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
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
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
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
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
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
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
, 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
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
completed a survey to reflect on theirperformance, using the Marino Interview Assessment Scale (MIAS), and answered questionsabout their preparedness and the system. Later, hiring managers (n = 2) watched the videos of theinteractions and rated the students’ performance using the MIAS. We used Mann-Whitney U teststo compare the students’ ratings to those of the external evaluators. We also utilized descriptivestatistics to analyze the closed-ended questions and thematic analysis for the open-endedresponses. Although there was no significant change in self-assessed performance relative toexternal evaluations in hiring scenarios, we observed the need to help students improve theirintroduction and closing in a job interview. Furthermore, 90% of
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
members should look for OER that are peer-reviewed or have undergone some form of quality assurance process. They should also verify that the OER reflects current knowledge and practices in the subject area.3. Relevance: Faculty members should look for OER that are relevant to their courses and the needs of their students. OER should cover the topics and concepts that are required for the course and should be presented in a way that is accessible and engaging for students.4. Currency: OER should be up-to-date, reflecting the latest research and developments in the subject area. Faculty members should look for OER that are regularly reviewed and updated to ensure that they remain relevant and accurate.5. Accessibility: OER should
to students.4.1.2 End of Semester SurveyWe then surveyed students from the Point-Restricted-Policy semester at the end of thesemester.We had the students compare the Poinr-Restricted-Policy course against Time-Restricted-Policycourse. We specifically asked in what ways did a student approach completing the programmingassignments differently between the two courses. Students were instructed to skip the question ifthey did not take the course recently with the time-restricted policy. We asked this question toanswer RQ1 from the perspective of students who have now experienced both policies and areable to reflect on both. These responses also help inform our answer to RQ2, supplementing ourobservations through the lab success measures as we
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
minutes in length, before attending class. During class, the algorithms were reinforced through demos. ii. Encouraging student engagement with the material through in-class discussions and demos. iii. Promoting student reflection by asking them to answer warm-up questions related to the video lectures at the beginning of each class.Furthermore, we employed short accountability quizzes to evaluate students' comprehension andencourage them to complete the video lectures prior to class meetings. These quizzes consistedmainly of multiple-choice questions and were administered through the course managementsystem, Canvas, enabling students to receive immediate feedback on their performance.Programming homework assignments were
withthese steps to maximize the positive impact while using CT to build solutions Figure 5. CT-Foundation-to-Creation ModelEarly childhood educators play an essential role in enhancing the CT experience and increasingstudent understanding of CT. Teachers need to determine HOT question types through solutionbuilding stage (Figure 6). An example of a question for the thinking step could be “I wonderwhat would happen if…?” An example of a testing question could be “Can you show me how touse it...?,” and a question for the self-reflection step could be “What was the most interestingthing you learned here?” An example question for the improvement step could be “What mightyou do differently next time
, malicioussoftware, and cryptography. And for the topics that require improvement, we'll focus on enhancingthe supporting information and explanations for better outcomes.AcknowledgementThis material is based upon the work supported by the United States National Science Foundationunder Grant No. 1903419 and 1903423 through the Security and Trustworthy CyberspaceEducation (SaTC: EDU) program. Any opinions, findings, and conclusions, or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views of theNational Science Foundation. This study was approved by the Institutional Review Board (IRB)at Purdue University Northwest and the University of Toledo under protocol numbers IRB-2020-1119 and IRB-301407-UT
engineering educators to research more holisticstudent networks than previously studied. Results of these future studies may yield moregeneralizable and accurate conclusions about which social practices help students succeed.Acknowledgements This material is based upon work supported by the second author's National ScienceFoundation Graduate Research Fellowship under Grant No. DGE1745048. Any opinions,findings, and conclusions or recommendations expressed in this material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation. References[1] A. Kozulin, Vygotsky’s Psychology: A biography of ideas. Cambridge, MA: Harvard University Press, 1990.[2
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
Figure 1. The main kit components include an Arduino based microprocessor called aRedboard, a motor driver, gear motors, servo motor, ultrasonic distance sensor, TMP36temperature sensor, photocell, Tricolor LCD, assorted color LCDs, buttons, power switch,piezoelectric speaker, resistors, LED display, various wires and wheels. Students begin to learnabout basic circuits, breadboards, programmable microcontrollers and the use of the Arduino IDE.In addition, a robot chassis is provided along with reflective sensors, ultrasonic distance sensorsand servo motors that are used as an initial platform in the robot builds.The process of learning the basics of Arduino is accomplished by completing 3 mini projects whichare outlined as follows. In project 1
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
the explainedcontent, giving the option to repeat the video and the quiz if the student so wishes. In terms of thepractical part, after the students watch the video and take the quiz, they are ready to put intopractice the knowledge acquired using the workstation, the applications and the correspondingpractice board for the course. To support the students in this practical part, step-by-step tutorials(figure 9) have been developed for each practice, which are fully illustrative and realistic, givingthe students the appropriate accompaniment so that the level of confusion is minimum whencreating a circuit. Similarly, to the explanatory videos for the practices, reflective questions(figure 10) have been developed that guide the students in the
students.Limitations and Future WorkThe frameworks must be validated through qualitative research, and the work should beexpanded to include integration pathways.AcknowledgementThis work was funded by the National Science Foundation (NSF) with Grant No DRLGEGI008182. However, the authors alone are responsible for the opinions expressed in thiswork and do not reflect the views of the NSF.References[1] B. Vittrup, S. Snider, K. K. Rose, and J. Rippy, "Parental perceptions of the role of media and technology in their young children’s lives," Journal of Early Childhood Research, vol. 14, no. 1, pp. 43-54, 2016.[2] A. Sullivan, M. Bers, and A. Pugnali, "The impact of user interface on young children’s computational thinking," Journal of Information
structure the meaning of the song. In the CS0 course, Python lists, dicts, and stringoperations are reviewed. In the CS1 course, we review the construction and freeing of memorywhen building singly and doubly linked lists in C++. And in the CS2 course, we review the lists,trees, queues, stacks, heaps, STL, and graph data structures covered in the class.The lyrics of Superstition also reflect the recursive case and base case of the groove flow. Whenhe is playing the “recursive case: groove, repetitive measures, he lists common superstitions.“Very superstitious, Writing's on the wall / Very superstitious, Ladders bout' to fall.” But whenhe plays the “base case” groove, he implores with the listener to not fall for superstitions. As hesings “when you