Empowering online teachers through predictive learning analytics

• Published in: British journal of educational technology Vol. 50; no. 6; pp. 3064 - 3079
• Main Authors: Herodotou, Christothea, Hlosta, Martin, Boroowa, Avinash, Rienties, Bart, Zdrahal, Zdenek, Mangafa, Chrysoula
• Format: Journal Article
• Language: English
• Published: Coventry Wiley-Blackwell 01.11.2019; Blackwell Publishing Ltd
• Subjects: Academic Achievement; Analytics; Artificial Intelligence; At Risk Students; CAI; College Faculty; Colleges & universities; Computer assisted instruction; Dashboards; Distance Education; Distance learning; Educational Practices; Electronic Learning; Empowerment; Identification; Identification methods; Learning Analytics; Machine learning; Mathematical analysis; Prediction; Predictions; Studies; Teacher Empowerment; Teachers; Teaching Methods; Undergraduate Students
• ISSN: 0007-1013; 1467-8535
• DOI: 10.1111/bjet.12853

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. Practitioner Notes 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.