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Dawson, S. P., McWilliam, E., & Tan, J. P.-L. (2008). Teaching smarter: How mining ICT data can inform and improve learning and teaching practice. Paper presented at the Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, Melbourne. Duval, E. (2011). Attention please!: learning analytics for visualization and recommendation. Paper presented at the 1st International Conference on Learning Analytics and Knowledge. Falakmasir, M. H., & Habibi, J. (2010). Using educational data mining methods to study the impact of virtual classroom in e-learning. Paper presented at the 3rd International Conference on Educational Data Mining, Pittsburgh. Fritz, J. (2013). Using Analytics at UMBC: Encouraging Student Responsibility and Identifying Effective Course Designs. Retrieved from http://www.educause.edu/library/resources/usinganalytics-umbc-encouraging-student-responsibility-and-identifying-effective-course-designs Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. Gunn, C., Donald, D., McDonald, J., Milne, J., & Nichols, M. (n.d.). Building an Evidence-Base for Learning Design Using Learning Analytics Data. Retrieved from https://sites.google.com/site/ldlanz2015/project-team Jackson, G., & Read, M. (2012). Connect 4 Success: A Proactive Student Identification and Support Program. Retrieved from http://fyhe.com.au/past_papers/papers12/Papers/9B.pdf Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6-47. Jones, D., Beer, C., & Clark, D. (2013). The IRAC framework: Locating the performance zone for learning analytics. Paper presented at the 30th Australasian Society for Computers in Learning in Tertiary Education Conference (ascilite 2013), Sydney. Jones, D., & Clark, D. (2014). Breaking BAD to bridge the reality/rhetoric chasm. Paper presented at the Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ascilite 2014), Dunedin. Kennedy, G., Corrin, L., Lockyer, L., Dawson, S., Williams, D., Mulder, R., et al. (2014). Completing the loop: returning learning analytics to teachers. Paper presented at the Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ascilite 2014), Dunedin. Kitto, K., Cross, S., Waters, Z., & Lupton, M. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. Paper presented at the Fifth International Conference on Learning Analytics And Knowledge. Kroner, G. (2014). LMS Data - The first year update: edutechnica. Retrieved from http://edutechnica.com/2014/09/23/lms-data-the-first-year-update/ Leece, R., & Hale, R. (2009). Student Engagement and Retention through e-Motional Intelligence. UNE: Australia. Retrieved from http://www.educationalpolicy.org/events/R09/PDF/Leece_E- Motion.pdf Liu, D. Y. T., Bridgeman, A. J., & Taylor, C. E. (2014). A simple web-based analytics system to efficiently capture, analyse, and respond to student engagement and achievement. The University of Technology, Sydney: Australian Learning Analytics Summer Institute. Liu, D. Y. T., Froissard, J.-C., Richards, D., & Atif, A. (2015). Validating the Effectiveness of the Moodle Engagement Analytics Plugin to Predict Student Academic Performance. Paper presented at the 2015 Americas Conference on Information Systems, Puerto Rico. Lodge, J., & Lewis, M. (2012). Pigeon pecks and mouse clicks: Putting the learning back into learning analytics. Paper presented at the 29th Australasian Society for Computers in Learning in Tertiary Education Conference (ascilite 2012), Wellington. Lonn, S., Krumm, A. E., Waddington, R. J., & Teasley, S. D. (2012). Bridging the gap from knowledge to action: Putting analytics in the hands of academic advisors. Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, Vancouver. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599. Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149-163. Massingham, P., & Herrington, T. (2006). Does attendance matter? An examination of student attitudes, participation, performance and attendance. Journal of university teaching & learning practice, 3(2), 3. Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal (KM&EL), 4(3), 236-247. 188 FP:176
Mazza, R., Bettoni, M., Faré, M., & Mazzola, L. (2012). Moclog–monitoring online courses with log data. Paper presented at the 1st Moodle Research Conference. Mazza, R., & Milani, C. (2005). Exploring usage analysis in learning systems: Gaining insights from visualisations. Paper presented at the Communication dans: the Workshop on Usage analysis in learning systems, the twelfth International Conference on Artificial Intelligence in Education. Amsterdam, The Netherlands. Moodle. (n.d.). Moodle Statistics. Retrieved from https://moodle.net/stats/ Pardo, A. (2014). Designing Learning Analytics Experiences. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 15-38): Springer. Reeves, T. C. (2006). Design research from a technology perspective. In J. v. d. Akker, K. Gravemeijer, S. McKenney & N. Nieveen (Eds.), Educational design research (pp. 52-66). Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality indicators for learning analytics. Journal of Educational Technology & Society, 17(4), 117-132. Sclater, N. (2014). Engagement reporting tools for Blackboard and Moodle. Retrieved from http://sclater.com/blog/engagement-reporting-tools-for-blackboard-and-moodle/ Sclater, N. (2015). Code of practice for learning analytics. Retrieved from https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, Vancouver. Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector: Society for Learning Analytics Research. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., Ferguson, R., et al. (2011). Open Learning Analytics: an integrated & modularized platform: Society for Learning Analytics Research. Singh, J. (2015). New Block: Analytics graphs. Retrieved from http://www.moodleworld.com/2015/03/31/new-block-analytics-graphs Slade, S., & Prinsloo, P. (2013). Learning analytics ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. SmartKlass. (2014). SmartKlass | The Learning Analytics Plugin. Retrieved from http://klassdata.com/smartklass-learning-analytics-plugin/ Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses. Journal of Asynchronous Learning Networks, 16(3), 51-61. Swan, K. (2003). Learning effectiveness: what the research tells us. In J. Bourne & J. C. Moore (Eds.), Elements of Quality Online Education, Practice and Direction (pp. 13-45). Needham, MA: Sloan Center for Online Education. Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414-2422. Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation, 27(2), 237-246. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500-1509. West, D. (n.d.). Let’s Talk Learning Analytics and Student Retention. Retrieved from http://www.letstalklearninganalytics.edu.au/ Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. Paper presented at the 3rd international conference on learning analytics and knowledge. Liu, D.-Y.T., Froissard, J.-C., Richards, D., & Atif, A. (2015). An enhanced learning analytics plugin for Moodle: student engagement and personalised intervention. In T. Reiners, B.R. von Konsky, D. Gibson, V. Chang, L. Irving, & K. Clarke (Eds.), Globally connected, digitally enabled. Proceedings ascilite 2015 in Perth (pp. FP:168- FP:177). Note: All published papers are refereed, having undergone a double-blind peer-review process. The author(s) assign a Creative Commons by attribution licence enabling others to distribute, remix, tweak, and build upon their work, even commercially, as long as credit is given to the author(s) for the original creation. 189 FP:177
