Student Study Habits as Inferred from On-line Watch Data

Student Study Habits as Inferred from On-line Watch Data

R. Hugo, R. Brennan (2016).  Student Study Habits as Inferred from On-line Watch Data. 13.

The use of online delivery for flipped or blended learning offers a number of opportunities to explore student study habits in ways not readily available when using traditional live lecture delivery. Given the nature by which data is recorded, online learning platforms provide an ability to collect data that is more objective in comparison to methods such as student surveys or manual time recording. The ability to monitor the number of times, duration, and time of day when students view content in relation to the date that homework is due or that exams are held can provide new insight into how students structure their study time. Further insight can be derived by extending this to include the dates of assessment activities in other courses taken by students during a single semester.

This paper will explore student online watch data collected during delivery of three different blended learning courses: Mechanical Engineering Thermodynamics taken by third-year students; Introductory Fluid Mechanics taken by second-year students; and Heat Transfer taken by third-year students. Each course offering involved different assessment schedules, making it possible to examine how student study habits vary in relation to the differing schedules. Both Mechanical Engineering Thermodynamics and Heat Transfer were taught over a 13-week-long semester with most students enrolled in 5 to 6 courses at a time. Introductory Fluid Mechanics was taught over 6 weeks during a Summer session with most students taking only one course during the Summer term. Given the varying lengths of academic semesters, the differences in assessment schedules, and the differing course loads, it becomes possible to examine how student study habits change in response to these parameters.

Data provided by online learning platforms is relatively new to the engineering education community and consequently part of the paper will be devoted to examining methods for visualizing these data sets. The paper will then examine how changes in assessment schedules relate to student study habits in regular 13 week semesters. Student study habits during long weekends or prescribed study (reading) weeks will also be examined. The paper then examines how student study habits vary between 13-week-long semesters and shorter 6-week-long semesters. An attempt will be made to ascertain which of the assessment structures investigated results in the best student study habits, ideally characterized as work performed consistently over the entire semester.

Proceedings of the 12th International CDIO Conference, Turku, Finland, June 12-16 2016

Authors (New): 
Ronald J Hugo
Robert W Brennan
University of Calgary, Canada
Blended Learning
On-line Delivery
CDIO Standard 10
CDIO Standard 11
Al-Zahrani, A.M., (2015). “From passive to active: The impact of the flipped classroom through social learning platforms on higher education students’ creative thinking”, British Journal of Educational Technology, Vol. 46, No. 6, pp. 1133–1148. : 
Bates, A.W. (2010). Managing Technological Change. San Francisco, CA: Jossey-Bass. : 
Dutton, J., Dutton, M., and Perry, J. (2011). “Do online students perform as well as lecture students?”, Journal of Engineering Education. : 
Juster, F.T. and Stafford, F.P. (1991). “The Allocation of Time: Empirical Findings, Behavioral Models, and Problems of Measurement”, Journal of Economic Literature, Vol. XXIX, pp. 471-522. : 
Kember, D., Jamieson, Q.W., Pomfret, M., and Wong, E.T.T. (1995). “Learning approaches, study time and academic performance”, Higher Education, Vol. 29, pp. 329-343. : 
Khanova, J., Roth, M.T., Rodgers, J.E. and McLaughlin, J.E. (2015). “Student experiences across multiple flipped courses in a single curriculum”, Medical Education, Vol. 49, pp. 1038-1048. : 
Mazur, E. 1997. Peer Instruction, A User’s Manual, Prentice Hall.: 
McAndrew, P., Scanlon, E. (2013). “Open learning at a distance: lessons for struggling MOOCs,” Science. Vol. 342, pp. 1450–1451. : 
Oosterbeek, H. (1995). “Choosing the Optimum Mix of Duration and Effort in Education”, Economics of Education Review, Vol. 14, No. 3, pp. 253-263. : 
Pappano, L. (2012) “The year of the MOOC”, The New York Times: 
Peercy, P.S. and Cramer, S.M. (2011). “Redefining quality in engineering education through hybrid instruction”, Journal of Engineering Education, Vol. 100, No. 4, pp. 625-629. : 
U.S. Department of Education (2010). Office of Planning, Evaluation, and Policy Development. Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation and Policy Development. : 
Wankat, P.C. and Oreovicz, F.S. (1992). Teaching Engineering. McGraw-Hill College. : 
Zuriff, G.E. (2003). “A Method for Measuring Student Study Time and Preliminary Results”, College Student Journal, Vol. 37, Issue 1, pp. 72-79. : 
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