Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149–163.
institutional level; academic analytics: Instead, the best institutional predictors of educational gain are “measures of educational process: what institutions do with their resources to make the most of whatever students they have” (Gibbs, 2010, p. 2). To investigate the impact, a longitudinal participant observation process (Douglas, 1976). data indicating apparent correlations between student online engagement and student achievement. However, In essence, the findings derived from the learning analytics process failed to generate the sense of urgency or motivation for change as it related to technology adoption within the institution. “Critical interpretation of the implications of data describing the institution’s current LMS use was almost entirely absent. A focus on technological issues merely generates “urgency” around technical systems and integration concerns, and fails to address the complexities and challenges of institutional culture and change.” Challenges with adoption: “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 (Kavanagh & Ashkanasy, 2006). This reality is reflected in Rogers’ theory of diffusion of innovation (1995), which attempts to model the factors that determine the adoption rate of (or, conversely, resistance to) new innovations.”
The discussion part is especially good, pointing out cultural issues that are obstacles of adoption and institutional change. This could be connected with the micro-level challenges and needs to encourage cultural change.
“social and cultural change (that is, change in habits, practices and behaviours) is not brought about by simply giving people large volumes of logical data (Kotter & Cohen, 2002). Interpretation and meaning-making, however, are contingent upon a sound understanding of the specific institutional context. As the field of learning analytics continues to evolve we must be cognizant of the necessity for ensuring that any data analysis is overlaid with informed and contextualized interpretations.”