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
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
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
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
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
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