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time, number of forum posts contributed, and delays in assignment submission. These may fail to take into consideration the full complexity of learner activity, paint a limited picture of student engagement and learning, and be difficult to derive relevant interventions and recommendations from (Gašević et al., 2015). An alternate approach to counts and averages of these data involved aggregating and classifying them as a number of interactions between agents, such as studentstudent, student-content, or student-teacher (Agudo-Peregrina, Iglesias-Pradas, Conde-González, & Hernández-García, 2014). These measures were significantly correlated with final unit grade, and this approach presents another perspective on information that can be made available through learning analytics. Interestingly, this study and others (e.g. Jayaprakash et al., 2014) highlight the importance of unit-independent models, even though differences between learners in different units (Wolff, Zdrahal, Nikolov, & Pantucek, 2013) or the pedagogical design of units (Gašević et al., 2015) may have substantial impact on the accuracy of learning analytics. Further comparative research is therefore needed to determine the value of unit-independent and unit-dependent systems and models, and MEAP+ contributes to evidence of the efficacy of the latter. Representation Representations of information in learning analytics systems are also important to aid analyses and decision making - in particular, being able to understand and use the information are crucial (Jones et al., 2013). Highly abstracted representations such as traffic lights can provide students and staff with a quick indication of progress or predicted risk (Arnold & Pistilli, 2012). More elaborate dashboards can provide visual representations that offer quantified insight into student interactions with resources (Duval, 2011; Pardo, 2014). MEAP also has a traffic light interface, but this may not be as informative for reflecting student disengagement compared to the calculated risk ratings that are used to derive the light colour (Liu et al., 2015). Although the MEAP parameters are presumably determined by an instructor before viewing the risk ratings, this abstraction fails to provide a nuanced representation of student interactions. This is especially important if action will be taken based on an instructor’s understanding and application of these representations. In fact, confusion around percentage risk ratings and the need for less abstraction was seen in the staff interviews. Since feedback with explicit suggestions for improvement are more impactful (Tanes et al., 2011), a more nuanced understanding of information will allow more targeted and valuable feedback to be provided to students. As such, the alternative representation in MEAP+ gives instructors deeper and human-readable visibility of variables that have an existing evidence base around student performance and engagement. Since the aim of representation is to allow a learning analytics user to intuitively understand information in a few seconds (Pardo, 2014), the descriptive summary in MEAP+ is more intelligible than percentage risk ratings, and easier to understand than graphical visualisations. However, these representations are currently not customisable (for example, the instructor cannot choose to show number of replies instead of number of posts), so the importance and impact of this would be an area of future investigation. Affordances for action Action based on available information is a critical and often neglected aspect of the learning analytics loop (Clow, 2012). Specifically, affordances for integrated intervention are needed so that the efficiency and workload barriers to adoption are adequately addressed (Macfadyen & Dawson, 2012; Jayaprakash et al., 2014). For example, the Early Alert Student Indicators project at Central Queensland University integrates the sending of ‘nudges’ directly into the informational interface which helps to encourage engagement between staff and students (Beer et al., 2014). In a similar way, MEAP+ integrates information delivery and affordances of action into one coherent touch point, lowering this barrier for adoption. The composition of the messages themselves is also an important consideration, since their summative or formative nature and motivational or instructional focus impact upon the success of interventions (Tanes et al., 2011). In MEAP+, message composition is supported by ‘message snippets’ which appear as suggestions based on the indicator(s) that is/are flagged as triggering the intervention. We derived some of these snippets from PassNote, a repository of short comments based on research-supported good practice which staff can readily select and use (Croton et al., 2014), and composed a number of snippets ourselves. We are conducting further research on the use and customisation of messages delivered through this system, especially in terms of the content and nature of these interventions and their impact on students. This last point not only reflects the efficacy of MEAP+, but also the ethical implications of intervention-based learning analytics, such as ensuring only positive outcomes for students, recognising student agency and autonomy, and appreciating that student success is complex and unlikely to be causally linked to any one intervention (Slade & Prinsloo, 2013; Sclater, 2015). 186 FP:174
