The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study

Abstract

A vast number of studies, yet mostly small-scale reported exciting innovations and practices in the field of learning analytics. Whilst these studies provide substantial insights, there are still relatively few studies that have explored how the stakeholders' (i.e., teachers, students, researchers, management) perspectives and involvement influence largescale and institutional-wide adaptation of learning analytics. This study reports on one such large-scale and long-term implementation of Predictive Learning Analytics (PLA) spanning a period of four years at a distance learning university. OU Analyse (OUA) is the PLA system used in this study, providing predictive insights to teachers about students and their chance of passing a course. Over the last four years, OUA has been accessed by 1,182 unique teachers and reached 23,640 students in 231 undergraduate online courses. The aim of this study is twofold: (a) to reflect on the macro-level of adoption by detailing usage, challenges and factors facilitating adoption at the organisational level, and (b) to detail the micro-level of adoption, that is the teachers' perspectives about OUA. Amongst the factors critical to the scalable PLA implementation were: the faculty's engagement with OUA, teachers as "champions", evidence generation and dissemination, digital literacy, and conceptions about teaching online.

Defining the Boundaries Between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence

“…With the rise and availability of big data in education and AI, substantial leaps in the conceptual, theoretical, and evidence-based understanding of learning and teaching have been made in the four fields discussed. However, as highlighted by a range of reviews, most of these innovations have been localized in small lab studies, or in a single course, or specific context, with limited large-scale adoption within and across institutions (Viberg et al, 2018;Herodotou et al, 2020).…”

“…Several higher education institutions and distance learning providers have started to explore the use of LA dashboards that can display learner and learning behavior to teachers and instructional designers in order to provide more real-time or just-in-time support to students (Jivet et al, 2018;Herodotou et al, 2020). Furthermore, several institutions have developed predictive LA approaches to help identify, as early as possible, students who may be considered "at risk" of failing, and which of those students may need additional support (Viberg et al, 2018;Herodotou et al, 2020). Some institutions are also currently experimenting with providing LA data directly to students in order to support their learning processes and self-regulation (Winne, 2017;Rienties et al, 2019).…”

Addressing student attrition within higher education online programs through a collaborative community of practice

“…There are established and emerging models of student retention that can calculate a student's risk profile for success, all of which depend upon reliable data collection. One example is at the Open University in the United Kingdom, where OU-Analyse software has been developed that integrates over 70 identified 'key factors' -both static factors (identified at the time commencing study) and changing factors during the academic year that can predict, with alarming accuracy, the outcome for students in different programs (Herodotou et al, 2020). Efforts at the University of Tasmania to achieve a similar, nuanced approach to understand specific student cohorts, that aim to significantly impact university retention rates, are also understood and need to be institution-wide in scope, probing all levels of collected data.…”

Student Learning Experience and Student Retention Strategies: A Transformative Approach to Quality Education

“…This modern decade quest of universities to increase education access to students who are located in Aarts et al 2020). To a large extent, this approach to the delivery of the higher education has addressed the discrimination that existed in the delivery of higher education since the colonial period and has eliminated the geographical barriers faced by students in acquiring high quality education (Chauhan 2020;Herodotou et al 2020). Student retention in the face-to-face courses is usually higher than the distance and flexible learning courses.…”

“…Student retention in the face-to-face courses is usually higher than the distance and flexible learning courses. The instructors and higher education leaders can design the curriculum and instruction for the distance learning courses by considering the constraints, challenges, and the background of the distance learners (Chauhan 2020;Herodotou et al 2020). For instance, as compared to the faceto-face students, the distance learners are not able to access the campus resources as the faceto-face students can access, and this places them at a resource disadvantage position.…”