Learning analytics in higher education: a preponderance of analytics but very little learning?

Using Learning Analytics to Evaluate the Clinical Education Podcast Format

Learning analytics has been rigorously applied to various forms of e-learning, but not to the evaluation of clinical education podcasts. We created a podcast series using the Anchor by Spotify platform, uploading an episode every 2 weeks starting on March 30, 2021. We examined analytics data using a censoring date of November 1, 2022. Based on 29,747 total plays, average audience retention declined 10%/minute until an inflection point at 2.5 minutes, followed by a steady decline of 1.8%/minute. With a maximum episode length of 17 minutes, we did not identify a limit on learner attention for short-form podcasts.

Untangling connections between challenges in the adoption of learning analytics in higher education

Potential benefits of learning analytics (LA) for improving students' performance, predicting students' success, and enhancing teaching and learning practice have increasingly been recognized in higher education. However, the adoption of LA in higher education institutions (HEIs) to date remains sporadic and predominantly small in scale due to several socio-technical challenges. To better understand why HEIs struggle to scale LA adoption, it is needed to untangle adoption challenges and their related factors. This paper presents the findings of a study that sought to investigate the associations of adoption factors with challenges HEIs face in the adoption of LA and how these associations are compared among HEIs at different scopes of adoption. The study was based on a series of semi-structured interviews with senior managers in HEIs. The interview data were thematically analysed to identify the main challenges in LA adoption. The connections between challenges and other factors related to LA adoption were analysed using epistemic network analysis (ENA). From senior managers' viewpoints, ethical issues of informed consent and resistance culture had the strongest links with challenges of learning analytic adoption in HEI; this was especially true for those institutions that had not adopted LA or who were in the initial phase of adoption (i.e., preparing for or partially implementing LA). By contrast, among HEIs that had fully adopted LA, the main challenges were found to be associated with centralized leadership, gaps in the analytic capabilities, external stakeholders, and evaluations of technology. Based on the results, we discuss implications for LA strategy that can be useful for institutions at various stages of LA adoption, from early stages of interest to the full adoption phase.

Learning analytics dashboard: a tool for providing actionable insights to learners

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

Privacy and E-Learning: A Pending Task

Most educational software programs use and gather personal information and metadata from students. Additionally, most of the educational software programs are no longer operated by the learning institutions but are run by third-party agencies. This means that in the decade since 2020, information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems and online learning tools is increasingly in custody of cloud computing platforms, software-as-a-service providers, and learning tool vendors. There is an increasing will to use all the data and metadata from the activity of the students for research, to develop education management strategies, pedagogy approaches, and develop behavior control tools or learning tools informed by behavior analysis from learning analytics. Many times, these studies lack the ethical and moral perspective. In addition, there is an increasing number of cases in which this information has leaked or has been used in a shady way. Additionally, this information will be around for a long time, tied to the future digital profiles of the students whose data has been leaked. This paper hypothesizes that there has been an ongoing process of technological evolution that leads to a loss of control over personal information, which makes it even more difficult to protect user confidentiality and ensuring privacy, that data surveillance has entered the world of education, and that the current legal frameworks are not enough to really protect the student’s personal information. The paper analyzes how this situation came to pass, and why this is wrong. We conclude with some proposals to address it from its different root dimensions: technical, cultural, legal, and organizational.