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ascilite2015 Australasian Society f
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To type or handwrite: student's exp
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Concise papers Learning maps: A des
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The three pillars to building staff
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Posters Metacognitive Development i
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Discussion Papers Decisions and des
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The Conceived, the Perceived and th
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concentration of evaluations being
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“I just felt like we skimmed over
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Figure 3: Student Infographic Proto
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Learning design for science teacher
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existing teaching practice with tec
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the module, 7% said they had heard,
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Calculus 1, 2014 (undergraduat e, 5
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ambitions towards educational techn
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Tensions and turning points: explor
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Virtual Learning Environment Teachi
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• Virtual Learning Environment
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newsroom role-playing scenario requ
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Lisa’s fourth turning point event
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vertically in that the lecturer may
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Navigate Me: maximising student pot
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improve academic outcomes, rather t
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Figure 4: Part of an action plan Fr
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Most common issues One useful produ
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communication/assessment and assist
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Designing an authentic professional
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for designing authentic learning en
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Bannan (2010) proposes a simpler fo
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An overview of CMALT requirements W
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students. From the roof, the overal
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Bruns, A. (2007, March 21-23). Beyo
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Investigating the effectiveness of
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labile, and diachronic interaction,
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learning. It was thought that this
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Table 2: Summary of questionnaire r
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the material (E) my colleagues (S)
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co-construction of knowledge by sup
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Community volunteers in collaborati
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Induction and persistence How are v
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Figure 1: Contributions made in gen
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such acts as mentorship: One is the
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established a record of high qualit
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the OERu project or similar open de
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A ‘participant first’ approach
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five ways in which learning resourc
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Participant Perspective Consider yo
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key focus was on designing tasks th
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Management System, the primary tool
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Jordan, K. (2015). MOOC completion
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new pedagogical methods to engage t
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een perceived to be boring, with lo
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accounting concepts to others. Post
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the one model provided by the resea
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workplace. A further graduate attri
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Moy, J. (1999). The Impact of gener
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provider to understand the dependen
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• In the first semester of their
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Age at Course Start 22.98 4.3 >99.9
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3 1 Figure 4. Attrition Rate: (Top)
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New applications, new global audien
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The modality of game-based learning
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3% 2% 2% 2% 2% 2% Education Other H
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Machinima is being utilised to supp
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a lack of support from many institu
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2015). There are new hardware and s
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Gregory, S., Gregory, B., Wood, D.,
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studies have found that university
Page 137 and 138: Importantly, TEL is a broad term an
Page 139 and 140: needs, etc.). Educators create digi
Page 141 and 142: could take the form of authorities
Page 143 and 144: To type or handwrite: student's exp
Page 145 and 146: Phase 1 Institution wide online sur
Page 147 and 148: There were just over 200 students (
