leaders have called for incorporating thedevelopment of professional skills, like problem-solving for open-ended engineering designproblems, across all the different engineering courses. Following such a call, I, the author of thispaper, incorporated an engineering design project into the Computer Programming for Engineerscourse taught at University of Florida for two semesters, hoping that such instructionalintervention positively impacts students' problem-solving skills.2. Frameworks2.1 Conceptual Framework2.1.1 Social Problem-solvingThere are many ways in which literature has defined problem-solving; still, assessment tools formeasuring such skills are scarce. In this study, I used a model developed by D'Zurilla et al. [1] inwhich their team
, demographic surveys, and three tasks. Descriptive statistics and statistical tests provide insights.Performance discrepancies between IT and non-IT backgrounds are statistically significant. Feedback indicatespositive perceptions of low code. 1. Introduction In recent years, the intersection of technology and education has undergone a profound transformation, withemerging paradigms reshaping traditional approaches to teaching and learning. One such paradigm that hasgarnered increasing attention is low-code development—a revolutionary approach to software creation thatempowers individuals, regardless of their technical background, to design and deploy fully functional applicationswith minimal coding expertise. Low-code platforms provide
selecting VS Code and our approach to introducing it to engineering students. To assist students with diverse programming backgrounds, we provide comprehensive guidance with hierarchical indexing. By seamlessly integrating VS Code, known as a rich text editor, with a selection of extensions, our aim is to streamline the learning process for students by enabling it to function as an IDE. We perform an experimental evaluation of students' programming experience of using VS Code and validate the VS Code together with guidance as a promising solution for CS1 programming courses. 1. IntroductionIntegrated Development Environments (IDEs) play an important role in learning a
programming language has long been a staple in college computing education. AlthoughJava and Python are popular languages, C is still a top programming language of instruction [1], [2].Even if the introductory courses are taught in other languages, many programs still provide coursesthat teach the languages, typically in systems programming courses or operating systemcourses [3]–[5].However, unlike Java or Python where there is a single authorative compiler, C programming issupported by many compilers, editors, and other tools. In addition, installing a C developmentenvironment has traditionally been challenging for Windows systems. As a result, some institutionsopt for installing the C development environment in a server and have the students
instructor. However, often, a student would not complete the assignment during lab hours, so would have to wait for office hours to get an instructor's help. To submit, a student would upload the developed program files, then wait a week or more for grading to be completed and feedback to be provided.I n the last decade, many auto-graded programming assignment systems have been developed, both in academia and commercially [1–4]. Such systems are often web-based, save instructor's time with grading, and provide students more rapid feedback. Such systems have enabled instructors to switch from assigning one-large-program to many-small-programming assignments each week, wherein each assignment was more focused on a
that is engaging, interactive, and fun. This approachwas also compared with a research-centric group project that delved into establishing secure meth-ods for cyber-physical systems. The study indicates that a majority of students (77.4%) viewed theCapture the Flag Scavenger Hunt as a highly valuable learning experience.1 IntroductionStudying computer security is crucial in today’s interconnected digital landscape to safeguard sen-sitive information, preserve privacy, and ensure the reliable functioning of computer systems 1 . Anundergraduate (UG) course in computer security typically includes topics such as network security,operating system security, cryptography, software security 2 . Cryptography, a fundamental pillar
granularity demonstrates high levels of abstractionin the initial flowchart design, which may point to under-designing by participants and/or lowerlevels of metacognition. Comparatively, having high cohesion and granularity may point to over-designing by the participant and often stems from a one-to-one mapping of flowchart nodes tolines of code. Our results point toward a logical relationship between Design Cohesion andstudents’ level of self-estimated skill, and we are confident that Design Cohesion will serve asviable metric for understanding introductory programming metacognition.1. IntroductionThis paper presents our initial characterization of Design Cohesion and Granularity Level andour case study approach to the qualitative exploratory coding
