in engineering andcomputer science courses. Written solutions document students’ thought processes, but theremay be other thinking and reasoning that the instructor cannot observe from a solution alone.The pedagogical technique reported in this paper is the use of video reflections of solutions toexam problems. Students created one short video explanation of their solution to a randomlyassigned exam problem for each exam. The educational objectives for the video included: 1)encourage reflection and meta-cognition about the creation and testing of a solution, 2) practiceoral communication of technical process.From 2021 to 2023, students in three different computer science courses took exams and createdvideo recordings of their solutions. The
Paper ID #38248Board 63: Work in progress: Uncovering engineering students’ sentimentsfrom weekly reflections using natural language processingMr. Ahmed Ashraf Butt, Purdue University at West Lafayette (COE)Dr. Saira Anwar, Texas A&M University Saira Anwar is an Assistant Professor at Department of Multidisciplinary Engineering, Texas A &M Uni- versity. Dr. Anwar has over 13 years of teaching experience, primarily in the disciplines of engineering education, computer science and software engineering. Her research focuses on studying the unique con- tribution of different instructional strategies on students
?In particular, we first use answers to Questions 1 - 3 to address RQ1. Then, we use the results ofQuestions 4 - 6 and pre- and post-lab questionnaires to address RQ2. Finally, we use answers toQuestions 7 - 15 to address RQ3 because we think they reflect students’ needs, which will help usimprove the quality of lectures and hands-on labs.6 Results of Assessment (a) Question 1 (b) Satisfaction Trend in Institution 1Figure 6: Aggregated students’ responses to Questions 1 and the satisfaction itrend in Institution 16.1 Research FindingsTo demonstrate our findings and answer RQs without losing generality, we chose four labs weconstantly offered students. To answer RQ1, we conducted the
recognized by two best paperProf. Matthew West, University of Illinois Urbana-Champaign Matthew West is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. Prior to joining Illinois he was on the faculties of the Department of Aeronautics and Astronautics at Stanfo ©American Society for Engineering Education, 2024 Reflections on 10 years of operating a computer-based testing facility: Lessons learned, best practices1 IntroductionAssessment is an integral component of any educational experience, but it is also a practice thatbecomes increasingly difficult for faculty to implement well as class enrollments
Paper ID #42399Board 62: Work in progress: A Comparative Analysis of Large LanguageModels and NLP Algorithms to Enhance Student Reflection SummariesDr. Ahmed Ashraf Butt, Carnegie Mellon University Ahmed Ashraf Butt has recently completed his Ph.D. in the School of Engineering Education at Purdue University, where he cultivated a multidisciplinary research portfolio bridging learning science, Human-Computer Interaction (HCI), and engineering education. His primary research focuses on designing and developing educational technologies that facilitate different student learning aspects (e.g., engagement). Further, he is
another's work either synchronously or asynchronously.Using a qualitative thematic analysis of preservice teachers’ anonymous exit slips and coursereflections, we generated three overarching themes as our key findings. These themeshighlighted the growth and development of preservice teachers' technological, pedagogical, andcontent knowledge (TPACK), reflective practices as future K-12 STEM teachers, and thepromotion of access and equity of educational technology in STEM education. We suggest thatmore longitudinal case studies with quantitative and qualitative analyses are needed to furtherexplore what aspects of STEM preservice teachers’ subsequent teaching practicum might beenhanced by the use of collaborative technologies during the micro
Computer Science (A Case Study)Abstract:As technology continues to evolve and spaces in the field of Computer Science (CS) areexpanding, the promotion of equity, inclusion, and representation for all need to reflect thisevolution and expansion. Even though efforts have been made to address such challenges forwomen and minorities in CS, more work needs to be done. This is especially the case for blackwomen, who account for less than 3% of the tech workforce. As Computer Scientists, blackwomen face regular affronts to their character and capabilities because of their race and gender.While the combination of racial and gender discrimination have spanned over decades for blackwomen in CS, the tech industry, and related spaces, efforts regarding their
card. Of the 83 students in the course,the number of completed surveys ranged from 12 to 18 participants, and we had 69 completesurveys throughout the semester. Rojas did not have access to the survey data until the end ofthe semester, but Quan occasionally shared broad patterns as formative feedback during thesemester.To capture the instructor's perspectives on the course as well as how the implementation ofmastery grading shifted over time, Rojas engaged in regular reflective journaling. We alsocollected documents and artifacts associated with the course including emails to and fromstudents which discussed mastery grading and syllabi from the focal semester and previoussemesters. We also viewed student course evaluations administered by the
