Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics
DOI:
https://doi.org/10.18608/jla.2018.53.5Keywords:
Educational Technology, Curriculum Analytics, Survival Analysis, Early Warning Systems, Undergraduate EducationAbstract
In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students’ changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the organization of the curriculum) that highlight different trends in student experience of the focal course. Course difficulty, discipline of major, time in semester, and simultaneous difficulty across courses were all significantly related to entering a moderate and high-risk classification in the early warning system (EWS). Course difficulty, discipline of major, and time in semester were related to exiting academic difficulty classifications. We observe a snowball effect, whereby students who are experiencing difficulty in the focal course are at increased risk of experiencing difficulty in their other courses. Our findings suggest that different metrics may be needed to identify risk for academic difficulty and recovery from academic difficulty. Our results demonstrate what a more holistic assessment of academic functioning might look like in early warning systems and course recommender systems, and suggest that academic planners consider the relationship between course co-enrollment and student academic success.
References
Adelman, C. (2006). The Toolbox Revisited: Paths to Degree Completion from High School Through College. Washington, D.C.: U.S. Department of Education.
Attewell, P., Heil, S., & Reisel, L. (2012). What is academic momentum? And does it matter?. Educational Evaluation and Policy Analysis, 34(1), 27-44.
Biglan, A. (1973). Relationships between subject matter characteristics and the structure and output of university departments. Journal of applied psychology, 57(3), 204.
Braxton, J. M., & Hargens, L. L. (1996). Variation among academic disciplines: Analytical frameworks and research. Higher Education. 11, 1-46.
Brown, M., DeMonbrun, R. M., Lonn, S., Aguilar, S., & Teasley, S. D. (2016). What and when: The role of course type and timing in students’ academic performance. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16) (pp. 459–468). New York: ACM. http://dx.doi.org/10.1145/2883851.2883907
Brown, M., DeMonbrun, R. M., & Teasley, S. D. (2017). Don’t call it a comeback: Academic recovery and the timing of educational technology adoption. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 489–493). New York: ACM. http://dx.doi.org/10.1145/3027385.3027393
Brown, M., DeMonbrun, R. M., & Teasley, S. D. (2018). Conceptualizing co-enrollment: Accounting for student experiences across the curriculum. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 305–309). New York: ACM.
Calcagno, J. C., Costa, P., Bailey, T., & Jenkins, D. (2006). Stepping stones to a degree: The impact of enrollment pathways and milestones on older community college student outcomes (CCRC Brief No. 32). New York, NY: Community College Research Center, Teachers College, Columbia University.
Chambliss, D. & Takacs, C.G. (2014). How college works. Harvard University Press.
Dawson, S., & Hubball, H. (2014). Curriculum analytics: application of social network analysis for improving strategic curriculum decision-making in a research-intensive university. Teaching and Learning Inquiry: The ISSOTL Journal, 2(2), 59-74.
Drachsler, H., Verbert, K., Santos, O. & Manouselis, N. (2015) Panorama of Recommender Systems to Support Learning. In: Ricci F., Rokach L., Shapira B. (eds) Recommender Systems Handbook. Springer, Boston, MA, 421-451.
Denley, T. (2014). How Predictive Analytics and Choice Architecture Can Improve Student Success. Research & Practice in Assessment, 9, 61-69.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132.
Feng, Y. Fiorini, S. Shepard, L., & Groth, D. (2017). The Effect of Grades in STEM Courses on Retention. In Proceedings of the 13th Annual National Symposium on Student Retention. Consortium for Student Retention Data Exchange/University of Oklahoma. (p. 416-427).
Heileman, G. L., Hickman, M., Slim, A., & Abdallah, C. T. (2017). Characterizing the complexity of curricular patterns in engineering programs. In Proceedings of the 2017 American Society for Engineering Education (ASEE) Annual Conference, Columbus, Ohio.
Hodara, M. & Rodríguez, O. (2013). Tracking Student Progression through the Core Curriculum. CCRC Analytics. New York, NY: Community College Research Center, Teachers College, Columbia University.
Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. The Journal of Higher Education, 77(5), 861-885.
Knight, S., Wise, A. F., & Chen, B. (2017). Time for change: Why learning analytics needs temporal analysis. Journal of Learning Analytics, 4 (3), 7–17.