institutions towards the adoption of computer-based exams [1, 2, 5, 6]. Studies like those by Lappalainen et al. [1], who found improvedoutcomes by beginning with paper-based exams and continue with computer-based exams, andGrissom et al. [4], who reported higher success in writing recursive solutions through computer-based exams, underscore this trend. Deloatch et al. [15] further highlighted a preference forcomputer-based exams, citing perceived improvements in quality, speed, and anxiety reduction.Computer-based exams present both opportunities and challenges, particularly in terms oftechnical stability and academic integrity.. For example, Rajala et al. [2] developed anexamination platform for Java programming, integrating multiple-choice
andappreciated the instant feedback and the chance to improve their scores.Background/IntroductionGrading of 3D solid models can be a time-consuming task. Baxter and Guerci usedSOLIDWORKS macros to grade 3D CAD files [1]. Kirstukas developed a file comparisonprogram in Visual Basic to evaluate Siemens NX solid model files [2]. Ault and Fraser createdan automated grading system for Creo files, which checked for the number of each feature typeand overall geometry. [3] Garland and Grigg compared human and software grading in anengineering CAD course [4] using Graderworks [5], which Dr. Garland developed. He hascontinued improving the product and has become a Certified Solution Partner forSOLIDWORKS [6]. Graderworks can compare geometric properties such as
the ethical use of AI. Additionally, faculty hiring trends in STEMfields have brought in faculty who have access to and experience in using “toolboxes” such as AI,machine learning, data science and cybersecurity to enhance their research. Furthermore, to helpcontextualize academic research needs at comprehensive institutions, many university libraries areadding faculty positions with specific aims including data science, copyright / intellectual property;virtual / extended reality and AI / emerging technologies to support research in critical areas suchas autonomy, advanced materials, big data, cultural geography, linguistics, discovery and digitalhumanities.Aside from formulation of the algorithms behind LLM’s [1], a great deal of dialogue
foradministrators. Another interesting result is that funding is the greatest barrier faced by allinvolved in primary and secondary CER, regardless of role.Implications. Our findings provides insight into why there is minimal research studying certain 1Gransbury, Heckman, McGill, DeLyser, Rosato ASEE 2024topics and groups. To address these barriers, the CER community can focus on creating materials,workshops, and professional development initiatives to inform researchers about resources as wellas methods for mitigating these barriers.1 IntroductionThe addition of computer science (CS) into primary and secondary schools (K-12) had led to thegrowing field of K-12
comprehensive coverage ofpervasive computing cybersecurity allows students to learn state-of-the-art research findings, gainhands-on experiences with recent software, and engage with cutting-edge cybersecurity technol-ogy. Finally, we share the lessons we learned from our study, make ReScuE lab materials availableto the public, and aim to benefit the broader audience of cybersecurity education.1 IntroductionAs a growing computing paradigm, pervasive computing allows devices to interconnect and un-derstand their surroundings with minimal human intervention. With the empowerment of high-performance cloud infrastructure and low-cost network connectivity, pervasive computing canperform collaborative jobs by collecting and analyzing data and communicating
exercises immediately.The purpose of this work is to attempt to understand the effect of additional examples andexplanation in an online, free, voluntary, online, asynchronous Python programming course toimprove student learning and engagement with the material.1. IntroductionThere is high demand for software developers, and this leads to demand for education related tosoftware development. Unfortunately, it can be difficult to learn these skills – especiallyprogramming and how to effectively use a programming language. This can be even morechallenging in a free, online environment where students have not paid to participate and are notbound by the threat of failure on their permanent record. Students must be self-directed and wellsupported in
, Morgan State University ©American Society for Engineering Education, 2024Exploring the Impact of Exposing Command Line Programming to Early CS Majors (An HBCU Case Study)AbstractLearning to program is an essential part of developing computational skills amongst computerscience (CS) majors. Yet, CS majors can encounter programming as a barrier and in many casesleave the field altogether. The learning process that CS majors encounter while developing theirprogramming skills is multifaceted. They are expected to: 1) grasp necessary programmingconcepts, paradigms, and data structures, 2) become adept with employing the appropriate syntaxand semantics for a given programming language used for code
interaction with simulationprograms can vary from something as simple as text input to more advanced methods such assoftware-implemented sliders or graphical “what-you-see-is-what-you-get" input interfaces.Computer-based simulations as an augmentation to traditional narrative course materials (e.g.print or digital textbooks) can be an important resource in an active learning environment. GenAlpha students (those born in or after 2010), often referred to as “digital natives,” have neverknown a world without an iPad. Many have also experienced education with one-to-one (1:1)device or technology programs in place. A 2017 report [1] found that more than 50% of K-12teachers taught in 1:1 classroom environments and a meta-analysis of 15 years of