conducted in2023 [8] offers a granular perspective on the implementation of these platforms in a traditionally non-digital sector.This work is seminal in discussing the operational efficiencies and innovative prospects afforded by low-codeplatforms, as well as addressing the potential drawbacks that may arise from an over-dependence on said platforms. At the same time, another work [9] that takes a multidisciplinary approach provides a retrospective view of theevolution of low-code platforms, elucidating their strategic integration with ERP systems. It reflects on thehistorical progression from model-driven development to the current state where low-code platforms are essentialin enhancing business processes, fostering agility, and enabling
phases: planning, monitoring, control, and reaction andreflection [3], [8]. The planning phase involves planning for the problem such as guidingquestions, making a concept map, or planning ahead as seen in [1, Tab. 1], [3]. The monitoringphase could have diagrams, prompts for self-explanation or reasoning, or cognitive feedbackdone by the student [3], [12]. In the control phase, there could be worked out examples,processing and reflective prompts, or guiding questions [3], [10]. Lastly, in the reflection phase,students reflect on the learning they’ve done [3], [13]. As previously mentioned, effectivescaffolds can be both domain-general and domain-specific in each phase. In the context ofcomputer-based learning environments, or CBLEs, prompts
of debuggingand fixing errors in the code. Finally, looking back or reviewing is when one reflects on the finalproduct, thinking metacognitively about the entire process to improve upon the steps taken forfuture problems.General coding mistakes is one of the large barriers to success for students with no programmingexperience. Prior studies exploring student problem solving primarily focused on students’coding, debugging, and errors. These studies show that most errors can be categorized into ahandful of common errors that students with no prior experience make [9], [10], [11]. Focusingon these errors to find better ways to prevent students from making them is an importantendeavor. However, these errors do not solely come from coding itself
example, a Building Information Model [8]. High schoolstudents need to primarily learn 2D geometry, but 3D geometry can be used as motivation and aneventual goal. Besides standard motions (translations and rotations), the virtual model can beused to study scaling (dilations), reflections and shears which are not possible with the physicalmodel. For example, a 2D reflection can be implemented by lifting a triangle up out of the 2Dplane into 3D space, flipping it over and putting it down again in the plane [9]. From aneducational technology design and development perspective, the team’s general theoreticalcontext and learning sciences framework includes several key components, which havecollectively demonstrated effectiveness during previous NSF
aligns with the targeted age range, 11-18, i.e., middle and high school age, of our broadening education intervention. It is highly likely that these students either play or played Minecraft games. They may either be interested in Minecraft or have fond memories of it. Their positive experience with Minecraft could serve as a foundation for developing an interest in computer programming. 2) Minecraft allows us to create a virtual world that reflects reality: the identity of the players and the socio-cultural context. We want these students' identities to be represented to encourage engagement, particularly from underrepresented students. Minecraft allows us to create characters of different races, genders
shown varying levelsof empirical data demonstrating improved student learning [1,19]. One example of a positive impact is fromForte and Guzdail [11], who observed improved motivation and computational thinking when data scienceskills were put into the context of a given major. According to Yardi [16], appropriately formatted andscoped content can enhance conceptual understanding, problem-solving skills, and reflective learningamong other benefits. Other research indicates that both faculty and students are more satisfied with coursesthat adopt this approach, leading to higher course success rates and increased enrollment [20]. However,there is still a need for further research to fully understand the potential impact of contextualized
. For instance: as a personal tutor, aSocratic opponent, a reflective study buddy and idea generator, or an explorer [9]. Moreover,Stanford’s Center for Human-centered Artificial Intelligence (HAI) purports benefits of ChatGPTsuch as allowing teachers and instructors to scale their learning, adapt to individual interests, andimprove learning accessibility—all without fear of peer judgment [10]. Of course, though,students can use ChatGPT to cheat. Whether writing essays or answering homework questions,students may be passing off generated text as their own [2], [8]. This requires caution, but thisdisruption can lead to an exciting foray into new skills, new domains, and new meaning behindlife, work, and education [11].3. Conceptual FrameworkThis