Laird, T. F. N. & Garver, A. K. (2010). The effect of teaching general education courses on deep approaches to learning: How disciplinary context matters. Research in Higher Education, 51 (3), 248-265.
Lattuca, L. R. & Stark, J. S. (2011). Shaping the College Curriculum: Academic Plans in Context. John Wiley & Sons.
Lightfoot, J. M. (2010). A Graph-Theoretic Approach to Improved Curriculum Structure and Assessment Placement. Communications of the IIMA, 10(2), 5.
Martin, A. J., Wilson, R., Liem, G. A. D., & Ginns, P. (2013). Academic momentum at university/college: Exploring the roles of prior learning, life experience, and ongoing performance in academic achievement across time. The Journal of Higher Education, 84(5), 640-674.
Mayhew, M. J., Pascarella, E. T., Bowman, N. A., Rockenbach, A. N., Terenzini, P. T., Seifert, T. A., & Wolniak, G. C. (2016). How college affects students: 21st century evidence that higher education works (Vol. 3). John Wiley & Sons.
Mahzoon, M. J., Maher, M. L., Eltayeby, O., Dou, W., & Grace, K. (2018). A Sequence Data Model for Analyzing Temporal Patterns of Student Data. Journal of Learning Analytics, 5(1), 55-74.
Meghanathan, N. (2017). Curriculum network graph: relative contribution of courses. International Journal of Network Science, 1(3), 223-247.
Nam, S., Lonn, S., Brown, T. , Davis, C., and Koch., D. (2014). Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, 16–25.
Nespor., J. (2007). Curriculum charts and time in undergraduate education. British Journal of Sociology of Education 28, 6 (2007), 753–766.
Pascarella, E. T. & Terenzini, P.T. (2005). How college affects students. Jossey-Bass San Francisco, CA.
Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of educational psychology, 106(1), 315.
Phadke, A. S., & Kulkarni, S. S. (2018). Use of Network Model for Analysis of Curriculum and its Mapping to Program Outcomes. Journal of Engineering Education Transformations, 31(3), 30-34.
Seymour, E., & Hewitt, N. M. (1997). Talking about leaving. Westview Press, Boulder, Co.
Slim, A. (2016). Curricular Analytics in Higher Education(Doctoral dissertation, The University of New Mexico).
Slim, A., Heileman, G. L., Kozlick, J., & Abdallah, C. T. (2014A). Employing markov networks on curriculum graphs to predict student performance. In 2014 13th International Conference on Machine Learning and Applications (ICMLA). (pp. 415-418). IEEE.
Slim, A., Heileman, G. L., Kozlick, J., & Abdallah, C. T. (2014B). Predicting student success based on prior performance. In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), (pp. 410-415). IEEE.
Slim, A., Kozlick, J., Heileman, G. L., Wigdahl, J., & Abdallah, C. T. (2014C). Network analysis of university courses. In Proceedings of the 23rd International Conference on World Wide Web (pp. 713-718). ACM.
Slim, A., Kozlick, J., Heileman, G. L., & Abdallah, C. T. (2014D). The complexity of university curricula according to course cruciality. In 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). (pp. 242-248). IEEE.
Tinto, V. (1997). Classrooms as Communities: Exploring the Educational Character of Student Persistence. The Journal of Higher Education 68, 6 (November 1997), 599–623. DOI:https://doi.org/10.1080/00221546.1997.11779003
Trowler, P., & Cooper, A. (2002). Teaching and learning regimes: Implicit theories and recurrent practices in the enhancement of teaching and learning through educational development programmes. Higher Education Research and Development, 21(3), 221-240.
Villano, R., Harrison, S., Lynch, G., & Chen, G. (2018). Linking early alert systems and student retention: a survival analysis approach. Higher Education, 1-18.
Wang, X. (2016). Course-taking patterns of community college students beginning in STEM: Using data mining techniques to reveal viable STEM transfer pathways. Research in Higher Education, 57(5), 544-569.
Wang, Y., Liu, X., & Chen, Y. (2017). Analyzing cross-college course enrollments via contextual graph mining. PloS one, 12(11), e0188577.
Willcox, K. E., & Huang, L. (2017). Network models for mapping educational data. Design Science, 3.
Wigdahl, J., Heileman, G. L., Slim, A., & Abdallah, C. T. (2014, June). Curricular efficiency: What role does it play in student success? In Proceedings of the the 121st ASEE Annual Conference and Exposition. IEEE.
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