. The overall survey data indicatedhigh rates of correctness and helpfulness in the Bot responses. We found that hallucination wasnot common, and most incorrect responses were identifiable by students. The Bot also performedbetter than general purpose bots for project-specific help.Our experience can provide insights for faculty using GenAI to assist students in their courses. Acustomized chatbot can be helpful to students and augment traditional course resources.2 Introduction and Related WorkGenerative AI tools, such as ChatGPT [1], have become increasingly prevalent for studentsthroughout the past year [2][3]. A study has shown that the use of ChatGPT in education has had apositive impact on students’ learning and educators’ teaching, with
1 Department of Electrical and Computer Engineering, Mississippi State University 2 National Taipei University, and Tainan National University of the Arts 3 Department of Civil and Mechanical Engineering, Purdue University Fort WayneAbstractComputer Architecture course can be a particularly challenging and intimidating subject forcomputer science, electrical and computer engineering students in engineering and computerscience disciplines. It mainly addresses structures in modern microprocessor and computersystem architecture design. Among them, MIPS instruction set design is a challenging portion inthe learning curve. In this paper, our recent experiences in applying an
seemed to bemismatched with the enthusiasm and excitement of AI from students. Only a few months later,the university encouraged the use of creatively incorporating LLMs in the classroom to fosterlearning and increase students’ awareness of the limitations of the tools. In a technologydepartment especially, instructors falling behind the curve of digital literacy may impactstudents’ satisfaction with their education. Future work should be done to understand howuniversity guidance impacts faculty beliefs and how that translates to pedagogical techniques andlearning outcomes.1. IntroductionLarge language models (LLM) and their conversational counterparts like ChatGPT have causedconcern among teachers but excitement among students in the past year
-teaching in the STEMmethods courses in teacher education.IntroductionThis full paper on computer supported pedagogy serves as a medium of exchange for innovativeapplications of educational technologies in education. We report the findings of evidence-basedresearch [1] to inform curricular and pedagogical initiatives for students and teachers’development particularly in the context of the post-secondary Initial Teacher Education (ITE)programs in STEM (science, technology, engineering, and mathematics) education.The paper describes what we have learned from using these innovative technologies in methodscourses with preservice STEM teachers in Canada during the COVID-19 pandemic. Ouroverarching research focus revolves around the question: What did
use pre-programmed coding blocks to build things from their imagination.Researchers and educators have considered it potentially transformative for fostering learningand cognitive skills [6]. Since an early version of Minecraft in 2009, millions of childrenworldwide have spent hundreds of thousands of cumulative years playing the game [9].Minecraft Education [8] is a learning platform based on Minecraft, where players can learncomputer programming and create their own Minecraft games, called "worlds," throughprogramming. We chose to use Minecraft Education as the platform for our socio-cultural gameand programming environment because: 1) Minecraft is one of the most popular game platforms for children aged 6-14 [6]. This age range
an open-ended project assignment, we conclude that in its current freeversion, ChatGPT was able to provide correct solutions about 66% of the time with the prompt asgiven ‘as-is’ in the assignment. However, the solutions to the AP course assignments were notcorrect all of the time, and occasionally the solution includes a fatal flaw that someone who doesnot know basic coding would not be able to identify or correct. This poster includes conclusionsand recommendations from a high school student’s perspective.1 IntroductionA big problem has appeared in the world of computer science education and that is the use ofChatGPT in introductory computer programming courses. ChatGPT can quickly generate aresponse to almost any computer programming
. eNotebookis a comprehensive online notebook app that enables students to develop study strategies andevaluate their performance for corrective measures. This study proposes to 1) pilot testeNotebook’s AI-enhanced study features and 3) investigate how well eNotebook enhances self-regulatory efficacy in Ecampus and hybrid STEM courses. eNotebook was designed anddeveloped by the PIs and EECS collaborators based on the prototype feedback of 140 Universitystudents during the Fall 2022 term. The results of this study are expected to inform AI-enhancedstudy methods and self-regulatory efficacy in Ecampus and hybrid STEM courses.Keywords: AI-study app, STEM, personalized learningIntroductionAlthough face-to-face STEM education has a 60% retention rate [4,5