Page 149 and 150: exam questions for computerisation,
Page 151 and 152: students on these two issues by cou
Page 153 and 154: from http://educationtechnologysolu
Page 155 and 156: Predictors of students’ perceived
Page 157 and 158: Lim, Morris, and Yoon (2006) sugges
Page 159 and 160: The questionnaire was pilot tested
Page 161 and 162: ** p < .001 - a This parameter was
Page 163 and 164: Among the four exogenous variables,
Page 165 and 166: influence on perceived course outco
Page 167 and 168: 5(3), 255-269. Marks, R. B., Sibley
Page 169 and 170: and continuously evaluate the deliv
Page 171 and 172: supporting teachers in the transiti
Page 173 and 174: etween other VET schools and the co
Page 175 and 176: sharing of the tasks, and their pee
Page 177 and 178: learning. Digital education has led
Page 179 and 180: McLoughlin, C. (2013). Teacher prof
Page 181 and 182: advancing learning analytics (Dawso
Page 183 and 184: contact students. Additionally, we
Page 185 and 186: Figure 4: Screenshot of part of the
Page 187: Change The IRAC framework allowed u
Page 191 and 192: misconceptions (e.g. Lehman, D'Mell
Page 193 and 194: understanding of the material on th
Page 195 and 196: 6 5 4 3 2 1 0 Excited Interested Co
Page 197 and 198: Results The participants in the sim
Page 199 and 200: The two studies presented here were
Page 201 and 202: The author(s) assign a Creative Com
Page 203 and 204: approach which focuses on documenti
Page 205 and 206: within the context of their assessm
Page 207 and 208: perceptions of the use of technolog
Page 209 and 210: frequently than library databases.
Page 211 and 212: Figure 1: Sample of technology mapp
Page 213 and 214: Conclusion In contrast with other s
Page 215 and 216: Yorke, M. (2003). Formative assessm
Page 217 and 218: Erickson, Greer, & Gutwin, 2011), e
Page 219 and 220: higher than the Australian universi
Page 221 and 222: educators must be careful to offer
Page 223 and 224: Miles, C.A. (2015). Strong and incr
Page 225 and 226: in formal online education. To do s
Page 227 and 228: week analysis for the 8-week 3 cour
Page 229 and 230: First Quartile 1 1 Median 1 1 Mean
Page 231 and 232: participation. Similar to Gilliani
Page 233 and 234: Coffrin, C., Corrin, L., de Barba,
Page 235 and 236: Designing for relatedness: learning
Page 237 and 238: inquiry. This collaborative yarning
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online in the LMS until connections
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each other in educational environme
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Figure 2: Evaluation of Components
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Conclusion The experience of Aborig
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Torche, F., & Valenzuela, E. (2011)
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types of content and new models of
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ased on real journalism content 10
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efficiently access the key literatu
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systems. These initiatives are valu
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Ren, X. (2013). Beyond open access:
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within a constructivist framework (
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library, learning centers and couns
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testing occurs beyond that? What is
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Interdisciplinary opportunities and
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interdisciplinary project that aime
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J F M A M J J A S O N D 2014 Academ
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also vital. Achieving the correct b
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odies. Moreover, attracting enough
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Paving the way for institution wide
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Activity (Workshop) Description and
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Keyboard The physical keyboard is a
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• Annotating diagrams • Ability
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Conclusion This project demonstrate
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MyCourseMap: an interactive visual
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Figure 1: MyCourseMap visual overvi
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The tool has particular applicabili
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11. Media gallery 10. Graduate attr
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Barriers or challenges. actually go
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Figure 8: The unit information page
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Standing on the shoulders of others
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designer employed by La Trobe Unive
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student interaction are essential c
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Figure 2: Weekly learning design se
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The current [template] speaks clear
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Weaver, D, Robbie, D, Kokonis, S &
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Background Learning analytics The r
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and conducted workshops as the proj
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• Communication (email, Facebook,
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180 Daily Weekly Fortnightly Monthl
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Relevance and comprehensiveness of
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Further Information More informatio
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Virtual worlds have great potential
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conceptual understanding, and (3) m
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Figure 5: Map and Hints of the VW E
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All quiz questions are saved in a X
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Gütl, C. (2011). The Support of Vi
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Remote Access Laboratories for Prep
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een derived from the STEBI-B (Enoch
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15 Students’ achievement in techn
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RALfie offers both hands-on and onl
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and non-user groups and efforts hav
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Maxwell, A. D., Orwin, L., Kist, A.
