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.
This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes.
What is already known about this topic
Pedagogical and personal support to students is a significant responsibility of online teachers.
Student's support is a challenging activity due to the lack of face‐to‐face interactions.
Predictive learning analytics (PLA) can identify students at risk of failing their studies.
What this paper adds
One of the few large‐scale studies is available for examining the impact of analytics on student's performance.
Teachers' usage of PLA was significantly related to better learning outcomes.
Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access.
Implications for practice and/or policy
PLA can empower online teachers and complement the teaching practice.
PLA can help in the identification and proactive intervention of students at risk of failing their studies.
Actions should be taken to motivate and engage online teachers with PLA.
The choice of mobile applications (apps) for learning has been heavily relied on customer and teacher reviews, designers’ descriptions, and alignment with existing learning and human-computer interaction theories. There is limited empirical evidence to advise on the educational value of mobile apps as these are used by children. Understanding the impact of mobile apps on young children’s learning is timely given the lack of evidence-based recommendations that could guide parents and teachers in selecting apps for their children. In this paper, we present the results of a series of randomised control trial (RCTs) with 376 children aged 5 to 6 years old who interacted with two maths apps in three schools in the UK. Pre/post-test comparisons revealed learning gains in both the control and intervention groups, suggesting that the selected applications are equally good to standard maths practice. Implications for the selection and use of mobile apps are discussed.
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