Developing a New Generation MOOC (ngMOOC): A Design-Based Implementation Research Project with Cognitive Architecture and Student Feedback in Mind

Geoff Woolcott; Carolyn Seton; Raina Mason; Ouhao Chen; Warren Lake; Christos Markopoulos; William Boyd

doi:10.2478/eurodl-2019-0002

Developing a New Generation MOOC (ngMOOC): A Design-Based Implementation Research Project with Cognitive Architecture and Student Feedback in Mind

European Journal of Open, Distance and E-Learning

Volume 22 (2019): Issue 1

By: Geoff Woolcott, Carolyn Seton, Raina Mason, Ouhao Chen, Warren Lake, Christos Markopoulos and William Boyd

Open Access

|Jan 2020

Abstract

This paper describes a design-based implementation research (DBIR) approach to the development and trialling of a new generation massive open online course (ngMOOC) situated in an instructional setting of undergraduate mathematics at a regional Australian university. This process is underscored by two important innovations: (a) a basis in a well-established human cognitive architecture in terms of cognitive load theory; and (b) point-of-contact feedback based in a well-tested online system dedicated to enhancing the learning process. Analysis of preliminary trials suggests that the DBIR approach to the ngMOOC construction and development supports theoretical standpoints that argue for an understanding of how design for optimal learning can utilise conditions, such as differing online or blended educational contexts, in order to be effective and scalable. The ngMOOC development described in this paper marks the adoption of a cognitive architecture in conjunction with feedback systems, offering the groundwork for use of adaptive systems that cater for learner expertise. This approach seems especially useful in constructing and developing online learning that is self-paced and curriculum-based.

Abstrakt

Dieses Dokument beschreibt einen designbasierten Implementierungsforschungsansatz (DBIR-Ansatz) für die Entwicklung und Erprobung eines modernen offenen Online-Kurs (ngMOOC - new generation massive open online course), der sich im pädagogischen Rahmen der Bachelormathematik bei einem regionalen australischen Universitätbefindet. Dieser Prozess wird durch zwei wichtige Neuerungen unterstrichen: (a) eine Grundlage in einer etablierten humanen kognitiven Architektur in Bezug auf die kognitive Belastungstheorie; und (b) Feedback bei den Kontaktpunkten das sich in einem bewährten Online-System basiert, und das sich der Verbesserung des Lernprozesses widmet. Die Analyse von Vorversuchen legt nahe, dass der DBIR-Ansatz für die Konstruktion und Entwicklung von ngMOOC theoretische Positionen unterstützt, die ein Verständnis dafür zulassen, wie Design für optimales Lernen Bedingungen wie unterschiedliche Online- oder gemischte Bildungskontexte nutzen kann, um effektiv und skalierbar zu sein. Die in diesem Dokument beschriebene ngMOOC-Entwicklung markiert die Einführung einer kognitiven Architektur in Verbindung mit Feedback-Systemen, die Grundlage für den Einsatz von adaptiven Systemen bietet, die für Lernkompetenz sorgen. Dieser Ansatz scheint besonders nützlich zu sein, wenn es darum geht, Online-Lernen aufzubauen und zu entwickeln, das selbstständig ist und sich auf Lehrplänen basiert.

