Timing: 2 hours
- Start by reading the paragraph headed ‘Outcomes of participant observation’ (p. 157) and then read from the heading ‘Why numbers are not enough’ (p. 159) to the end of the paper.
- Macfadyen and Dawson (2012), Numbers are not enough.
- Dawson and Macfadyen group the reasons for lack of uptake under two headings: ‘Perceived attributes of an innovation’ and ‘The realities of university culture’. In a blog post, or in your learning journal, note the reasons they identify for lack of uptake, and choose your own headings to group them under.
- In the discussion forum, or in OU Live, discuss the headings you have selected. Can you agree on a common set of headings? Do any of these groups of reasons stand out as more important than the others?
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– We suggest here that this may be the result of lack of attention to institutional culture within higher education.
– lack of understanding of the degree to which individuals and cultures resist innovation and change.
– lack of understanding of approaches to motivating social and cultural change.
– Although social systems such as educational institutions do evolve and change over time, they are inherently resistant to change and designed to neutralize the impact of attempts to bring about change.
Lack of Support From Individuals
– no vision or plan will emerge or be embraced without the support of faculty and staff.
– an individual’s reaction to change reflects their cognitive evaluation of the way in which a new event or context will affect their personal wellbeing
– individuals will assess it situationally for its “relative advantage.
– They will assess it for “compatibility”: the degree to which it is consistent with existing practice and values, and with needs of potential adopters.
– They will assess it for “complexity”: the degree to which it is perceived to be difficult to understand or to use.
– Faculty may view the introduction of technologies into teaching as a time-consuming imposition
– Activities may be perceived as being antithetical to the current institutional culture.
– time-commitment needed for quality instructional design
– Potential for learning technologies to enhance teaching and learning may be poorly understood and incongruent with individual perceptions and beliefs surrounding good teaching practice.
– Faculty may worry that spending time on technology will actually hamper their career due to poor evaluations of teaching.
Underdeveloped Ed Tech Sector
– academic culture still rewards faculty for verifiable teaching expertise.
– current lack of standardized methods of assessment of online teaching expertise.
– cooperative nature of effective team-based course development mean that incentives are often very low for faculty to invest time in working with technology.
Senior Staff Reluctance
– members of the senior administration participating in committees charged with LMS review and selection—are typically senior faculty members rather than professional managers.
– assessing the degree to which any change will burden themselves and their colleagues with the need to learn how to use complex new tools, and/or the need to redesign change their teaching habits and practices, without offering any appreciable advantage or reward.
Investment in Student Outcomes
– Information technology managers and staff similarly are most likely to assess proposals for new technology innovations from the perspective of workload and technical compatibility with existing systems, and have an even smaller investment in student learning outcomes.
Lack of Clear Goal(s)
– absence of a strategic goal or vision (and of any clear incentives to strive towards such a strategic vision), analytic data reporting on current LMS data have little motivating power.
– institutional resistance” is found in the very culture of academic institutions
– consensus governance (rather than industrial-style hierarchical management)
– faculty control over the major goal activities (teaching and research)
– an organizational culture that supports change by adding resources rather than by strategically reallocating resources
– a curriculum structure that makes false (though some would argue, necessary) assumptions about learner homogeneity
– any direct interference in faculty democracy is not welcome.
Interpretation and Observability of Analytics
– Not used used to highlight progress and room for growth against a backdrop of institutional targets and vision—and if participants are committed to the vision and motivated to achieve it
– Interpretation remains critical
– Greater attention is needed to the accessibility and presentation of analytics processes and findings so that learning analytics discoveries also have the capacity to surprise and compel, and thus motivate behavioural change
– to date, efforts to mine educational data have been hampered by the lack of data mining tools that are easy for non-experts to use.
– Poor integration of data mining tools with e-learning systems; and by a lack of standardization of data and models so that tools remain useful only for specific courses/frameworks.
– Collectively, these difficulties make analytics data difficult for non-specialists to generate (and generate in meaningful context), to visualize in compelling ways, or to understand, limiting their observability and decreasing their impact.