Predictive Learning Analytics "At Scale": Towards Guidelines to Successful Implementation in Higher Education Based on the Case of the Open University UK.

@article{Herodotou2019PredictiveLA,
  title={Predictive Learning Analytics "At Scale": Towards Guidelines to Successful Implementation in Higher Education Based on the Case of the Open University UK.},
  author={Christothea Herodotou and B. Rienties and Barry Verdin and Avinash Boroowa},
  journal={Journal of learning Analytics},
  year={2019},
  volume={6},
  pages={85-95}
}
Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders (i.e., managers, teachers, students) about PLA. This paper presents an “at scale” implementation of PLA at a distance learning higher education… Expand

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References

SHOWING 1-10 OF 34 REFERENCES
A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described asExpand
Implementing predictive learning analytics on a large scale: the teacher's perspective
TLDR
Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk. Expand
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success
TLDR
The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts, and require consideration before the log-data can be merged to create a generalized model for predicting academic success. Expand
Implementing a Learning Analytics Intervention and Evaluation Framework: what works?
TLDR
This chapter argues that the largest challenge for learning analytics research and practice still lies ahead of us: using learning analytics modelling, which types of interventions have a positive impact on learners’ Attitudes, Behaviour and Cognition. Expand
Making Sense of Learning Analytics Dashboards: A Technology Acceptance Perspective of 95 Teachers
TLDR
Teachers’ readiness for learning analytics visualisations amongst 95 experienced teaching staff at one of the largest distance learning universities is explored by using an innovative training method called Analytics4Action Workshop, indicating that participants appreciated the interactive and hands-on approach, but at the same time were skeptical about the perceived ease of use of learning analytics tools they were offered. Expand
Understanding academics’ resistance towards (online) student evaluation
Many higher educational institutions and academic staff are still sceptical about the validity and reliability of student evaluation questionnaires, in particular when these evaluations are completedExpand
Developing a model and applications for probabilities of student success: a case study of predictive analytics
TLDR
The use of predictive analytics is demonstrated to generate a model of the probabilities of success and retention at different points in a student journey, to help tailor student support to individual students and therefore improve low retention in open access distance education. Expand
Rethinking learning analytics adoption through complexity leadership theory
TLDR
The framing of LA adoption in complexity leadership theory (CLT) to study the overarching system dynamics is proposed and suggests there is a need to broaden the focus of research in LA adoption models to move on from small-scale course/program levels to a more holistic and complex organizational level. Expand
Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment
TLDR
Predicting student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour is revealed. Expand
A study of teaching presence and student sense of learning community in fully online and web-enhanced college courses
TLDR
Factor and regression analysis indicate a significant link between students' sense of learning community and effective instructional design and “directed facilitation” on the part of course instructors, and highlights interesting differences between online and classroom environments. Expand
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