References

1. Australian Academy of Science (2016). The mathematical sciences in Australia: A vision for 2025. Canberra, Australia: Australian Academy of Science.
Search in Google Scholar Back to article
2. Bali, M. (2014). MOOC pedagogy: gleaning good practice from existing MOOCs. Journal of Online Learning and Teaching, 10, 44-56.
Search in Google Scholar Back to article
3. Biggs, J. (1987). The study process questionnaire (SPQ): Manual. British Journal of Educational Psychology, 68, 395-407.
Search in Google Scholar Back to article
4. Biggs, J. (1999). What the student does: Teaching for enhanced learning. Higher Education Research & Development, 18, 57-75.
Search in Google Scholar Back to article
5. Biggs, J., Kember, D., & Leung, D. Y. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133-149
Search in Google Scholar Back to article
6. Boyd, W., Foster, A., Smith, J., & Boyd, W. E. (2014). Feeling good about teaching mathematics: Addressing anxiety amongst pre-service teachers. Creative Education, 5, 207-217.
Search in Google Scholar Back to article
7. Bozkurt, A., Akgün-Özbek, E., & Zawacki-Richter, O. (2017). Trends and patterns in Massive Open Online Courses: Review and content analysis of research on MOOCs (2008-2015). The International Review of Research in Open and Distributed Learning, 18(5). doi.org/10.19173/irrodl.v18i5.3080.
Search in Google Scholar Back to article
8. Bralić, A., & Divjak, B. (2018). Integrating MOOCs in traditionally taught courses: achieving learning outcomes with blended learning. International Journal of Educational Technology in Higher Education, 15(1). doi: 10.1186/s41239-017-0085-7.
Search in Google Scholar Back to article
9. Bressoud, D. M. (2014). Attracting and retaining students to complete two-and four-year undergraduate degrees in STEM: The role of undergraduate mathematics education. Commissioned paper prepared for the Committee on Barriers and Opportunities in Completing 2-Year and 4-Year STEM Degrees. Washington, DC: National Academy of Sciences.
Search in Google Scholar Back to article
10. Brookhart, S. M. (2008). How to give effective feedback to your students. Alexandria, VA: Association of Supervision and Curriculum Development.
Search in Google Scholar Back to article
11. Bruer, J. T. (2016). Where Is Educational Neuroscience? Educational Neuroscience, 1, 1-13.
Search in Google Scholar Back to article
12. Burdman, P. (2015). Degrees of freedom: Diversifying math requirements for college readiness and graduation. Oakland, CA: Learning Works and Policy Analysis for California Education.
Search in Google Scholar Back to article
13. Bureau of Labor Statistics (2011). 2010–11 Occupational Outlook Handbook. Retrieved from https://www.bls.gov/ooh/home.htm
Search in Google Scholar Back to article
14. Burnheim, C., & Harvey, A. (2016). Far from the studying crowd? Regional and remote students in higher education. In A. Harvey, C. Burnheim & M. Brett (Eds.), Student equity in Australian higher education (pp. 143-162). Singapore: Springer.
Search in Google Scholar Back to article
15. Champaign, J., Colvin, K. F., Liu, A., Fredericks, C., Seaton, D., & Pritchard, D. E. (2014). Correlating skill and improvement in 2 MOOCs with a student’s time on tasks. Proceedings of the first ACM conference on Learning @ scale conference, ACM, New York, USA, 11-20.
Search in Google Scholar Back to article
16. Chen, O., Woolcott, G., & Sweller, J. (2017). Using cognitive load theory to structure MOOCs and other computer-based learning. Journal of Computer Assisted Learning, 33(4), 293-305. doi:10.1111/jcal.12188
Search in Google Scholar Back to article
17. Chubb, I., Findlay, C., Du, L., Burmester, B., & Kusa, L. (2012). Mathematics, engineering and science in the national interest. Canberra, Australia: Office of the Chief Scientist.
Search in Google Scholar Back to article
18. Clarà, M., & Barberà, E. (2014). Three problems with the connectivist conception of learning. Journal of Computer Assisted Learning, 30, 197-206.
Search in Google Scholar Back to article
19. Croft, A., Harrison, M., & Robinson, C. (2009). Recruitment and retention of students–an integrated and holistic vision of mathematics support. International Journal of Mathematical Education in Science and Technology, 40, 109-125.
Search in Google Scholar Back to article
20. Daza, V., Makriyannis, N., & Rovira Riera, C. (2013). MOOC attack: closing the gap between pre-university and university mathematics. Open Learning: The Journal of Open, Distance and e-Learning, 28(3), 227-238.