direct reflection of unfavorable perceptions and stigmas that have plagued thefield of Computing for some time as it pertains to race and ethnicity [24]. There have beeninitiatives by tech companies [27, 30], who are making efforts to address this issue aroundretention, especially with underrepresented minorities. Likewise, tech companies have begunworking closely with minority-serving institutions in efforts to provide insight on the type ofcomputational skills and programming proficiency a student (or prospective employee) mustpossess for success in these sectors [11, 22, 33]. One anecdotal and common insight from theirobservations concerns a candidate’s ability to exhibit proficient critical thinking skills to solveproblems through technical
disposition towards command line programming, which wasalso reflected in their initial struggle to adjust to using a command line tool. On the other hand,the OOP students showed a better performance and disposition towards command lineprogramming, but this could have been influenced by acquired experiences both prior andexternal with using such tools.1. IntroductionDeveloping ways to effectively teach early computer science (CS) majors how to program hasbeen an important topic of interest for some time. When addressing student learning in earlyprogramming courses, there have been a variety of elements researched and observed, notableones being: 1) the type of paradigms that are ideal for introducing students to programming [1],[2], [3], [4], 2) the
to”, “I believe this class could beof some value to me” and “I believe doing this class is important”.The Index of Learning Styles [8] is a survey instrument used to assess preferences onfour dimensions (active/reflective, sensing/intuitive, visual/verbal, and sequential/global).The instrument was developed and validated by [8]. Users answer 44 a-b questions with11 questions for each of the four dimensions. After answering the question students get ascore for each of the four dimensions that ranges from 0 to 11. for example, the 11 itemsthat corresponded to the Activist/Reflective spectrum were added with a score of 1 if theresponse corresponded to Activist and a score of 0 if the response corresponded to Reflective.Sense of belonging to
learningcommunity (FLC) with a local two-year institution to foster a collaborative community andsupport faculty in adopting APEX materials, which included helping them to consider, plan,apply, and reflect on effective practices for integrating computing into their courses. Buildingupon these pilot efforts, we are actively expanding adoption of the APEX program in severalways. First, we have begun holding summer and winter training workshops for faculty at severaladditional community colleges. Second, we are refining and improving the FLC experience aswe initiate new FLCs with these institutional partners. Finally, we will continue to assess theprogram’s efficacy through a research plan that evaluates student and faculty experiences,allowing us to optimize
engineering education broadly andpedagogy specifically.This study presents an overview of ongoing efforts to integrate GAI as a pedagogical tool at aLand Grant R1 University on the East Coast of the United States. Also, we are hoping to collect awithin-case study of instructors who have successfully implemented artificial intelligence in theirclassrooms and course design. Data will be collected from the instructors through classroomobservations and interviews on their classroom implementation. These will be thematicallyanalyzed. Also, a deep exploration of students' learning experiences using the GAI will beconducted using focus group discussions and end-of-the-semester reflection. Other data sourcesthat will be thematically analyzed include the
ConclusionsA. Student metacognitionMetacognition involves a person critically analyzing their own understanding. Within engineeringeducation, this reflective practice by the student enhances learning and problem solving. Thereare numerous classroom structures or techniques we can use to build these skills. ChatGPTprovides interesting ways for a student to engage with material, and may further a student’sunderstanding of their own learning processes, problem-solving strategies, and perhaps identifyknowledge gaps.The process of initially re-engaging with the test question without the assistance of AI, provided ameans to both reflect on their own work, as well as explore more traditional means of correctingor expanding their original code outside the
suffer from high attrition rates[2] [4] [5]. If factors that improve the chances of student success in this type of course could beidentified, they could be used to reduce attrition rates and improve educational outcomes in amore scalable fashion.The purpose of this research is to understand if identified student attributes and behaviors arerelated to higher levels of success in a free, online, voluntary, noncredit, introductory Pythonprogramming course. The course was developed by the authors and provided to over 900students in several cohorts, with the same general curriculum delivered online via GoogleClassroom over a period of 18 months. Students in these courses were evaluated using multiple-choice quizzes, participation in reflection
