|Title:||Underrepresented Student Learning in Online Introductory STEM College Courses|
|Principal Investigator:||Perry, Michelle||Awardee:||University of Illinois, Urbana-Champaign|
|Program:||Postsecondary and Adult Education [Program Details]|
|Award Period:||3 years (07/01/2018 - 06/30/2021)||Award Amount:||$1,399,194|
Co-Principal Investigator: Bhat, Suma; Anderson, Carolyn; Angrave, Lawrence; Bosch, Nigel
Purpose: The project explores the interaction among various characteristics of online instruction and postsecondary students' persistence in STEM courses. In particular, it explores how students traditionally underrepresented in STEM (e.g., women, first-generation students, minorities) benefit from or are impeded by online features (e.g., course videos, discussion boards). Online instruction has the potential to make course content more accessible to a larger number of students, thereby strengthening the STEM pipeline. However, instructors and administrators need to know if certain online pedagogical content or approaches create barriers so that they can provide better advising and supports to students who are considering online courses or (re)design online courses to serve all students.
Project Activities: Using data-mining and statistical analytic approaches, the researchers will explore correlations among various course design features, instructor characteristics, and student background factors with near- and long-term postsecondary success.
Products: Researchers will produce a theory of postsecondary online STEM instruction and peer-reviewed publications.
Setting: The research will be conducted at a large, Midwestern university.
Sample: The sample will include undergraduate students enrolled in large online classes (approximately 50 students per class). Approximately 124 classes will participate over 7 semesters, leading to data from about 5000 students.
Malleable Factors: Multiple factors may influence how long students persist in an online environment and whether they succeed. The three main factors this project considers are course design, instructors, and students. Relevant course design features may include the number, length, and quality of course videos; the use of online discussion boards; and course quizzes. Instructor factors may include teacher attributes (e.g., full-/part-time, years of teaching experience), teacher behavior (e.g., how engaging or open the instructor seems in course videos), and teaching processes (e.g., use of wait time and questions). Student characteristics may include background characteristics (e.g., sex, minority status), behavior (e.g., amount of time spent with course content, number of posts or quiz attempts, other course enrollment), and preparation (e.g., previous knowledge of course content, familiarity with online courses).
Research Design and Methods: The researchers will use data-mining techniques as well as content-analysis approaches to explore possible connections across the various factors. They will collaborate with experts to create scoring rubrics for analyzing the course design and instructor characteristics (e.g., how to rate instructors' openness). They will leverage software, such as OpenFace, to track instructor smiling and linguistic analyses of discussion board posts to track online, student rapport building. They will collect and code course syllabi as well as department and university guidelines and policies regarding online courses. They will combine these data with administrative data and analyze whether different combinations appear to be more beneficial for any or all students. This analysis will consider STEM fields independently (e.g., looking across science courses separate from the analysis of math courses) and STEM courses as a whole.
Control Condition: Due to the exploratory nature of this project, there is no control or comparison condition.
Key Measures: The researchers will use administrative data for student background (e.g., gender, underrepresented status, first-generation college status) and outcome measures (e.g., grades, retention). They will use log data for measures of student engagement with online course content (e.g., time-sequenced clickstream interaction data, discussion board use) and researcher-developed coding rubrics for measures of course design features and instructor characteristics (e.g., lecture features).
Data Analytic Strategy: The researchers will use multiple modeling strategies, including hierarchical cluster analysis, logistic regression, survival analyses, and proportional odds models.
Henricks, G. M., Bhat, S., & Perry, M. (2021). Gender and gendered discourse in two online STEM college courses. Computer-Based Learning in Context, 3(1), 1–16.
Angrave L., Jensen K., Zhang Z., Mahipal C., Mussulman M., Schmitz C., Baird, R., Liu H., Sui R., Wu M., & Kooper R. (2020). Improving student accessibility, equity, course performance, and lab skills: How introduction of ClassTranscribe is changing engineering education at the University of Illinois. Proceedings of ASEE Annual Virtual Conference 2020.
Angrave, L., Zhang Z., Henricks G., Mahipal C. (2020). Who benefits? Positive learner outcomes from behavioral analytics of online lecture video viewing using ClassTranscribe. SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1193–1199). Portland, OR: ACM. Full Text
Bosch, N., Crues, R. W., Shaik, N., & Paquette, L. (2020). "Hello, [REDACTED]": Protecting student privacy in analyses of online discussion forums. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020) (pp. 39–49). International Educational Data Mining Society. Nominated for Best Paper.
Bosch, N., Huang, E., Angrave, L.C., & Perry, M. (2019). Modeling improvement for underrepresented minorities in online STEM education. In G.A. Papadopoulos, G. Samaras, S. Weibelzahl, D. Jannach, & O. Santos (Eds.), Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (pp. 327–335). New York, NY: ACM. Full Text
Henricks, G., Perry, M., & Bhat, S. (2020). Gender and gendered discourse in two online STEM courses. In M. Gresalfi & I. S. Horn (Eds.), Proceedings of the 14th International Conference on Learning Sciences (ICLS) 2020, Vol 2. (pp. 811–812). Nashville, TN: International Society of the Learning Sciences.
Huang, E., Valdiviejas, H., & Bosch, N. (2019). I'm sure! Automatic detection of metacognition in online course discussion forums. Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII 2019), 241–247. Full Text
Jay, V., Henricks, G., Bosch, N., Perry, M., Bhat, S. P., Williams-Dobosz, D., Angrave, L. C., & Shaik, N. (2020). Online discussion forum help-seeking behaviors of students underrepresented in STEM. In M. Gresalfi & I. S. Horn (Eds.), Proceedings of the 14th International Conference on Learning Sciences (ICLS) 2020, Vol 2. (pp. 809–810). Nashville, TN: International Society of the Learning Sciences.
Valdiviejas, H., & Bosch, N. (2020). Using association rule mining to uncover rarely occurring relationships in two university online STEM courses: A comparative analysis. Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020) (pp. 686–690). International Educational Data Mining Society.
Williams-Dobosz, D., Azevedo, R., Jeng, A., Thakkar, V., Bhat, S., Bosch, N., & Perry, M. (2021). A social network analysis of online engagement for college students traditionally underrepresented in STEM. In M. Scheffel, N. Dowell, S. Joksimovic, & G. Siemens (Eds.) The impact we make: The contributions of learning analytics to learning, Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK21) (pp. 207–215). New York, NY: ACM. https://dl.acm.org/doi/pdf/10.1145/3448139.3448159
Zhou, J. & Bhat, S. (2021). Modeling consistency of engagement patterns in online courses. In M. Scheffel, N. Dowell, S. Joksimovic, & G. Siemens (Eds.) The impact we make: The contributions of learning analytics to learning, Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK21) (pp. 226–236). New York, NY: ACM. https://dl.acm.org/doi/pdf/10.1145/3448139.3448161