Search in Google Scholar Back to article
21. Deloitte Report (2012). Measuring the economic benefits of mathematical science research in the UK. London, UK: Deloitte MCS Ltd.
Search in Google Scholar Back to article
22. DiSalvo, C. (2017). Viewing participatory design from the learning sciences and the field of design. In B. DiSalvo, J. Yip, E. Bonsignore & C. DiSalvo (Eds.), Participatory design for learning: Perspectives from practice and research (pp. 28-42). New York, NY: Routledge.
Search in Google Scholar Back to article
23. Diseth, A. (2003). Personality and approaches to learning as predictors of academic achievement. European Journal of Personality, 17, 143-155.
Search in Google Scholar Back to article
24. Dollinger, M., Lodge, J., & Coates, H. (2018). Co-creation in higher education: Towards a conceptual model. Journal of Marketing for Higher Education, 28(2), 1-22. doi: 10.1080/08841241.2018.1466756.
Search in Google Scholar Back to article
25. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132.
Search in Google Scholar Back to article
26. El-Hmoudova, D. (2014). MOOCs motivation and communication in the cyber learning environment. Procedia-Social and Behavioral Sciences, 131, 29-34.
Search in Google Scholar Back to article
27. Entwistle, N., Tait, H., & McCune, V. (2000). Patterns of response to an approaches to studying inventory across contrasting groups and contexts. European Journal of Psychology of Education, 15, 33-48.
Search in Google Scholar Back to article
28. Finkel, A. (2018). Winning the game of Faculty. Universities Australia Higher Education Conference Dinner Address, Parliament House, Canberra, Wednesday 28^th February 2018. Retrieved from http://www.chiefscientist.gov.au/wp-content/uploads/Universities-Australia-dinner-address.pdf
Search in Google Scholar Back to article
29. Fishman, B. J., Penuel, W. R., Allen, A. R., Cheng, B. H., & Sabelli, N. O. R. A. (2013). Design-based implementation research: An emerging model for transforming the relationship of research and practice. National Society for the Study of Education, 112, 136-156.
Search in Google Scholar Back to article
30. Freitas, A., & Paredes, J. (2018). Understanding the faculty perspectives influencing their innovative practices in MOOCs/SPOCs: a case study. International Journal of Educational Technology in Higher Education, 15(1). doi: 10.1186/s41239-017-0086-6.
Search in Google Scholar Back to article
31. Galligan, L. (2013). A systematic approach to embedding academic numeracy at university. Higher Education Research & Development, 32, 734-747.
Search in Google Scholar Back to article
32. Gašević, D., Kovanović, V., Joksimović, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15(5).
Search in Google Scholar Back to article
33. Geary, D. C. (2012). Evolutionary educational psychology. In K. Harris, S. Graham, & T. Urdan (Eds.), APA Educational Psychology Handbook (Vol. 1, pp. 597-621). Washington, D.C.: American Psychological Association.
Search in Google Scholar Back to article
34. Groen, L., Coupland, M., Langtry, T., Memar, J., Moore, B., & Stanley, J. (2015). The mathematics problem and mastery learning for first-year, undergraduate STEM students. International Journal of Learning, Teaching and Educational Research, 11, 141-160.
Search in Google Scholar Back to article
35. Hanushek, E., & Woessmann, L. (2010). The High Cost of Low Educational Performance: The Long-Run Economic Impact of Improving PISA Outcomes. Paris, France: Author.
Search in Google Scholar Back to article
36. Hew, K. F. (2015). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCs. British Journal of Educational Technology, 47, 320-341.
Search in Google Scholar Back to article
37. Hill, H. C., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching on student achievement. American Educational Research Journal, 42, 371-406.
Search in Google Scholar Back to article
38. Holdren, J., & Lander, E. (2012). Report to the President – Engage to excel: Producing one million additional college graduates with degrees in Science, Technology, Engineering, and Mathematics. Washington, DC: President’s Council of Advisors on Science and Technology:
Search in Google Scholar Back to article
39. Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126-136.
Search in Google Scholar Back to article
40. Kay, R., & Kletskin, I. (2012). Evaluating the use of problem-based video podcasts to teach mathematics in higher education. Computers & Education, 59, 619-627.