interview process, whichcould decrease their success during official interviews.Some CS departments and institutions have identified the need to educate and prepare theirstudents for technical interviews. Yet, there exists a greater disparity for awareness andpreparation when observing many other CS departments and institutions. This disparityrepresents an opportunity to promote the importance and need for technical interview preparationand awareness across the CS spectrum and academy.The nature of this article is to provide a survey of literature reflecting current efforts pertaining totechnical interview preparation initiatives and overall awareness in CS curriculums, CSdepartments, and institutions at large. Key findings reveal that more
classroom modality.In the fall of 2022, first-year ECE students were given a survey about their experiences in bothcourses. The same survey was given to sophomore ECE students, who persisted in the programand complete the aforementioned course sequence one year prior, asking them to reflect on theirfirst-year experience. A quantitative analysis of the Likert scale survey questions and adiscussion of themes present in the student responses are detailed in the next section.IV. Results and DiscussionResulting from 24 responses from students who began their university studies in the fall of 2021and fall of 2022, figure 1 shows a picture of the student experience with respect to usingtechnology for learning. For the survey responses, rarely was defined
Literature Review of Empirical Research on ChatGPT in Education.” Rochester, NY, Sep. 06, 2023. doi: 10.2139/ssrn.4562771.[18] C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,” Educ. Sci., vol. 13, no. 4, Art. no. 4, Apr. 2023, doi: 10.3390/educsci13040410.[19] C. M. L. Phillips, J. S. London, W. C. Lee, A. S. Van Epps, and B. A. Watford, “Reflections on the messiness of initiating a systematic literature review on broadening participation in engineering and computer science,” in 2017 IEEE Frontiers in Education Conference (FIE), Oct. 2017, pp. 1–8. doi: 10.1109/FIE.2017.8190482.[20] L. Krupp et al., “Unreflected Acceptance -- Investigating the Negative Consequences of ChatGPT
Paper ID #43097Student Preferences and Performance in Active Learning Online EnvironmentsMinkyung Lee, Pennsylvania State University Minkyung Lee is a doctoral candidate in the Department of Learning and Performance Systems at Penn State University and serves as a Graduate Assistant at the Leonhard Center, an engineering education center at Penn State. Her academic journey and professional contributions reflect her dedication to the field of educational technology and design.Dr. Stephanie Cutler, Pennsylvania State University Dr. Stephanie Cutler has degrees in Mechanical Engineering, Industrial and Systems Engineering
Python in the introductory computing course. The course topics and learning goalsfor the course were not changed, and course lectures were only changed to reflect the change inprogramming language.This paper compares student achievement between classes that took the MATLAB-based versionof the course and those who took the Python-based version. Students in the two versions weregiven very similar exams and final project problems so that their achievement of course goalscould be compared.This work is the first phase of a longer-term project intended to assess the digital literacy ofWestern Carolina Engineering graduates. Students’ programming skills will be assessed as theyprogress through the four-year engineering curricula. A particular focus of
, Asian, andAfrican American. The parents and children voluntarily walked into our booth. After obtainingparental consent, each child played two episodes of the path-finding game: Game 1 taking five toten minutes and Game 2 taking ten to twenty minutes. Before playing the game, children worethe motion capture jacket and a hat with the assistance of a research assistant. The motioncapture suit was attached by reflective markers to track children’s movements during the session.When children approached the game place, a social robot greeted with utterances which wasinstantly operated by a human operator behind the scene. A social robot expressedencouragement when a kid struggled to finding a next step during the game. Various utterancesof a social
to compare student preferences to outcomes. Theremaining students were randomly assigned to either longer lessons or shorter lessons. Studentperformance was evaluated through quizzes, assignments, reflection exercises, and a final exam.Other than the inclusion of more explanation and additional examples, the content in the twocourses was identical.In the second cohort, students were randomly assigned to one of three groups. All three groupsreceived ungraded exercises with each lesson in order to evaluate the effect of solutions to theseexercises. The first group did not receive solutions to these. The second group received solutionsto these exercises, but after a delay of more than 12 hours. The third group received solutions tothese
engagement, educational technologies, curriculum design which includes innovative and equitable pedagogical approaches, and support programs that boost the academic success of different groups of students. She teaches in active learning environments and strives to bring EE and CER into practice. ©American Society for Engineering Education, 2024 Equitable Computing Education Abstract The field of computing continues to struggle to increase participation that better reflects the domestic composition of the US society at large. Society could benefit from diversifying its workforce as broader participation would