?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
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
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
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
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
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
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
Group B Group A Group B Group B Group A Group B Group ASelf-reporting data collection to understand the student and faculty perspective onanonymous grading.Once we have successfully tested out our platform for anonymous grading, we would like tosurvey students for their perception of the tool and its efficacy. We believe that anonymousgrading will have a positive reinforcement effect on students as it, by definition, implies that nofactors other than the solution of the exam will be used for grading. To test this hypothesis, wewill use a questionnaire on student perceptions of anonymous grading and reflections on theirperformance. Specifically, we will ask the
exploratory study aims to discover temporal patterns that illuminate group problem-solvingbehaviors. It is important to emphasize that our analysis is conducted at the group level sincestudents submit assignments and receive credits collectively. As a result, all log traces within thesame group are aggregated to derive group-level submission patterns. Specifically, we focus onpatterns derived from the time spent on each submission attempt, employing sequential patternmining techniques to identify patterns potentially reflecting group problem-solving strategies.Our analytical pipeline comprises the following steps:1. Submission LabelPrairieLearn platform supports two types of saving events: students can either save currentprogress for later
teaching linear algebra that have shown success and promise [5]. Theemerging area of inquiry oriented linear algebra (IOLA) has undergone many iterations to itspedagogical practice by applying a design based research practice and provides an empiricallytested curriculum for linear algebra instructors [6].1.1 Inquiry Oriented Linear AlgebraThe IOLA curriculum draws on RME instructional design heuristics to guide students throughvarious levels of activity and reflection on that activity to leverage their informal, intuitiveknowledge into more general and formal mathematics. The first unit of the curriculum, referred toas the Magic Carpet Ride (MCR) sequence, serves as an example of RME instructional design.Specifically, the tasks reflect four
Exercise DescriptionThe robotic platforms were used in an operating systems and systems programming course at PennState Behrend as a part of a lab exercise to demonstrate concepts related to task design, timing,synchronization, and mutual exclusion mechanisms. The exercise was divided into sections:Introduction to the robotic platform operation, task design using timing and synchronizationmechanisms, and feedback and reflection on the lesson learned.The tudentts were first introduced to the basic operation of the robotic arm using manual controland Application Programming Interfaces (API) control through a Python control program. Thechallenges of moving the arm in space using different coordinates and keeping track of the arm’sposition were
opportunity between engineering and the arts through thedevelopment of a “Special Topics: Interactive Fiction” course was developed and subsequentlyapproved by the curriculum committees of both colleges for the 2022-2023 academic year. Whilethe remainder of this paper focuses on this Interactive Fiction course, the authors want toacknowledge the key roles played by the instructors involved in these preceding courses.2023 - Interactive Fiction: Goals and LogisticsThe two primary goals for the Interactive Fiction course were (1) for students to learn how to usea natural language software platform, such as Inform [30], to design an interactive game in a waythat reflects the diversity of cultures and experiences encountered during the era of
19.3% Nursing 12.5% Psychology 11.9% Psychology 8.8% Nursing 10.4%Programming experience Programming experience No prior prog course 78.5% No prior prog course 80.0% No/very little Python 74.1% No/very little Python 88.0%Note: NB: Non-binary, SD: Self-described, PNR: Prefer not to respond, HI: Hawaiian, PacIsland: Pacific Islander, prog: ProgrammingDemographic data for student participants can be found in Table 1. The race and ethnicity profileof the sample broadly reflects that of the California community colleges from which studentswere recruited. We next evaluated
students’ willingness to reflect on their understanding, to identify misconceptions andareas of deficiency, and to make adjustments to improve learning and performance [1], [11],[12]. Constructive well-designed feedback has also been shown to improve student motivationand self-efficacy beliefs [13], [14]. Academic integrity research argues that meaningfulsupportive feedback empowers students, reducing their likelihood to cheat [15]. Educatorsadopting formative feedback as an instructional intervention too can benefit from the process, asit can offer them valuable insights into students’ understanding of the subject material to helpinform their pedagogy [16], [17].While most of the earlier research focused either on feedback to students as a
of applications that were introduced in the workshop.Upon completion of the workshop, the participants were given an eight-question exit post-trainingsurvey shown in Figure 2. There were six quantitative questions using a five point or a three-pointLikert scale as well as two qualitative questions. The two qualitative questions were also used aspedagogical tools based on experiential learning best practices. Question 7’s goal was to elicit apositive self-reflection while Question 8 reinforced learning through internalization andsummarization. 1. Exiting this workshop, I learned something new about AI concepts, applications, and ethics (1 - strongly disagree to 5 - strongly agree). 2. I have a better understanding of AI and how to
-URM basedon academic records provided by our institution. Our demographic records define URM as“African Americans, Hispanic Americans, American Indians/Native Alaskans, NativeHawaiians/Pacific Islanders (excluding Asian Americans), and multi-racial students identifying atleast one of previously listed URM categories.” The academic records provided by our universityalso included an “International” category. Our institution defines international students as “havinga citizenship status of Non-Resident Alien or Alien Under Tax Treaty”.The “International” category includes students with a broad and diverse range of experiences.“URM” and “non-URM” are contextualized terms that reflect the lived experiences of domesticstudents. Thus, we eliminated
ensure that students learning can perform effectively in a professionalsetting [5, 6]. Due to this factor, there have been several methods designed to aid in studentlearning especially in engineering education, such as active learning [7–11], project-basedlearning [12, 12–16], inquiry-based learning [17].Active learning has been aware of improvement of students’ affect toward engineering educationin support of meaningful engagement with computer engineering concepts and practices [7].Compeau et al. [8] developed an active learning pedagogy in engineering electromagneticscourse, in which engineering students are actively engaged in learning through specially designedactivities, followed by reflection upon. A teaching plan is elaborated in [9
Activity, students participated in the in-class activity by having defined roles and responsibilities. Some students were responsible for the oral presentation, others had to identify new discoveries in the content, while others needed to contextualize the events in their research.• Peer Assessment and feedback: Providing and receiving assessments from one’s peers can provide a variety of benefits for students involved in the peer assessment process. Students may have the opportunity to reflect, self-assess, and co-construct subject matter knowledge. Students’ confidence in the subject matter may also increase [10]. While these benefits have not been found to be universal, this study utilized collaborative learning
revisions and expansions to the lab environment. For instance, in the case of anotherelectronics course that utilizes different versions or manufacturers of instruments, developers caneasily extend the virtual lab to accommodate these new requirements with minimal changes. Thisprimarily involves updating the graphical model to reflect the different appearances of the newinstruments. Additionally, the physical model may be modified if there is a need to adjust thesimulation of the instruments’ physical behaviors, such as their interactions or movements withinthe virtual space. The functional model often remains unchanged, as the core functions of similarinstruments in electronics labs typically stay consistent. Figure 3 illustrates the three types
limitation is mostlikely due to the FPGA’s ability to connect two ALMs during the routing process, where a wirewith a width larger than 1120 cannot be connected between two ALMs. The data we report onlygoes up to a maximum bit-width of 1024, so this limitation is not reflected in our graphs. Also,the Goldschmidt divider has a smaller range than the other dividers because it was not able tosynthesize above a width of 244. This is due to the limited number of DSP blocks.4.1 AreaThe FPGA used in these tests is the 5CGXFC9E7F35C8 from the Cyclone V line. This FPGA ischosen due to its large amount of available ALMs and DSP blocks. The maximum ALMs that ourFPGA has in this study is 113,560. Very few dividers in this study approached this maximumnumber of