Search in Google Scholar Back to article
41. Keast, R., & Mandell, M. (2014). The collaborative push: Moving beyond rhetoric and gaining evidence. Journal of Management and Governance, 18, 9-28.
Search in Google Scholar Back to article
42. Kennedy, J. P., Lyons, T., & Quinn, F. (2014). The continuing decline of science and mathematics enrolments in Australian high schools. Teaching Science, 60, 34-46.
Search in Google Scholar Back to article
43. King, D., & Cattlin, J. (2015). The impact of assumed knowledge entry standards on undergraduate mathematics teaching in Australia. International Journal of Mathematical Education in Science and Technology, 46(7), 1032-1045.
Search in Google Scholar Back to article
44. Knox, J. (2016). Posthumanism and the massive open online course. New York, NY: Routledge.
Search in Google Scholar Back to article
45. Kuenzi, J. (2008). Science, technology, engineering, and mathematics (STEM) education: Background, federal policy, and legislative action. Retrieved from http://www.fas.org/sgp/crs/misc/RL33434.pdf
Search in Google Scholar Back to article
46. Lake, W., Boyd, W., Boyd, W., & Hellmundt, S. (2017). Just another student survey? Point of contact survey feedback enhances the student experience and lets researchers gather data. Australian Journal of Adult Learning, 57, 82-104.
Search in Google Scholar Back to article
47. Lake, W., Wallin, M., Boyd, W. E., Woolcott, G., Boyd, W., Foster, A., & Markopoulos, C. (2018). Optimising the efficacy of hybrid academic teams: Lessons from a systematic review process. Australian Universities’ Review, 60(1), 16-24.
Search in Google Scholar Back to article
48. Lake, W., Wallin, M., Woolcott, G., Boyd, W. E., Foster, A., Markopoulos, C., & Boyd, W. (2017). Applying an alternative mathematics pedagogy for students with weak mathematics: Meta-analysis of alternative pedagogies. International Journal of Mathematical Education in Science and Technology, 48, 215-228.
Search in Google Scholar Back to article
49. Lakhani, J., Benzies, K., & Hayden, K. A. (2012). Attributes of interdisciplinary research teams: A comprehensive review of the literature. Clinical & Investigative Medicine, 35(5), E260-E265.
Search in Google Scholar Back to article
50. Lawson, D., Croft, T., & Waller, D. (2012). Mathematics support past, present and future. Proceedings of the International Conference on Innovation, Practice and Research in Engineering Education, 18-20. Loughborough University, UK: Centre for Engineering and Design Education.
Search in Google Scholar Back to article
51. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. The International Review of Research in Open and Distributed Learning, 14, 202-227.
Search in Google Scholar Back to article
52. Lyons, T., Cooksey, R., Panizzon, D., Parnell, A., & Pegg, J. (2006). Science, ICT and mathematics education in rural and regional Australia: The SiMERR national survey. A research report prepared for the Department of Education, Science and Training. Armidale, Australia: National Centre of Science, ICT and Mathematics Education for Rural and Regional Australia, and the University of New England.
Search in Google Scholar Back to article
53. Mack, J., & Walsh, B. (2013). Mathematics and science combinations NSW HSC 2001-2011 by gender. Technical paper. Retrieved from http://www.maths.usyd.edu.au/u/SMS/MWW2013.pdf
Search in Google Scholar Back to article
54. Mackness, J., Waite, M., Roberts, G., & Lovegrove, E. (2013). Learning in a small, task– oriented, connectivist MOOC: Pedagogical issues and implications for higher education. The International Review of Research in Open and Distributed Learning, 14(4). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1548/2636?utm_source
Search in Google Scholar Back to article
55. Mason, M. (2008). Complexity theory and the philosophy of education. Educational Philosophy and Theory, 40(1), 4-18.
Search in Google Scholar Back to article
56. McAndrew, P., & Scanlon, E. (2013). Open learning at a distance: Lessons for struggling MOOCs. Science, 342(6165), 1450-1451.
Search in Google Scholar Back to article
57. McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2015). The MOOC model for digital practice (2010). Retrieved from http://www.elearnspace.org/Articles/MOOC_Final.pdf
Search in Google Scholar Back to article
58. Means, B., & Anderson, K. (2013). Expanding evidence approaches for learning in a digital world. Washington, DC: US Department of Education Office of Educational Technology.