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To evaluate the feasibility of usin
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their observed behaviour. Towards t
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study and help them adapt to a new
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main student concern, there are add
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Information delivered through the a
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Learning maps: A design-based appro
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4 Integrative • Mixed research me
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Dede, C. (2004). If design-based re
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LAK demonstrates a great deal of co
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Experiences from early reusability
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The future of practice-based resear
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1.1. Consider factoring in control
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3. Consider the research questions
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Features of an online English langu
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Figure 2: Embedded Matbury’s MP3
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References Ayres, P., & Sweller, J.
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that will provoke a specific kind o
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Preliminary results The study is on
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Using expectation confirmation theo
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Findings and Discussion The data an
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Fawcett, S. L. (2002). Group gaming
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visualization affects learning but
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Demonstrate the ability to construc
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Implementing blended learning at fa
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Exploring approaches taken at other
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But, J. & Shobbrook, R. (2012). Web
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potential of social media in educat
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Another tweet included a photo of t
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Teachers Cloud-based Content Creati
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Deviation CBLD? 1. Learning Outcome
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McGee, P., & Reis, A. (2012). Blend
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The Next Wave of learning Innovatio
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than a static system / website / mo
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Loop: A learning analytics tool to
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Figure 1: The Loop tool dashboard f
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efore it is released as an open sou
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programmed through freely available
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In addition to this experiment, at
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An investigation of blended learnin
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Results The experiences of learning
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Chan, S. (1999). The Chinese learne
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ambiguous nature of discussion post
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Discussion Linking assessment to on
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Digital Futures research and societ
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challenges of specifying real-world
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Hemsley-Brown, J., & Sharp, C. (200
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is taking digital technologies into
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The Making the Connection project t
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Jeroschewski, A., Levisse, A., Sala
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(2014, p.19). New business models f
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Table 3. Badges on lifelong learnin
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The Agile Learning Model: Using big
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Preliminary results Methodology A p
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(e.g. students that were directed t
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It has been predicted that “by th
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including: timely access to teacher
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Occupational Medicine Simulation Pr
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just being presented with a data sh
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Gustafson, P. (2001). Meanings of p
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This exploratory research step focu
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Week 3 of the course. Figure 2: Acc
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A pedagogical end game for exams: a
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that a bachelor degree is the new h
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However, this vision is not pie-in-
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Are Higher Education Institutions P
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Method Design In order to reach a l
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of LA frameworks and on expected be
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Talented, and Learning and Behaviou
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I felt prepared to set my own LGs 5
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Measuring creativity in collaborati
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y Beghetto and Kaufman (2013), who
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And how does this creativity become
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In short, research in these paradig
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esearch is heavily context dependen
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Collaboration between Primary Stude
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Results The analysis of the observa
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even difficult for them to bring to
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A digital what? Creating a playspac
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As of writing, plans are underway t
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Lamond, H. & Rowatt, A.J. (2015). A
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Furthermore, staff PD in digital te
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Figure 1: Expert Review of Peer Rev
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Developing Self-Regulated Learning
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Technological Structure The underly
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analytics-functionality-and-wirefra
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model of professional learning that
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ecognition of their teaching in a t
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Connecting fun and learning- an act
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Figure 1. Engestrom's Activity Syst
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pedagogical and technological barri
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(e.g. Graesser et al., 2005), it ca
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ability to perform well on a test a
Page 527 and 528:
Exploring my university students’
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15.00% 10.00% 5.00% 0.00% created a
Page 531 and 532:
Miller, M. D. (2014). Online Learni
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component, evaluation that connects
Page 535 and 536:
not be enough to encourage its wide
Page 537 and 538:
Benchmarking for technology enhance
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Findings groups to try and streamli
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Author, M. (2014a). Benchmarking fo
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uploaded document with consistent m
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Promoting Critical Thinking in a La
Page 547 and 548:
Table 1: Overview Summary of How In
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and post-exam online surveys were a
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Seow, T.K. & Soong, S.K.A. (2015).
