Understanding Feedback to Improve Online Course Design

Understanding Feedback to Improve Online Course Design

A. Meikleham, R. Hugo (2017).  Understanding Feedback to Improve Online Course Design. 11.

Online learning is on the rise as universities look to reduce cost and scale their offerings (Stacey, 2013). Another trend is that some instructors have shifted to a “blended approach” which mixes brick and mortar university teaching with supplemental online lectures (Horn, 2013). While e-learning can follow a more structured approach where students embark on a fully-curated e-learning journey, online video portals can simply offer a la carte lectures that supplement in class learning. Khan Academy is an example of an online portal that has made learning accessible to anyone on a per-lecture basis (Khan, 2012). Online learning allows students on-demand access to information, but direct and real-time feedback was previously a barrier to gage student engagement and understanding (Pappas, 2016). Instructors now have access to web analytics which offer an in-depth look into students’ viewing habits. Online viewing data has the potential to be implemented and interpreted as feedback in teaching and course offering, as in a design cycle, this feedback can be used as an input in the next iteration of the online course design. For this paper, we will complete a literature review of the techniques researchers have used to analyze online and e-learning watch data and propose a novel framework for online course design using this feedback data that addresses a number of the CDIO Standards (“THE CDIO STANDARDS v 2.0,” 2010), including: (2) How online assessment and feedback can be used to evaluate learning progression; (3) Suggest pathways for integration of multiple online courses; (7) Integrate personal and interpersonal skills, products, processes and system building into the assessment framework; (8) Examine how experiential learning has previously been integrated with online learning, and examine potential ways to expand on this; (10) Demonstrate how enhanced feedback pathways will enhance faculty competence and student learning experience; (12) Findings can be taken into account for further iterations of courses, which results in stronger programs.

Bibliography Horn, M. (2013). The transformational potential of flipped classrooms. Education Next. Retrieved from http://search.proquest.com/openview/14d9ce81070d171cfdc4a27d67fb0a70/1?p... ite=gscholar&cbl=1766362 Khan, S. (2012). The one world schoolhouse: Education reimagined. Retrieved from https://books.google.ca/books?hl=en&lr=&id=xz-gkDYm4UUC&oi=fnd&pg=PT151&... world+schoolhouse+khan&ots=Q76yOHkfhQ&sig=H-PWt-51T2KPIo-GsBVHpuc7TBo Pappas, C. (2016, November 6). Top 8 eLearning Barriers That Inhibit Online Learners Engagement With eLearning Content - eLearning Industry. Retrieved January 1, 2016, from https://elearningindustry.com/top-elearning-barriers-that-inhibit-online... rs-engagement-elearning-content Stacey, P. (2013). The Pedagogy of MOOCs. Retrieved November 7, 2016, from https://edtechfrontier.com/2013/05/11/the-pedagogy-of-moocs/ THE CDIO STANDARDS v 2.0. (2010).

Proceedings of the 13th International CDIO Conference in Calgary, Canada, June 18-22 2017

