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E-learning timeline 1980s to 2010 (Conole, 2017). Permission to reproduce received from author May 14, 2019.
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This PhD study investigates how universities can build institutional capacity for mainstreaming e-learning innovations in university teaching practice and maximise the adoption of transformational new methods of teaching and learning. The study focusses on digital technology-enabled learning, known as e-learning, innovations that originate in highe...
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... the past three decades, the web has progressed from read-write services of Web 1.0 into the social-media environment of Web 2.0, towards the proposition of a semantic Web, commonly referred to as Web 3.0 and, most recently, the emergence of Web 4.0 as demonstrated in applications of Artificial Intelligence (AI) services in educational assessment (Conole, 2017). This evolution of the Web also reflects a constantly changing focus within higher education on early priorities for e-learning innovation and adoption (Conole, 2017) as illustrated in Figure 2. The timeline in Figure 2 tracks the early emergence of Web services through a sequence, from transmission to social to semantic functions in education. ...
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... the past three decades, the web has progressed from read-write services of Web 1.0 into the social-media environment of Web 2.0, towards the proposition of a semantic Web, commonly referred to as Web 3.0 and, most recently, the emergence of Web 4.0 as demonstrated in applications of Artificial Intelligence (AI) services in educational assessment (Conole, 2017). This evolution of the Web also reflects a constantly changing focus within higher education on early priorities for e-learning innovation and adoption (Conole, 2017) as illustrated in Figure 2. The timeline in Figure 2 tracks the early emergence of Web services through a sequence, from transmission to social to semantic functions in education. During the 1990s, Web 1.0 enabled the creation and transmission of learning objects (items of reusable and shareable digital content) and the introduction of LMS platforms and services, followed during the 2000s by Web 2.0 which ushered in the advent of social participatory media and experiments with MOOCs and, since 2010, Web 3.0 and most recently Web 4.0 which have been harnessed in applying learning analytics in higher education assessment (Conole, 2017). ...
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... Salmon (2017) noted, conceptualising these changes in education has led to claims about how this transition from transmission to social to semantic application is disrupting educational practice. Digital technologies, such as those listed in the timeline in Figure 2, have come to be known as disruptive innovations, a concept proposed in 1997 by American business consultant and academic Clayton M. Christensen (Christensen, Horn & Johnson, 2008). ...
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... (2018b) also viewed criticisms of university slowness and resistance to change as a strength rather than a weakness and proposed that this slow rate of change was illustrated in the careful adoption of LMSs in higher education. The LMS first appears as an e-learning priority in the Conole (2017) timeline (Figure 2) in 1995, yet its relevance and significance as a widely adopted educational technology in higher education is not evident until 2004, almost ten years later, according to the timeline proposed by Weller (2018a). Weller (2018a) concluded his analysis of the evolution of educational technologies in higher education by suggesting that in universities "educational transformation is a slow burn" (p. ...
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... comparing these three timelines, the lag in technology adoption in higher education noted by Weller (2018a) remains evident. For example, gaming technologies appear in 2000 ( Figure 2), but the first appearance of games and gamification as an emerging trend in higher education is 12 years later in 2012 ( Figure 3). By 2014, "scaling teaching innovations" (Adams Becker et al., 2018, p. 5) appears in Figure 3 for the first time as a significant challenge in higher education, followed three years later, in 2017, by "rethinking the role of educators" (Adams Becker et al., 2018, p. 5). ...
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... first step in answering this question exposed the numerical value of roles in the models through manipulation of the MMM code. Bar graphs using Excel (see Section 4.9) were then prepared from this numerical data to compare the level of impact of interactions and influences on each role in the real cases with the same roles in the ideal scenarios (see Figure 23). The next step compared all real cases with each other and similarly all the ideal scenarios to identify trends in frequency of enabling and inhibiting links, two-way enabling links and influences (see Table 27). ...
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... interface shown in Figure 20 was visible to participants throughout the interview. A key to the model elements and a content guide were provided as part of the interface to aid participants in gaining familiarity with the modelling interface. ...
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... same software was also used in coding the 45,000 words of interview transcripts from the 15 case studies. The results of coding these transcripts provide the primary data for this study, as shown in Figure 22 which shows a screen capture from the Quirkos software interface. Coding against the 11 factors identified through the secondary data analysis produced the following results in terms of frequency of key quotes from the transcripts: ...
