In 2021, two projects were funded totaling to $29,818.00
Developing Culturally Relevant Pedagogy to Improve the Enrollment and Retention of Underrepresented Students in Computing Programs
Shamima Mithun, epartment of Engineering and Technology, Senior Lecturer, firstname.lastname@example.org
Abstract: The intersection of culture and education has long been purported to aid in student interest and motivation to improve enrollment and retention rates, specifically for underrepresented learners. Although the culturally relevant pedagogical framework has been utilized over the years in education, the literature lacks detailed guidelines and or suggestions on how this may be incorporated into computing programs. For this study, we seek to explore the efficacy of incorporating culturally relevant pedagogy into the curriculum of undergraduate computing courses. This research aims to establish an inclusive academic community of mentor/mentee relationships and culturally relevant assessments to engage underrepresented learners and ultimately improve the enrollment and retention of underrepresented students within predominantly white institutions (PWIs). We posit that this research may also serve to guide the structure of other STEM-oriented programs within higher education.
Through this exploratory research, we seek to answer the following two research questions: (1) How do culturally diverse mentor-mentee relationships impact enrollment and retention of underrepresented students in computing programs? (2) How does the integration of culturally meaningful assessments in the form of projects influence the motivation, perceptions, performance, enrollment, and retention of underrepresented students in computing programs? This would include the development of a mentorship program and the design and implementation of assessments relevant to students’ culture and lived experiences, which are intended to improve enrollment and retention of underrepresented students in computing programs. To evaluate the effectiveness of our approach, students’ enrollment rate and retention rate over two years and performance data in CIT program will be analyzed. Interviews and surveys will also be conducted and qualitative and quantitative analysis will be performed.
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Analysis of digital course materials and student personal technology in STEM courses: Developing instructional design to support 21st century student learning
Robert Elliott, Department of Engineering and Technology, Teaching Professor, email@example.com
Abstract: The transition to online learning during the COVID-19 pandemic uncovered a disconnect in the digital learning tools in use by institutions of higher education and the technology practices and devices available to their students. Although analytic data is collected in almost every interaction between a student and their digital course content, examination of that data is often conducted without regard to the individual who is using the tool. In addition, instructional design decisions are made with an incomplete understanding of the devices used and preferences of students.
This study proposes the analysis of learning analytics in conjunction with demographic data related to the students. We will use machine learning algorithms to define various subgroups of students based on their demographics and the computing devices they use and will then examine those groups’ activities in the Canvas LMS to learn if differences in their behavior exist. After building a deeper understanding of the actions and habits of students, the investigators will then test pedagogical changes aligned with the discovered student behaviors to determine if we can improve student success and satisfaction in large-enrollment, general education STEM courses.
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