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individual centrality measures in a given network for: i) degree—the number of people one has cooccurred with; ii) betweenness—the measure indicating whether the individual has co-participated with other students who are otherwise unconnected; iii) closeness—the number of connections that exist between participants to link them directly, also denoting the “compactness” of the network (Wasserman & Faust, 1994). Centralization and density of the regular participants network in relation to the all learner network were plotted overtime at four different time points representing the identified course thematics: • Stage 1 for the first week of the course; • Stage 2 for thematic module 1 in weeks 2 and 3; • Stage 3 for the thematic module 2 in weeks 4, 5 and 6; • Stage 4 for thematic module 3 in weeks 7 and 8. Data manipulation and analysis was undertaken using the igraph package in R (Csardi & Nepusz, 2006). Results This paper analysed how the network of regular participants developed over the course offering compared with the network of the entire MOOC cohort. Given that regular participants returned to the forum, and were a relatively smaller group, we expected that this sub-population would have a much more cohesive network structure, compared to a loose network representing the entire cohort. The analyses indicate that both the all learners and regular participants networks had a similar structure. While the networks contained a small group of highly interconnected individuals that interacted with many people multiple times, the majority of participants interacted infrequently and only with a few people. We observed that 75% of regular participants most commonly interacted with 1 to 24 people in the course, and communicated with the same person once, on average. Yet, the remaining 25% of regular participants interacted with 24 to 179 people during the course, with the frequency of interaction with the same person ranging from 3 to 147 times. These inferences are derived from Table 1, illustrating the degree distribution, which here represents the number of people a participant co-occurred in a conversation with; while the mean edge weight denotes the frequency of cooccurrence with the same individual. A power-law distribution was observed in the all learners network. The majority (75%) of all learners interacted during the course with 1 to 32 people with an average frequency of once. The remaining 25% interacted with 32 to 593 people, with a frequency ranging from 1 to 147 times. It can be concluded that the most frequent interactions between the same individuals took place within the regular participants, since the maximum values for the edge weight are shared across the two networks. Table 1. Description of All Learner and Regular Participants Networks All Learners Regular Participants Nodes 2434 196 Edges 27559 2016 Density 0.009 0.1 Centralization Degree 0.23 0.81 Betweenness 0.18 0.23 Closeness 0.0009 0.62 Degree Distribution First Quartile 2 7 Median 8 15 Mean 22.6 20.5 Third Quartile 32 24 Maximum 593 179 Edge Weight Distribution 228 FP:216
First Quartile 1 1 Median 1 1 Mean 1.5 3.5 Third Quartile 1 3 Maximum 147 147 Average Edge Weight Distribution First Quartile 1 1.4 Median 1 2.1 Mean 1 2.6 Third Quartile 1.32 3 Maximum 12.3 14 The network representing the entire cohort is loosely coupled, highly decentralized, and characterized by most individuals located in smaller interconnected parts of the network that are linked by several individuals who co-participate in these otherwise disparate clusters. The regular participants network is similar in structure. However, the network is much more centralized than the all learners network with a degree value of 0.81 and 0.23 respectively. The closer the centralization measure is to ‘1’, the more highly centralized the network is, and marked by the clear boundary between its core and periphery (Scott, 2013). Given network’s power-law distribution and low betweenness value, one can assume that those who participated in most conversations, i.e. having high brokering power, would also be connected to the highest number of people (degree). Besides being brokers between conversations, they would also shorten the distance between the regular participants, as well as control much of the information within the network thereby explaining the differences in the observed network centrality measures. The pattern of development for the networks representing the entire cohort and more regular participants (both student-only and students-and-staff interactions) has been consistent up until Stage 3 (Figure 4). The network of the entire cohort retained its low density and low closeness centrality indicating that individuals were only connecting with a few people, and the network members were sparsely distributed. The period between Stage 1 and Stage 2 is marked by a growth in the degree and betweenness centrality measures. Such a dynamic signifies an increase in the network centralization post week 1 due to the emergence of more active participants. In contrast, regular participants network shows a steady change in structure from a loose to a more inter-connected network. Although its density remained low at all time points, its value is much higher for regular participants without staff and CAs suggesting that the regular participants activated more possible connections between each other, than those students participating infrequently. The much higher centralization and closeness of the regular participants network as compared to the same network without the staff and CAs points to the role played by this dedicated group of people. The staff and CAs provided a bridge between the structural holes in the network, thereby significantly reducing the distances among the actors. In conclusion, while still maintaining a loosely coupled structure, the regular participants formed stronger relational ties among its central actors and established their wide outreach to loosely interconnected periphery. Such a structure is reflective of the developmental stages of a community of practice. 229 FP:217
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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
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Importantly, TEL is a broad term an
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needs, etc.). Educators create digi
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could take the form of authorities
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To type or handwrite: student's exp
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Phase 1 Institution wide online sur
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There were just over 200 students (
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exam questions for computerisation,
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students on these two issues by cou
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from http://educationtechnologysolu
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Predictors of students’ perceived
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Lim, Morris, and Yoon (2006) sugges
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The questionnaire was pilot tested
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** p < .001 - a This parameter was
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Among the four exogenous variables,
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influence on perceived course outco
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5(3), 255-269. Marks, R. B., Sibley
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and continuously evaluate the deliv
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supporting teachers in the transiti
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etween other VET schools and the co
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sharing of the tasks, and their pee
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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 and 188: Change The IRAC framework allowed u
Page 189 and 190: Mazza, R., Bettoni, M., Faré, M.,
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
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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: week analysis for the 8-week 3 cour
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
Page 239 and 240: online in the LMS until connections
Page 241 and 242: each other in educational environme
Page 243 and 244: Figure 2: Evaluation of Components
Page 245 and 246: Conclusion The experience of Aborig
Page 247 and 248: Torche, F., & Valenzuela, E. (2011)
Page 249 and 250: types of content and new models of
Page 251 and 252: ased on real journalism content 10
Page 253 and 254: efficiently access the key literatu
Page 255 and 256: systems. These initiatives are valu
Page 257 and 258: Ren, X. (2013). Beyond open access:
Page 259 and 260: within a constructivist framework (
Page 261 and 262: library, learning centers and couns
Page 263 and 264: testing occurs beyond that? What is
Page 265 and 266: Interdisciplinary opportunities and
Page 267 and 268: interdisciplinary project that aime
Page 269 and 270: J F M A M J J A S O N D 2014 Academ
Page 271 and 272: also vital. Achieving the correct b
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Page 275 and 276: Paving the way for institution wide
Page 277 and 278: 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
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Exploring my university students’
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15.00% 10.00% 5.00% 0.00% created a
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Miller, M. D. (2014). Online Learni
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component, evaluation that connects
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not be enough to encourage its wide
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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
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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,
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The research methodologies appropri
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Teräs, H. (2014). Collaborative on
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Upon completion of a learning activ
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Figure 3: Drag and drop application
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Pre-service teachers’ reflections
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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.,
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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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