Change The IRAC framework allowed us to critically evaluate MEAP in the context of blended or fully online units at our institution to perform the task of assisting staff to identify and contact potentially disengaged students. Based on this, we took advantage of the open source nature of MEAP to undertake one cycle of development (Jones et al., 2013), and have released the resultant MEAP+ back to the open source community to encourage further change informed by wider implementation and development. The source code for the beta MEAP+ is available upon request. Conclusions and future directions Using a design-based research approach, we report the design and development of enhancements to MEAP based on needs analyses involving unit convenors and student support staff, supported through the IRAC framework for learning analytics functionality and quality. We extended the informational reach, improved the representation of data, and provided affordances for action directly within MEAP. Our next goal is to implement and evaluate the impact of MEAP+ in a range of units at our institution, and seek to address wider learning analytics quality indicators such as efficiency, helpfulness, availability, and effectiveness (Scheffel et al., 2014). We will explore how best to support staff to interact with the system, how it may be further modified to optimise the task of identifying and contacting students, and how it should be used to meet the needs and expectations of students. Through this more widespread usage, we will investigate the nature of feedback provided by staff, as well as the impact of these interventions on student success. Acknowledgements The authors would like to thank the unit convenors and student support staff who participated in this research. We would also like to acknowledge the technical support staff who provided advice and assistance. This project was funded by a Macquarie University Teaching Development Grant. References Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550. Arnold, K. E. (2010). Signals: Applying Academic Analytics. Educause Quarterly, 33(1), n1. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, Vancouver. Atif, A., Richards, D., Bilgin, A., & Marrone, M. (2013). Learning analytics in higher education: a summary of tools and approaches. Paper presented at the 30th Australasian Society for Computers in Learning in Tertiary Education Conference (ascilite 2013), Sydney. Barwick, H. (2014). UWS uses data analysis to track student performance. Retrieved from http://www.computerworld.com.au/article/555606/uws-uses-data-analysis-track-studentperformance/ Beer, C., Tickner, R., & Jones, D. (2014). Three paths for learning analytics and beyond: moving from rhetoric to reality. Paper presented at the 31st Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ascilite 2014), Dunedin. Black, E. W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. The Internet and Higher Education, 11(2), 65-70. Clow, D. (2012). The learning analytics cycle: closing the loop effectively. Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, Vancouver. Constantine, L. L., & Lockwood, L. A. (2001). Structure and style in use cases for user interface design. In M. Harmelen (Ed.), Object modeling and user interface design (pp. 245-280): Addison- Wesley. Croton, B. A., Willis III, J. E., & Fish, J. D. (2014). PassNote: A Feedback Tool for Improving Student Success Outcomes. Retrieved from http://www.educause.edu/ero/article/passnote-feedback-toolimproving-student-success Dawson, P., & Apperley, T. (2012). Workshop 1: Open-Source Learning Analytics and “what the student does”. Paper presented at the SoLAR Southern Flare Conference. Dawson, S. P. (n.d.). Learning Analytics in Australia. Retrieved from http://he-analytics.com/ 187 FP:175
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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.,
Page 135 and 136: 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
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Page 143 and 144: To type or handwrite: student's exp
Page 145 and 146: Phase 1 Institution wide online sur
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Page 149 and 150: exam questions for computerisation,
Page 151 and 152: students on these two issues by cou
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Page 155 and 156: Predictors of students’ perceived
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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
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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: Figure 4: Screenshot of part of the
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
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Page 197 and 198: Results The participants in the sim
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Page 201 and 202: The author(s) assign a Creative Com
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Page 213 and 214: Conclusion In contrast with other s
Page 215 and 216: Yorke, M. (2003). Formative assessm
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Page 219 and 220: higher than the Australian universi
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Page 223 and 224: Miles, C.A. (2015). Strong and incr
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Page 227 and 228: week analysis for the 8-week 3 cour
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Page 231 and 232: participation. Similar to Gilliani
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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
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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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