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... comparison of the values of means of all results (from modelling the 15 cases in the study) is shown in Figure 23. This comparison indicates development of the innovation (labelled Develop) ...
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... and Meso real cases, while readiness to adopt (Readiness) is clearly the most important role in all Inno and Meso ideal scenarios. Readiness appears in Figure 23 as the most important role in ideal models for both Inno and Meso cases in the study. A comparison of means indicating levels of importance of roles in interactions in all ideal models shows Readiness at 100%, closely followed by Develop (Development) at above 90% and then Sharing, Dissemination, Evidence and Experiment (Experimentation). ...
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... the Inno ideal models the least important role is Project Mgt while in the Meso ideal models it is (Project) Funding. The overall results in Figure 23 are noticeably lower for the importance of leadership, funding, project management, experimentation and central systems, which are associated with the management (macro) and central support (meso) quadrants in the model, compared with the innovator and adopter (micro) ...
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... for strategy development Implications for strategy development arising from Figure 24 along with the modelling results in Figure 23 are discussed in the next chapter and presented in the conclusion of this thesis. ...
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... for strategy development Implications for strategy development arising from Figure 24 along with the modelling results in Figure 23 are discussed in the next chapter and presented in the conclusion of this thesis. ...
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... multi-faceted characteristics of this complex problem in a complex system are most apparent in the many variations between the real and ideal models elicited from participants' stories in this study. No two models were the same and each contained a wide range of different inputs (see for example Table 27, Figure 23 and Figure 24). ...
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... multi-faceted characteristics of this complex problem in a complex system are most apparent in the many variations between the real and ideal models elicited from participants' stories in this study. No two models were the same and each contained a wide range of different inputs (see for example Table 27, Figure 23 and Figure 24). ...
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... of case-based modelling in this study allowed a full comparison (Figure 23) of better and worse results (ideal versus real) based on the inputs of enabling and inhibiting links and identification of influential roles (Table 27). From a comparison of results in Figure 23, the innovator and adopter (micro) quadrant roles of readiness to adopt, development of the innovation, sharing ideas and ownership, dissemination and evidence of effectiveness appear the most important in building institutional capacity for innovation adoption. ...
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... of case-based modelling in this study allowed a full comparison (Figure 23) of better and worse results (ideal versus real) based on the inputs of enabling and inhibiting links and identification of influential roles (Table 27). From a comparison of results in Figure 23, the innovator and adopter (micro) quadrant roles of readiness to adopt, development of the innovation, sharing ideas and ownership, dissemination and evidence of effectiveness appear the most important in building institutional capacity for innovation adoption. This suggests that while no role interactions are the same in any of the models produced by this study, it is still possible to identify where in a university the effort for mainstreaming e-learning innovation needs to occur, which is at the frontline ("coalface") of higher education teaching practice at the micro organisational level. ...
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... there appear to be no "right" or "wrong" ways to achieve mainstream e-learning innovation adoption, more enabling links (especially two-way links) between roles in the models resulted in more roles appearing important in the process of mainstreaming e-learning innovations in higher education teaching practice. In particular, as shown by the tallest graphs in Figure 23, these enabling links result in boosting institutional capacity for readiness to adopt, development of the innovation, sharing of ideas and ownership at the micro level of a university system. ...
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... outcomes of this study, as shown in Figure 23, have wide variations between the importance of the original ten stakeholder roles in real versus ideal models and across all ideal models. In The real models in this study each depicted widely varying relationships and contexts, even when cases of adoption of the same innovation occurred within a university (illustrated when comparing ...
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... I think it's been brilliant. Figure 25 is a poster summarising the research in this thesis that was presented at Flinders University Docfest 2019. Figure 25. ...
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... 25 is a poster summarising the research in this thesis that was presented at Flinders University Docfest 2019. Figure 25. Poster summarising research in this thesis. ...
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... appendix contains the application form for registration of interest in the research study ( Figure 26). Figure 26. ...
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... appendix contains the application form for registration of interest in the research study ( Figure 26). Figure 26. Registration of Interest Form. ...
