How do we start? An approach to learning analytics adoption in higher education

Abstract

Purpose The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? Design/methodology/approach A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. Findings The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. Originality/value The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

Policy Matters: Expert Recommendations for Learning Analytics Policy

“…This finding seems to suggest that defining objectives of LA in a HEI's LA policy is not a straightforward process. It is unclear whether this is due to a data-driven (rather than question-driven) approach to LA as an observed issue in the literature [17], or due to insufficient empirical evidence proving that LA has reached its ultimate goals to enhance learning and teaching [12,42,45]. However, as the set goals for LA would inevitably affect approaches to LA [13], and hence all the issues represented through these clustered themes, an LA policy in HEIs must explicitly state the objectives of LA, despite their low ease of implementation.…”

“…In particular, the interpretation of analytics results about learners needs to consider learning design choices [27]. In light of the issues related to data and metrics, Gašević and colleagues [17] argue that approaches to LA should be question-driven rather than data-driven, and that institutions need to explore creative data sourcing to tackle learning issues, while acknowledging the inherent limitations of data.…”

“…Moreover, to close the feedback loop effectively, key users are expected to have a certain degree of data literacy that allows them to interpret data and make critical decisions as to whether and how to act on the feedback [2,31,42,46] but insufficient data literacy among students could lead to misinterpretation of LA dashboards and negative emotions as a consequence [16]. Both the constraints of time and skills can stagnate the development of a data-informed culture in decision making, which is arguably a key step to enable institutional transformation with LA [17].…”

“…Learning analytics (LA) has attracted much attention by its promise to offer insights into some of the key challenges faced by higher education institutions (HEIs) [17,45]. Examples of the challenges that LA can address include student retention, adaptive learning, personalised feedback at scale, and quality enhancement.…”

The evolving field of learning analytics research in higher education

“…The modern landscape in higher education (HE) is shaped by several critical drivers, including meeting the needs of a diverse group of students, promoting lifelong learning, enhancing student learning experience and widening access (Gašević, Tsai, Dawson, & Pardo, 2019;Siemens, Gašević, & Dawson, 2015). As online learning has succeeded in becoming an integral part this landscape, focus has moved from the use of online technologies to provide access to education to the way it can be used to increase education quality (Lee, 2017).…”