-to-face, highly interactive studio environment to a remote/synchronous environment. At the end of the semester, our University and many others across the United States conducted a short survey of undergraduate students on their preference of face-to-face versus online instruction. Results showed a strong preference for face-to-face instruction. However, to adequately consider the extensive ranges of approach in both umbrella terms (“face-to-face instruction” and “online instruction”), we need to unpack the surface results. This paper reports on a short survey given to second-semester students in our College of Engineering, First-Year Engineering Program, and students in the first-year course in Systems Engineering. The survey sought to gather student preferences for two variations of our instructional models in current use in our first-year program: (a) remote/synchronous instruction versus (b) a hybrid environment that included face-to-face instruction with mandatory masking and social distancing. Results showed that students, at worst, held preferences that were generally not statistically different in terms of preferences. The several exceptions that did show significance showed numerical differences that were not of practical importance, with one exception. The core takeaway from our study is that determining student preferences for “face-to-face instruction” versus “distance learning” needs to be unpacked to enable students to register reasoned judgments and set the stage for meaningful results.

This Work in Progress, Research-to-Practice report focuses on an overview of an extensive project to update the First-Year Engineering Program, Michigan Technological University. After three planning years, and multiple pilot course offerings, we rolled out at scale in Fall, 2017. Our core student outcome goals in the revamped first-year program include strengthening the ability for open-ended problem solving, enhanced facility with computational problem solving applied to engineering problems, and increased student growth in traits of self-starting learning and positive attitudes for life-long learning. In this report, we describe both the core components of our revised program and the process we have followed to gain support for our revised first-year program, thus promoting program sustainability. We conclude with a core list of research questions we will pursue beginning in AY 2018-2019.

This WIP focusses on one component of our updated first year engineering program (FYEP), a student-owned laptop requirement. Requiring students to bring laptops will enable all students to practice the skills learned during their first-year engineering classes. Other engineering instructors will also be able to require students to bring and use laptops in their courses. Infusing the use of laptops into coursework throughout our engineering curriculums should positively affect computational problem solving and develop a mindset of “ubiquitous computing.” In this paper, we outline a longitudinal study in which we plan to assess the impact of the updated first year engineering program and in particular the laptop requirement on computational competencies and attitudes.

In this report we will describe our first steps in understanding the characteristics of individuals enrolling and completing MOOCs. The learner characteristic we focus on is the deep versus shallow learning dimension. We will use the revised two-factor study process questionnaire of Biggs in our study [1]. To our knowledge, there is no comparable research either reported in the literature or currently under way. Our focus is on Learning How to Learn (LHTL), currently the most heavily subscribed course on the Coursera platform. The last offering of LHTL, completed in January, 2015, attracted just under a quarter million learners. In the fourth and final week of the course, the R-SPQ-2F survey instrument was made available to all students on the LHTL site. Approximately 1,600 students completed the survey. We believe our research to be of interest widely because of the confluence in our research of (a) MOOCs, (b) the deep versus surface learning dimension, and (c) a methodology that can lead to better understanding of MOOCs.

Undergraduate STEM student enrollment has declined substantially over the last decade. Specifically there has been a steady decline in retention of early engineering students working through the first half of their degree programs. Student “leavers” typically fall into two categories (i) those facing academic difficulties and (ii) those that perceive the education environment of early engineering as hostile and not engaging. The Engaging Early Engineering Students Project (EEES) is a collaborative effort between Michigan State University (MSU) and Lansing Community College (LCC). EEES functions through the integration of four component programs designed to ease the transition of high school students into engineering undergraduate programs, and, by making the transition smoother, to increase retention at the College of Engineering (COE). The programs are: (a) Peer-Assisted Learning, (b) Connector Faculty, (c) Diagnostic-driven Early Intervention and (d) Cross Course linkages.

The Collaborative Process to Align Computing Education with Engineering Workforce Needs (CPACE) team developed a partnership among various stakeholders to identify the computational skills that are essential for a globally competitive engineering workforce. Our goal is to redesign the role of computing within the engineering programs at Michigan State University (MSU) and Lansing Community College (LCC) to develop computational competencies - informed by industry needs - by infusing computational learning opportunities into the undergraduate engineering curriculum. In this paper we summarize the process that we used to translate our research findings about the computational competencies needs in the engineering workplace into fundamental computer science (CS) concepts that can be used in curricular implementation. We also discuss the initial phase of our curricular implementation strategy in two disciplinary engineering programs at MSU and transfer programs at LCC.

Applied Engineering Sciences is a longstanding engineering undergraduate degree program in the College of Engineering, Michigan State University. It is at root an interdisciplinary program integrating core engineering studies, core business/management studies, and a depth-oriented “finishing area.” Over the last two years, an evolved program has been designed and implemented that retains the traditional strengths of the program while providing a strong underlying theme of systems thinking. In this WIP report we describe the new AES program, and our plans to leverage problem based learning as the core pedagogy for supporting student introduction and applications of systems thinking.

This paper presents an algorithm for recommending items using a diverse set of features. The items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. To support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the massive size of the graph. To overcome this problem, we apply multi-way clustering to group together the highly correlated nodes. A recommendation is then made by traversing the subgraph induced by clusters associated with a user's interest. Our experimental results on real data sets demonstrate the efficacy of the proposed algorithm.

Models that are constructed within the bounds of a single paradigm are not sufficient for modeling all aspects of complex systems. Therefore, even though reasoning and simulation systems that utilize a single modeling paradigm are the current norm, we explore a multimodel approach in this paper. A multimodel approach is defined as one in which more than one model-each derived from a different perspective, and utilizing correspondingly distinct reasoning and simulation strategies-are employed. By describing four models which illustrate the use of different modeling techniques, we show how a multimodel approach can enrich the modeling environment and make it correspond better with real world information. Our models come from many sources-Systems and Simulation literature for the modeling of natural phenomena and artificial devices, and Artificial Intelligence and Cognitive Science for the modeling of human intuition and expertise in reasoning. Generalizing from these four models, we suggest that modeling complex systems may best be approached from an integrated architectural viewpoint which combines multiple modeling paradigms.>

A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program.>

The concept of an identifiable ‘knowledge level’ has proven to be important by shifting emphasis from purely representational issues to implementation-free descriptions of problem-solving. The knowledge level proposal enables retrospective analysis of existing problem-solving agents, but sheds little light on how theories of problem-solving can make predictive statements while remaining aloof from implementation details. In this report, we discuss the knowledge level architecture, a proposal which extends the concepts of Newell and which enables verifiable prediction. The only prerequisite for application of our approach is that a problem-solving agent must be decomposable to the cooperative actions of a number of more primitive subagents. Implications of our work are in two areas. First, at the practical level, our framework provides a means for guiding the development of AI systems which embody previously understood problem-solving methods. Second, at the foundations of AI level, our results provide a focal point about which a number of pivotal ideas of AI are merged to yield a new perspective on knowledge-based problem-solving. We conclude with a discussion of how our proposal relates to other threads of current research.