a timely manner that adds to their stress levels.These limitations make the task of locating an unfamiliar classroom not only time-consumingbut also problematic for students who need to quickly locate their classroom for quizzes orexams. One of the existing solutions is ClassFind [1], a website that offers indoor navigationthrough the use of real photos and textual explanations. However, it comes short in terms ofcritical features that allows to navigate conveniently and accurately. In particular, ClassFindcannot: • Display each room location within each floor. • Determine user current location of the user. • Map or search offline.We have designed and implemented a mapping system called CampNav. It is an
anefficacious mechanism for fostering a dynamic feedback loop, thereby ensuring the continualrefinement and authenticity of academic courses within esteemed institutions.Background Investigation into the use of conceptual videos as supplementary tools in a first-yearprogramming course is informed by the systematic review titled "Blended Learning Models forIntroductory Programming Courses." The study classified models into five types, includingFlipped, Mixed, Flex, Supplemental, and Online-Practicing models, with a focus on enhancingthe learning experience of novice programmers [1]. These discoveries emphasized the effectiveness of blended learning approaches, with theMixed model, known for flexibility, showing potential for improved student
usability score (A+)for these platforms.1. IntroductionSeveral studies have used educational robots to teach STEM concepts [1]–[3]. Robotics is amultidisciplinary topic that can be integrated into several engineering and engineering technologyprograms. Constructing a robotics lab provides educational opportunities for undergraduatestudents to learn programming, mechatronics, and other skills. Hands-on experiential learning isan essential component of any engineering and engineering technology programs. Replication ofindustrial robotic platforms can help students receive hands-on experiences aligned with industrialpractices. However, these platforms are usually costly [4], [5]. Achieving an affordable-reconfigurable-industrial setup can be
learning experiencesbased on learners’ goals and performance criteria [1]. To support ISD, numerous AI sites areemerging to support educators in the design process from learning objective creation to lessonplanning to assessment development [2], [3], [4].Recent studies have explored or demonstrated how GAI tools could streamline and enhanceinstructional design. Thompson et al. [5] predicted that integrating AI into course design will“lead to enhanced student learning outcomes, engagement, active participation, and learningapproach.” Chng [6] compared current methods of design (human-only) with an AI-enabledapproach and noted AI’s potential to improve the design process: “The introduction of AI intohuman processes has the potential to streamline
with ’SAMCares: An Adaptive Learning Hub’ Syed Hasib Akhter Faruqui1,∗ , Nazia Tasnim2 , Iftekhar Ibne Basith1 , Suleiman Obeidat1 , Faruk Yildiz1 1 Engineering Technology, Sam Houston State University 2 College of Education, University of Texas at Austin ∗ Corresponding AuthorAbstractLearning never ends, and there is no age limit to grow yourself. However, the educational land-scape may face challenges in effectively catering to students’ inclusion and diverse learning needs.These students should have access to state-of-the-art methods for lecture delivery, online
revealed 99.7% of matches as valid, indicating mentors and mentees sharedtwo or more similarities.IntroductionSuccessful mentorship in engineering education by Akerele, Vermeulen, and Marnewick [1],demonstrates the pivotal role of mentorship in transforming theoretical knowledge into practicalskills. A study published in the International Journal for Academic Development indicates thatstudents with mentors exhibit a higher likelihood of successfully completing their engineeringdegrees and actively pursuing careers in the field [2]. In response to the limitations of existingresources, MentorMate introduces an automated solution with a matching algorithm aimed atsimplifying and expediting the mentorship process. Our objective is to devise an
relevant, basedon the input it receives. Released on November 30, 2022, ChatGPT represents a significantadvancement in Natural Language Processing (NLP). NLP is a specialized field in AI thatfocuses on enabling machines to understand, interpret, and even replicate human language in away that is both meaningful and accurate [1]. This technology enables ChatGPT to perform avariety of tasks, such as answering questions, writing essays, or even composing poetry, all byinterpreting and responding to the prompts given by users. In the context of engineering education, the adaptation to evolving technology andpedagogical methods is vital for keeping pace with the latest technological advancements andmeeting the evolving needs and demands of the
Using Generative AI for Reducing Faculty Workload in Online Engineering CoursesAbstractWIP. The demand for high-quality online engineering courses and credentials is surging, drivenby the upskilling and reskilling needs of industry partners and engineers with 8-10 years ofexperience. Creating accessible, top-tier online courses requires producing exceptional videos,transcripts, and content segmented appropriately for optimal student learning [1]. Beyond lecturepreparation, faculty are often tasked with creating well-designed slides and assessments toengage students and measure learning. Despite the support of instructional designers in manyinstitutions, this multifaceted process presents a significant challenge for