Leveraging local data for reflective teaching in large classes

References

• Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130–148. doi:10.1016/j.compedu.2012.10.023
   Web of Science ®Google Scholar
• Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching. San Francisco, CA: Wiley.
   Google Scholar
• Arnold, K. E. (2010). Signals: Applying Academic Analytics. EDUCAUSE Quarterly, 33, 11–11.
   Google Scholar
• Ausubel, D. P. (1968). Educational psychology: A cognitive view. New York, NY: Holt, Rinehart and Winston.
   Google Scholar
• Baer, L., & Campbell, J. P. (2012). From metrics to analytics, reporting to action: Analytics’ role in changing the learning environment. In D. G. Oblinger (Ed.), Game changers: Education and information technologies (pp. 53–65). Louisville, CO: EDUCAUSE.
   Google Scholar
• Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., & Lockyer, L. (2016, April). A conceptual framework linking learning design with learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 329–338). New York, NY: ACM. doi:10.1145/2883851.2883944
   Google Scholar
• Becher, T., & Trowler, P. (2001). Academic tribes and territories. Maidenhead: Open University Press.
   Google Scholar
• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief (Issue Brief). Washington, DC: U.S. Department of Education.
   Google Scholar
• Blackburn, R. T., & Lawrence, J. H. (1995). Faculty at work: Motivation, expectation, satisfaction. Baltimore, MD: Johns Hopkins University Press.
   Google Scholar
• Borrego, M., Froyd, J. E., & Hall, T. S. (2010). Diffusion of engineering education innovations: A survey of awareness and adoption rates in U.S. engineering departments. Journal of Engineering Education, 99, 185–207. doi:10.1002/j.2168-9830.2010.tb01056.x
   Web of Science ®Google Scholar
• Brozina, C. B., & Knight, D. B. (2015, June). Learning management systems: What can it tell us about student engagement? Proceedings of the 122nd Annual Conference of the American Society for Engineering Education, Seattle, Washington.
   Google Scholar
• Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42, 40–57.
   Google Scholar
• Carbone, E. (1999). Students behaving badly in large classes. New Directions for Teaching and Learning, 1999, 35–43. doi:10.1002/tl.7704
   Google Scholar
• Carbone, E., & Greenberg, J. (1998). Teaching large classes: Unpacking the problem and responding creatively. In M. Kaplan (Ed.), To improve the academy (Vol. 17, pp. 311–326). Stillwater, OK: New Forums Press and the Professional and Organizational Development Network in Higher Education.
   Google Scholar
• Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution. American Educational Research Journal, 46, 924–942. doi:10.3102/0002831209349460
   Web of Science ®Google Scholar
• Cuseo, J. (2007). The empirical case against large class size: Adverse effects on the teaching, learning, and retention of first-year students. The Journal of Faculty Development, 21, 5–21.
   Google Scholar
• Duval, E., Klerkx, J., Verbert, K., Nagel, T., Govaerts, S., Parra Chico, G. A., Vandeputte, B. (2012, May). Learning dashboards & learnscapes. In Educational Interfaces, Software, and Technology. 1–5.
   Google Scholar
• Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential. Creative Commons Attribution. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.456.7092
   Google Scholar
• Fairweather, J. S. (1996). Faculty work and public trust: Restoring the value of teaching and public service in American academic life. Needham Heights, MA: Allyn & Bacon.
   Google Scholar
• Gainen, J. (1995). Barriers to success in quantitative gatekeeper courses. New Directions for Teaching and Learning, 1995, 5–14. doi:10.1002/tl.37219956104
   Google Scholar
• Gibbs, G., & Jenkins, A. (1992). Teaching large classes in higher education: How to maintain quality with reduced resources. London: Kogan Page.
   Google Scholar
• Greller, W., & Drachsler, H. (2012). Translating learning into lumbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15, 42.
   Web of Science ®Google Scholar
• Grohs, J. R., Kinoshita, T., Novoselich, B., & Knight, D. B. (2015, June). Exploring engagement and achievement in undergraduate mechanics courses. Proceedings of the 122nd Annual Conference of the American Society for Engineering Education, Seattle, WA.
   Google Scholar
• Grohs, J. R., Knight, D. B., Young, G. D., & Soledad, M. M. (in press). Exploring academic performance paths and student learning strategies in a large foundational engineering course. International Journal of Education in Mathematics, Science and Technology (IJEMST).
   Google Scholar
• Grohs, J. R., Soledad, M. M., Knight, D. B., & Case, S. W. (2016, June). Understanding the effects of transferring in statics credit on performance in future mechanics courses. Proceedings of the 123rd Annual Conference of the American Society for Engineering Education, New Orleans, LA.
   Google Scholar
• Knight, D. B., Brozina, C., & Novoselich, B. J. (2016). An investigation of first-year engineering student and instructor perspectives of learning analytics approaches. Journal of Learning Analytics, 3, 215–238.10.18608/jla.2016.33.11
   Google Scholar
• Lattuca, L. R., & Stark, J. S. (2009). Shaping the college curriculum: Academic plans in context (2nd ed.). San Francisco, CA: Jossey-Bass.
   Google Scholar
• Lattuca, L. R., Terenzini, P. T., Litzinger, T., & Walser, A. (2008). Prototype to production: Processes and conditions for preparing the engineer of 2020. Lattuca, L. R. and Terenzini, P. T. (PIs), Litzinger, T., and Walser, A. (Co-PIs). $999,854 over 36 months from the National Science Foundation (EEC-0550608), awarded Feb. 2006.
   Google Scholar
• Long, P., & Siemens, G. (2011). Penetrating the FOG. Educause Review, 46, 31–40.
   Google Scholar
• McKay, T., Miller, K., & Tritz, J. (2012, April). What to do with actionable intelligence: E 2 Coach as an intervention engine. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 88–91). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2330627
   Google Scholar
• Mulryan-Kyne, C. (2010). Teaching large classes at college and university level: Challenges and opportunities. Teaching in Higher Education, 15, 175–185. doi:10.1080/13562511003620001
   Web of Science ®Google Scholar
• National Academy of Engineering & National Academies of Sciences, Engineering, and Medicine. (2016). Barriers and opportunities for 2-Year and 4-Year STEM degrees: Systemic change to support students’ diverse pathways. Washington, DC: The National Academies Press. doi:10.17226/21739.
   Google Scholar
• National Science Board. (2014). Science and engineering indicators 2014. Washington DC: National Science Foundation.
   Google Scholar
• O’Meara, K., Terosky, A. L., & Neumann, A. (2008). Faculty careers and work lives: A professional growth perspective. ASHE Higher Education Report, 34, 1–221.
   Google Scholar
• Olson, S., & Riordan, D. G. (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the President. Executive Office of the President. Retrieved from http://eric.ed.gov/?id=ED541511
   Google Scholar
• Schunk, D. H., & Zimmerman, B. J. (1998). Self-regulated learning: From teaching to self-reflective practice. New York, NY: Guilford Press.
   Google Scholar
• Seymour, E. (2008). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.
   Google Scholar
• Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57, 1380–1400.10.1177/0002764213498851
   Web of Science ®Google Scholar
• Stark, J. S. (1998). Classifying professional preparation programs. The Journal of Higher Education, 69, 353–383.10.1080/00221546.1998.11775140
   Web of Science ®Google Scholar
• Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Journal of Educational Technology & Society, 15, 133–148.
   Web of Science ®Google Scholar