Authors (New): 
Alexandra Meikleham
Ronald J Hugo
University of Calgary, Canada
Blended Learning
Online learning
CDIO Standard 2
CDIO Standard 3
CDIO Standard 7
CDIO Standard 8
CDIO Standard 10
CDIO Standard 12
Ambrose, S. A. (2010). How learning works : seven research-based principles for smart teaching. Jossey-Bass.: 
Asarta, C. J., & Schmidt, J. R. (2016). Comparing student performance in blended and traditional courses: Does prior academic achievement matter? The Internet and Higher Education, 32, 29– 38.: 
Bassi, R., Daradoumis, T., Xhafa, F., Caballe, S., & Sula, A. (2014). Software agents in large scale open e-learning: A critical component for the future of massive online courses (MOOCs). Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014, 184–188. : 
Biggs, J., & Tang, C. (2007). Teaching for Quality Learning at University. The Society for Research into Higher Education (Third). Berkshire: Open University Press, McGraw Hill. : 
Boud, D., & Molloy, E. (2013). Changing conceptions of feedback. In D. Boud & E. Molloy (Eds.), Feedback in Higher and Professional Education : Understanding it and doing it well. (pp. 11–33). Abingdon: Routledge.: 
Bowen, W. G. (2012). The “Cost Disease” in Higher Education: Is Technology the Answer? Palo Alto. de Freitas, S. I., Morgan, J., & Gibson, D. (2015). Will MOOCs transform learning and teaching in Proceedings of the 13th International CDIO Conference, University of Calgary, Calgary, Canada, June 18-22, 2017. higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology, 46(3), 455–471.: 
Delgado Kloos, C., Muñoz-Merino, P. J., Alario-Hoyos, C., Estévez Ayres, I., & Fernández-Panadero, C. (2015). Mixing and blending MOOC Technologies with face-to-face pedagogies. IEEE Global Engineering Education Conference, EDUCON, 2015–April(March), 967–971. : 
Dodero, J. M., González-Conejero, E. J., Gutiérrez-Herrera, G., Peinado, S., Tocino, J. T., & RuizRube, I. (2017). Trade-off between interoperability and data collection performance when designing an architecture for learning analytics. Future Generation Computer Systems, 68, 31– 37.: 
Formative vs Summative Assessment - Whys and Hows of Assessment. (2015). Pittsburgh, Pennsylvania: Eberly Center for Teaching Excellence & Innovation, Carnegie Mellon University. Retrieved from https://www.cmu.edu/teaching/assessment/basics/formative-summative.html: 
Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.: 
Gillet, D., Nguyen Ngoc, A. V., & Rekik, Y. (2005). Collaborative web-based experimentation in flexible engineering education. IEEE Transactions on Education, 48(4), 696–704. : 
Goncher, A. M., & Boles, W. (2016). Investigating the use of vector analysis to assess students ’ understanding. In A. Rose (Ed.), AAEE2016 Conference (pp. 1–8). Coffs Harbour, Australia.: 
Gorham, J., & Millette, D. M. (1997). A comparative analysis of teacher and student perceptions of sources of motivation and demotivation in college classes. Communication Education, 46(4), 245–261.: 
Gose, B. (2016, October). When the Teaching Assistant Is a Robot. The Chronicle of Higher Education. Retrieved from http://www.chronicle.com/article/When-the-Teaching-AssistantIs/238114: 
Halupka, V. (2012). Augmented Reality in Engineering Education : Current Status and Future Opportunities. In A. Rose (Ed.), AAEE2016 Conference (pp. 1–9). Coffs Harbour, Australia.: 
Hattie, J. (2003). Teachers Make a Difference : What is the research evidence ? Building Teacher Quality. In Australian Council for Educational Research Annual Conference on: Building Teacher Quality (pp. 1–17). Melbourne, Australia.: 
Hugo, R. J. (2014). From the printing press to you tube – Welcome to the world of lecture 2.0. In 10th International CDIO Conference. Barcelona, Spain: Universitat Politecnica de Catalunya.: 
Hugo, R. J., & Meikleham, A. (2016). Statistical Analysis of Global Online Watch Data. In A. Rose (Ed.), AAEE2016 Conference (pp. 1–9). Coffs Harbour, Australia.: 
Jokinen, K. (2009). Nonverbal Feedback in Interactions. In Affective Information Processing (pp. 227– 240). London: Springer London.: 
Kloos, C. D., Muñoz-Merino, P. J., & Muñoz-Organero, M. (2015). Extending google course builder with real-world projects in a master’s course. Revista Iberoamericana de Tecnologias Del Aprendizaje, 10(1), 3–10.: 
Koen, B. V. (2002). On the importance of presence in a web-based class. In 32nd ASEE/IEEE Frontiers in Education Conference (pp. 21–26). IEEE.: 
Kuo, M.-S., & Chuang, T.-Y. (2016). How gamification motivates visits and engagement for online academic dissemination – An empirical study. Computers in Human Behavior, 55, 16–27. : 
Mikroyannidis, A., Domingue, J., Pareit, D., Gerwen, J. V. Van, Tranoris, C., Jourjon, G., & MarquezBarja, J. M. (2016). Applying a methodology for the design, delivery and evaluation of learning resources for remote experimentation. In IEEE Global Engineering Education Conference, EDUCON (pp. 448–454). Abu Dhabi, UAE: IEEE.: 
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. : 
Sarmento, A. (2011). Enhance Students’ Computing Skills via Web-Mediated Self-Regulated Learning with Feedback in Blended Environment. In Human Interaction with Technology for Working, Proceedings of the 13th International CDIO Conference, University of Calgary, Calgary, Canada, June 18-22, 2017. Communicating, and Learning : Advancements (pp. 154–161). Hershey, US: IGI Global: 
Schuessler, H., Kolomenski, A., Bunker, P., & Perkins, C. (2016). Improving effectiveness of teaching large introductory physics courses with modern information technology. In 2nd International Conference on Higher Education Advances (Vol. 228, pp. 249–256). Valencia, Spain: Elsevier: 
Sheridan, N. (2015). 75.000 Views and Growing: Creating Vidcasts for YouTube With no Budget. In European Conference on e-Learning.: 
Tisdell, C. C. (2016). How do Australasian students engage with instructional YouTube videos ? An engineering mathematics case study. In A. Rose (Ed.), AAEE2016 Conference (pp. 1–9). Coffs Harbour, Australia.: 
Topps, D., Helmer, J., & Ellaway, R. (2013). YouTube as a Platform for Publishing Clinical Skills Training Videos. Academic Medicine, 88(2), 192–197. : 
Wautelet, Y., Heng, S., Kolp, M., Penserini, L., & Poelmans, S. (2016). Designing an MOOC as an agent-platform aggregating heterogeneous virtual learning environments. Behaviour & Information Technology, 35(11), 980–997.: 
Wise, A. F., Cui, Y., Jin, W., & Vytasek, J. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. Internet and Higher Education, 32, 11–28.: 
Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53. : 
Zhang, Y., Dang, Y., & Amer, B. (2016). A Large-Scale Blended and Flipped Class: Class Design and Investigation of Factors Influencing Students’ Intention to Learn. IEEE Transactions on Education, 59(4), 263–273. : 
Go to top