Search in Google Scholar Back to article
59. van Merriënboer, J. J., & De Croock, M. B. (1992). Strategies for computer-based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research, 8, 365-394.
Search in Google Scholar Back to article
60. Mesoudi, A. (2016). Cultural evolution: Integrating psychology, evolution and culture. Current Opinion in Psychology, 7, 17-22.
Search in Google Scholar Back to article
61. Moe, R. (2015). MOOCs as a Canary: A Critical Look at the Rise of EdTech. Proceedings of the E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (Vol. 2015, No. 1, pp. 1037-1042). Hawaii: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
Search in Google Scholar Back to article
62. Office of the Chief Scientist (2012). Mathematics, engineering and science in the national interest. Canberra, Australia: Office of the Chief Scientist.
Search in Google Scholar Back to article
63. Office of the Chief Scientist (2016). Australia’s STEM Workforce: Science, Technology, Engineering and Mathematics. Canberra, Australia: Australian Government.
Search in Google Scholar Back to article
64. Okimoto, H., & Heck, R. (2015). Examining the impact of redesigned developmental math courses in community colleges. Community College Journal of Research and Practice, 39, 633-646.
Search in Google Scholar Back to article
65. Paas, F. G., & van Merriënboer, J. J. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122-133.
Search in Google Scholar Back to article
66. Parikh, A., McReelis, K., & Hodges, B. (2001). Student feedback in problem based learning: A survey of 103 final year students across five Ontario medical schools. Medical Education, 35, 632-636.
Search in Google Scholar Back to article
67. Penney, C. G. (1989). Modality effects and the structure of short-term verbal memory. Memory & Cognition, 17, 398-422.
Search in Google Scholar Back to article
68. Penuel, W. R., Allen, A. R., Coburn, C. E., & Farrell, C. (2015). Conceptualizing research– practice partnerships as joint work at boundaries. Journal of Education for Students Placed at Risk (JESPAR), 20, 182-197.
Search in Google Scholar Back to article
69. Penuel, W. R., Coburn, C. E., & Gallagher, D. J. (2013). Negotiating problems of practice in research-practice design partnerships. Yearbook of the National Society for the Study of Education, 112, 237-255.
Search in Google Scholar Back to article
70. Penuel, W. R., Fishman, B. J., Cheng, B. H., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational researcher, 40, 331-337.
Search in Google Scholar Back to article
71. Penuel, W. R., Fishman, B. J., Cheng, B. H., & Sabelli, N. (2016). Organizing research and development at the intersection of learning, implementation, and design. Annual Review of Policy Design, 4, 1-10.
Search in Google Scholar Back to article
72. Penuel, W. R., Roschelle, J., & Shechtman, N. (2007). Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning, 2, 51-74.
Search in Google Scholar Back to article
73. Peters, M. L. (2013). Examining the relationships among classroom climate, self-efficacy, and achievement in undergraduate mathematics: A multi-level analysis. International Journal of Science and Mathematics Education, 11, 459-480.
Search in Google Scholar Back to article
74. Petronzi, D., & Hadi, M. (2016). Exploring the factors associated with MOOC engagement, retention and the wider benefits for learners. European Journal of Open, Distance and E-learning, 19(2), 112-129.
Search in Google Scholar Back to article
75. Renkl, A., Atkinson, R. K., & Große, C. S. (2004). How fading worked solution steps works– a cognitive load perspective. Instructional Science, 32, 59-82.
Search in Google Scholar Back to article
76. Richardson, J. T. (2005). Instruments for obtaining student feedback: A review of the literature. Assessment & Evaluation in Higher Education, 30, 387-415.
Search in Google Scholar Back to article
77. Rodriguez, C. O. (2012). MOOCs and the AI-Stanford like courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance and E-Learning, 15(2). Retrieved from http://www.eurodl.org/materials/contrib/2012/Rodriguez.pdf
Search in Google Scholar Back to article
78. Scott, A., Woolcott, G., Keast, R., & Chamberlain, D. (2018). Sustainability of collaborative networks in higher education research projects: Why complexity? Why now? Public Management Review, 20(7), 1068-1087.
Search in Google Scholar Back to article
