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Purpose of the study and research questions The implementation of a LA framework following the matrix of LA benefits (see Table 1) requires specialised staff and technological capabilities (d’Aquin, Dietze, Herder, Drachsler, & Taibi, 2014). Given the emerging field of LA, staff as well as technological solutions are scarce. Therefore, the purpose of this study was threefold: 1) to investigate the current capabilities for LA at higher education institutions, 2) to explore the importance of various data sources for a valid learning analytics framework, and 3) to build an understanding on how important insights from LA using a summative, real-time and predictive perspective are perceived. Table 1: Learning analytics benefits matrix Perspective Stakeholder Summative Real-time Predictive Governance • Apply crossinstitutional comparisons • Develop benchmarks • Inform policy making • Inform quality assurance processes Institution • Analyze processes • Optimize resource allocation • Meet institutional standards • Compare units across programs and faculties Learning design • Analyze pedagogical models • Measure impact of interventions • Increase quality of curriculum Facilitator • Compare learners, cohorts and courses • Analyze teaching practices • Increase quality of teaching Student • Understand learning habits • Compare learning paths • Analyze learning outcomes • Track progress towards goals • Increase productivity • Apply rapid response to critical incidents • Analyze performance • Monitor processes • Evaluate resources • Track enrollments • Analyze churn • Compare learning designs • Evaluate learning materials • Adjust difficulty levels • Provide resources required by learners • Monitor learning progression • Create meaningful interventions • Increase interaction • Modify content to meet cohorts’ needs • Receive automated interventions and scaffolds • Take assessments including just-in-time feedback • Model impact of organizational decision-making • Plan for change management • Forecast processes • Project attrition • Model retention rates • Identify gaps • Identify learning preferences • Plan for future interventions • Model difficulty levels • Model pathways • Identify learners at risk • Forecast learning progression • Plan interventions • Model success rates • Optimize learning paths • Adapt to recommendations • Increase engagement • Increase success rates 472 CP:120
Method Design In order to reach a large number of international higher education institutions, the principle means of data collection was an online survey which was conducted between August and October 2013. The survey was implemented on the Qualtrics platform (www.qualtrics.com). International listservs, forums, and social media channels focussing on educational technology and learning analytics were used to disseminate the link to the online survey. Participants The initial dataset consisted of 176 responses. After removing incomplete responses, the final dataset included 153 valid responses (21% female, 78% male, 1% indeterminate/intersex/unspecified). The average age of the participants was 44.68 years (SD = 9.10). 30% worked in a research position, 28% were research and teaching staff, 7% reported to be in a teaching position, 4% were in a senior management role, 1% reported to work in IT services, and 1% worked as library staff. 29% worked in other roles such as data analyst, statistician, or instructional designer. The majority of participants were located in the United States (28%) and Australia (19%). Other countries included United Kingdom (5%), Canada (5%), and the Netherlands (4%). 31% of the participants reported that they were currently involved in a project focussing on LA. Instrument The survey instrument consisted of the following sections: 1. Staff capabilities for learning analytics (7 items, Cronbach’s α = .89), 2. Available technology for learning analytics (13 items, Cronbach’s α = .98), 3. Barriers for implementing learning analytics (13 items, Cronbach’s α = .93), 4. Importance of student data (9 items, Cronbach’s α = .81), 5. Importance of learning environment data (13 items, Cronbach’s α = .85), 6. Benefits from learning analytics for the institution (20 items, Cronbach’s α = .93), 7. Importance of summative learning analytics (17 items, Cronbach’s α = .93), 8. Importance of real-time learning analytics (17 items, Cronbach’s α = .94), 9. Importance of predictive learning analytics (18 items, Cronbach’s α = .94), 10. Personal background (6 items). Most items were answered on a five-point Likert scale (e.g., 5 = very important; 4 = important; 3 = undecided; 2 = not very important; 1= not at all important). It took approximately 15 minutes to complete the survey. Data analysis All data stored on the Qualtrics platform was anonymised, exported, and analysed using SPSS V.22. Initial data checks showed that the distributions of ratings and scores satisfied the assumptions underlying the analysis procedures. Results Capabilities for learning analytics When asked about staff capabilities available for LA projects, over half of the participants reported that their institution had at least one learning management specialist (62.7%) and at least one learning designer (68.6%). Other staff capabilities available for LA projects included database analyst (41.2%), statistician (38.5%), and information management architect (22.9%). Only 25% of the participants reported that they had staff in the role of a learning analytics specialist. When asked about available technology for LA, only a small number of participants reported that their institution had a data warehouse in place (19.0%), used data visualisation capabilities (19.0%), and practised automated data reporting (21.6%) as well as predictive analytics (28.1%). One out of four participants indicated that their institution had interactive dashboards available for students and facilitators (25.5%). Interestingly, several institutions already utilised natural language processing (26.8), automated discussion board analytics (26.1), automated essay scoring (27.5%), and social network analysis (24.2%). 473 CP:121
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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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learning. Digital education has led
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McLoughlin, C. (2013). Teacher prof
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advancing learning analytics (Dawso
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contact students. Additionally, we
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Figure 4: Screenshot of part of the
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Change The IRAC framework allowed u
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Mazza, R., Bettoni, M., Faré, M.,
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misconceptions (e.g. Lehman, D'Mell
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understanding of the material on th
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6 5 4 3 2 1 0 Excited Interested Co
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Results The participants in the sim
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The two studies presented here were
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The author(s) assign a Creative Com
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approach which focuses on documenti
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within the context of their assessm
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perceptions of the use of technolog
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frequently than library databases.
