Learning Analytics Tools in Higher Education: Adoption at the Intersection of Institutional Commitment and Individual Action

Learning Analytics in Higher Education

“…Our work has extended these models by underscoring the importance of inclusion and transparency as trust‐building organizational levers that can encourage adoption of learning analytics tools (Klein et al., in press). Trust building is a foundational activity that is especially important when implementing learning analytics tools, which are often less trusted because of concerns related to bias and predictive outcomes.…”

“…We argue that organizational factors that create barriers and opportunities for learning analytics implementation and adoption in higher education are rooted in issues of institutional structures, commitment, resources, readiness, and capacity and a lack of incentives and rewards (Arnold, Lonn, & Pistilli, ; Austin, ; Bichsel, ; Kezar & Lester, ; Macfadyen & Dawson, ; Norris & Baer, ). Implementation of learning analytics also requires attention to a host of technological factors including provision of data, technical data analytics expertise, cross‐organization collaboration, leadership, and attention to organizational climate (Arnold et al., ; Bichsel, ; Ferguson, ; Klein, Lester, Rangwala, & Johri, in press; Norris & Baer, ). The chapter concludes with an overview of technological aspects of learning analytics tools and individual decision making, including a review of innovation adoption and tool alignment.…”

“…Student retention is the problem, and learning analytics tools are a strategy to address the problem. In our work, the definition and meaning of the problem may be shared across constituents but the use of learning analytics as a strategy is not (Klein, Lester, Rangwala, & Johri, in press, , ). Advisors and faculty do not view an online tool as the intervention but more hands‐on advising and mentoring as the effective practice.…”

“…Lack of proper infrastructure, resources, personnel, and a culture of learning analytics readiness can negatively affect deployment of these tools on campus (Arnold, Lonn, & Pistilli, ; Norris & Baer, ). Implementation of learning analytics also requires attention to a host of technological factors to include provision of data, technical data analytics expertise, cross‐organization collaboration, leadership, and attention to organizational climate (Arnold et al., ; Bichsel, ; Ferguson, ; Klein, Lester, Rangwala, & Johri, in press; Norris & Baer, ).…”

“…Technological constraints exist on multiple levels including a lack of appropriate infrastructure, adequate support and technical staff, adequate levels of data, and lack of alignment and integration of learning analytics tool data into already existing technologies (Arnold et al., ; Bichsel, ; Klein et al., in press; Norris & Baer, ). Individual decisions by faculty and advisors to use or refuse learning analytics tools hinge on awareness, interest, time, training, disciplinary socialization and personal beliefs, and trust (Amey, ; Austin, , ; Bichsel, ; Dahlstrom et al., ; Fairweather, ; Hora & Holden, ; Klein et al., in press, ; Norris & Baer, ). Students face similar decision points based on trust, alignment, understanding, and usefulness of the data they use and interpret (Arnold & Pistilli, ; Park & Jo, , Verbert et al., , ).…”