79. Siemens, G. (2008). MOOC or mega-connectivism course. Retrieved from http://ltc.umanitoba.ca/connectivism/?p=53
Search in Google Scholar Back to article
80. Siemens, G. (2013). Massive open online courses: Innovation in education. Open Educational Resources: Innovation, Research and Practice, 5, 5-15.
Search in Google Scholar Back to article
81. Steffens, K., Bannan, B., Dalgarno, B., Bartolomé, A. R., Esteve-González, V., & Cela-Ranilla, J. M. (2015). Recent developments in technology-enhanced learning: A critical assessment. RUSC: Universities and Knowledge Society Journal, 12(2). 73-86.
Search in Google Scholar Back to article
82. Stone, M. L., Kent, K. M., Roscoe, R. D., Corley, K. M., Allen, L. K., & McNamara, D. S. (2017). The design implementation framework. In R. D. Roscoe, S. D. Craig & I. Douglas (Eds.), End-User considerations in educational technology design (pp. 76-98). Hershey, PA: IGI Global.
Search in Google Scholar Back to article
83. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer.
Search in Google Scholar Back to article
84. Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59-89.
Search in Google Scholar Back to article
85. Verstegen, D. M., Spruijt, A., Dolmans, D., & van Merriënboer, J. J. (2016). Problem-based learning in a MOOC – Exploring an innovative instructional design at a large scale. Proceedings of the 8th International Conference on Computer Supported Education – Volume 2, CSEDU, 369-377. doi: 10.5220/0005757003690377.
Search in Google Scholar Back to article
86. Waldrop, M. M. (2013). Campus 2.0. Nature, 495(7440), 160-163.
Search in Google Scholar Back to article
87. Watson, S. (2003). Closing the feedback loop: Ensuring effective action from student feedback. Tertiary Education and Management, 9, 145.
Search in Google Scholar Back to article
88. Woolcott, G., & Chamberlain, D. (2018). Measuring a university-community collaboration using social network analysis. International Journal of Learning and Change, 11(1), 18.
Search in Google Scholar Back to article
89. Woolcott, G., Chamberlain, D., Whannell, R., & Galligan, L. (2018a). Examining undergraduate student retention in mathematics using network analysis and relative risk. International Journal of Mathematical Education in Science and Technology TMES, 50(3). doi: 10.1080/0020739X.2018.1520932.
Search in Google Scholar Back to article
90. Woolcott, G., Keast, R., Tsasis, P., Charles, M., Farr-Wharton, B., Kivits, R., & Chamberlain, D. (2018b). A network connectivity framework for person-centred service models. Panel paper presented at the 22^nd Annual International Research Society for Public Management Conference, IRSPM2018, University of Edinburgh Business School, Edinburgh, UK, 11-13 April 2018.
Search in Google Scholar Back to article
91. Woolcott, G., Mason, R., Markopoulos, C., Boyd, W., Chen, O., Seton, C., Lake, W., Whannell, R., Foster, A., Galligan, L., Marshman, M., Schmalz, J., & Sultanova, N. (2017a). Bite size maths—Building mathematics low SES student capability in regional/remote Australia. Final Report 2017 for the Higher Education Participation and Partnerships Programme (HEPPP) 2015 National Priorities Pool, Australian Government Department of Education and Training. Canberra, Australia: Australian Government.
Search in Google Scholar Back to article
92. Woolcott, G., Scott, A., Norton, M., Whannell, R., Galligan, L., Marshman, M., Pfeiffer, L., & Wines, C. (2017b). It’s part of my life: Engaging university and community to enhance science and mathematics education. Final report for Enhancing the Training of Mathematics and Science Teachers. Canberra, Australia: Department of Education and Training.
Search in Google Scholar Back to article
93. Woolcott, G., Scott, A., Norton, M., Whannell, R., Galligan, L., Marshman, M., Pfeiffer, L., & Wines, C. (2017c). The Enhancement-Lesson-Reflection process: A resource manual for science and mathematics learning and teaching. Companion Report to the Final report: It’s part of my life: Engaging university and community to enhance science and mathematics education. Canberra, Australia: Department of Education and Training.
Search in Google Scholar Back to article
94. Yin, R. K. (2013). Case study research: Design and methods. Thousand Oaks, CA: Sage.
Search in Google Scholar Back to article
95. Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors and perceptions in MOOCs. Proceedings of the 18^th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1882-1895. ACM.
Search in Google Scholar Back to article