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Figure 1: Sample of technology mapp
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Conclusion In contrast with other s
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Yorke, M. (2003). Formative assessm
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Erickson, Greer, & Gutwin, 2011), e
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higher than the Australian universi
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educators must be careful to offer
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Miles, C.A. (2015). Strong and incr
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in formal online education. To do s
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week analysis for the 8-week 3 cour
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First Quartile 1 1 Median 1 1 Mean
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participation. Similar to Gilliani
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Coffrin, C., Corrin, L., de Barba,
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Designing for relatedness: learning
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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
Page 421 and 422: Results The experiences of learning
Page 423 and 424: Chan, S. (1999). The Chinese learne
Page 425 and 426: ambiguous nature of discussion post
Page 427 and 428: Discussion Linking assessment to on
Page 429 and 430: Digital Futures research and societ
Page 431 and 432: challenges of specifying real-world
Page 433 and 434: Hemsley-Brown, J., & Sharp, C. (200
Page 435 and 436: is taking digital technologies into
Page 437 and 438: The Making the Connection project t
Page 439 and 440: Jeroschewski, A., Levisse, A., Sala
Page 441 and 442: (2014, p.19). New business models f
Page 443 and 444: Table 3. Badges on lifelong learnin
Page 445 and 446: The Agile Learning Model: Using big
Page 447 and 448: Preliminary results Methodology A p
Page 449 and 450: (e.g. students that were directed t
Page 451 and 452: It has been predicted that “by th
Page 453 and 454: including: timely access to teacher
Page 455 and 456: Occupational Medicine Simulation Pr
Page 457 and 458: just being presented with a data sh
Page 459 and 460: Gustafson, P. (2001). Meanings of p
Page 461 and 462: This exploratory research step focu
Page 463 and 464: Week 3 of the course. Figure 2: Acc
Page 465 and 466: A pedagogical end game for exams: a
Page 467 and 468: that a bachelor degree is the new h
Page 469 and 470: However, this vision is not pie-in-
Page 471: Are Higher Education Institutions P
Page 475 and 476: of LA frameworks and on expected be
Page 477 and 478: Talented, and Learning and Behaviou
Page 479 and 480: I felt prepared to set my own LGs 5
Page 481 and 482: Measuring creativity in collaborati
Page 483 and 484: y Beghetto and Kaufman (2013), who
Page 485 and 486: And how does this creativity become
Page 487 and 488: In short, research in these paradig
Page 489 and 490: esearch is heavily context dependen
Page 491 and 492: Collaboration between Primary Stude
Page 493 and 494: Results The analysis of the observa
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Page 497 and 498: A digital what? Creating a playspac
Page 499 and 500: As of writing, plans are underway t
Page 501 and 502: Lamond, H. & Rowatt, A.J. (2015). A
Page 503 and 504: Furthermore, staff PD in digital te
Page 505 and 506: Figure 1: Expert Review of Peer Rev
Page 507 and 508: Developing Self-Regulated Learning
Page 509 and 510: Technological Structure The underly
Page 511 and 512: analytics-functionality-and-wirefra
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Page 517 and 518: Connecting fun and learning- an act
Page 519 and 520: Figure 1. Engestrom's Activity Syst
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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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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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ascilite 2015 Reviewers Reem Abu As
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Kulari Lokuge Dona Swinburne Univer
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