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family and friends to help them. Di
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Upon completion of the unit, studen
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Blended Learning Adoption Monitorin
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for each stage are listed as Strate
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References Alammary, A., Sheard, J.
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environment), and (3) civic engagem
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Kolb, D. A. (1984). Experiential le
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Freitas, 2006; Fröding & Peterson,
Page 569 and 570:
The research methodologies appropri
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Teräs, H. (2014). Collaborative on
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Upon completion of a learning activ
Page 575 and 576:
Figure 3: Drag and drop application
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Pre-service teachers’ reflections
Page 579 and 580:
like the Renaissance, and the teach
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from http://www.researchgate.net/pu
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concept of summer learning loss. Th
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Figure 2: Relationships between Cou
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Webby, B., Quinn, D., Albrecht, A.,
Page 589 and 590:
The University’s Approach to a Le
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Figure 2: Draft CSU Learning Analyt
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Dreaming of Electric Sheep: CSU’s
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student learning. Furthermore, many
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something that is done by the “da
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SkillBox: a pilot study Rachel Anne
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agree, A=agree, N=neutral, D=disagr
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Whitsed, R.A. & Parker, J. (2015).
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critical theory and feminist studie
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connectedness between individual an
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Description of proposed interventio
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Kaufman & L. Li (Eds.), Routledge i
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Matthews, A., Aggarwal, R., and Lim
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Schau, C., & Mattern, N. (1997). Us
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eight of the nine interviewees in a
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communicate proposals, plans, compa
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Creating concept vignettes as a mod
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Preparing Students for Future Learn
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References Association for the Adva
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The use of rubrics for the assessme
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Developing an online challenge-base
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Let’s Talk Learning Analytics and
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Experiential Learning in Accounting
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The CSU Online Learning model Tim K
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MOOCs as spaces to innovate Alison
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The author(s) assign a Creative Com
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In terms of educational uses, the l
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The framework At the University of
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Enhancing Workplace Learning throug
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Figure 2: GPS for WPL structure Con
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The success of this new support mod
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Acknowledgement Supported by the Di
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Discussion Who owns learning? Educa
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Pawlaczek, Z. & Allan, G. (2015). D
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These divergent student perceptions
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Connecting or constructing academic
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define one academic literacy for al
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Learning, 12(3), 139-160. Retrieved
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Data collection The seven case stud
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share their own experiences in orde
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of Goals-Signals-Metrics as columns
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material, perform self-assessment t
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Many educational technology researc
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non-technical users can participate
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particularly in the early stages. T
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Williams, T., Kaui,T. & Ernst, J. (
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Campbell & Oblinger, 2007; Clow, 20
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Johnson, L., Adams Becker, S., & Ha
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sine qua non of LA, and a recent co
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Lodge, J., & Lewis, M. (2012). Pige
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academic referencing system Sente 6
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Norton, A. (2012). Mapping Australi
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How new teaching methods affect our
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Calderon, O., Ginsberg, A., Ciabocc
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thus enabling libraries to learn ab
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Formulating research questions whic
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as well as examples of each element
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the learning materials are presente
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activity results in a collaborative
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the definition, not the technology.
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in researching and developing teach
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2. Examples of open scholarship net
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#NPF14LMD Learners and Mobile Devic
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Adrienne Moyle Academic Advisor wit
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Easing into mobile learning Angela
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Clinical Logs: Best Practices in th
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Digitally enabled learning through
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Using interactive multimedia for
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References Barbezat, D., & Bush, M.
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The author(s) assign a Creative Com
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ascilite 2015 Reviewers Reem Abu As
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Kulari Lokuge Dona Swinburne Univer
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