A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective

Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdráhal, Zdeněk; Hlosta, Martin

doi:10.1007/s11423-019-09685-0

Cited by 110 publications

(69 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This might be driven by students’ personal preferences (e.g., [27, 28]) or by the teaching activities prescribed and/or preferred by different disciplines and programmes (see e.g. [8, 41]). Instead we find that students who are engaged with learning tend to be engaged with all learning activities and systems; engagement appears to be a holistic phenomenon (Tables 3 and 4).…”

Section: Discussionmentioning

confidence: 99%

Student engagement and wellbeing over time at a higher education institution

et al. 2019

View full text Add to dashboard Cite

Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students’ subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records.

show abstract

Section: Discussionmentioning

confidence: 99%

Student engagement and wellbeing over time at a higher education institution

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Through a mixed method study at a distance learning institution, Herodotou et al () found mixed effects on student's performance when 240 teachers in 10 modules were given access to PLA, yet usage analysis showed that teachers made only limited use of PLA in their practice that could explain these mixed effects. Five follow‐up interviews revealed that teachers had positive views about the use of PLA in teaching as they recognized their usefulness for complementing the teaching practice and being “on top of things.” Also, Herodotou, Rienties, Boroowa, Zdrahal, and Martin (n.d.) in a multi‐method study with 59 teachers and more than 1300 students identified that the teachers' overall engagement with predictive data was the second most significant factor explaining the student's performance, following the previous best performance.…”

Section: Theoretical Backgroundmentioning

confidence: 95%

Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Additional support is garnered for the data-driven university in that the sector practice now places a greater emphasis on learner analytics and their proposed ability to predict when a student needs help or is at risk of dropping out (Herodotou et al 2019 ). Whilst the encouragement to utilise this initiative originated in the exosystem, it could also be argued that this puts the microsystem) (the student) in the central focus.…”

Section: The Shifting Higher Education Landscape—the Driving Force Bementioning

confidence: 99%

The Postdigital University: Do We Still Need Just a Little of That Human Touch?

2020

View full text Add to dashboard Cite

An increasing body of literature considers the role of belonging and social connectivity in undergraduate student success. The core tenet of this research is that relationships are crucial to the development of a sense of belonging. However, within the Higher Education (HE) sector, our processes, and therefore how we interact with students, are becoming more and more automated. None more so than during the Covid-19 pandemic and the ‘new normal’ in HE. This paper considers how we, as a profession, might support each student’s developing sense of belonging within a sector that is shifting towards increased digitalisation. This is achieved through considering the political agenda that drives the creation of digital education and some of the assumptions that underpin the movement towards it. As a result, a theoretical platform is created to consider the areas where digitisation impacts on teaching staff, and on students, and how this relates to each student’s sense of belonging within HE. The inclusion of two case studies has provided the opportunity to answer two key questions: (1) What is important to students developing a personal sense of belonging in HE during their first few weeks in a university? (2) How can the differentiated human touch be provided by ‘third space’ professionals both in person and virtually?

show abstract

A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective

Cited by 110 publications

References 53 publications

Student engagement and wellbeing over time at a higher education institution

Student engagement and wellbeing over time at a higher education institution

Empowering online teachers through predictive learning analytics

The Postdigital University: Do We Still Need Just a Little of That Human Touch?

Contact Info

Product

Resources

About