Learning Analytics in Higher Education

2017 ◽  
Vol 43 (5) ◽  
pp. 9-135 ◽  
Author(s):  
Jaime Lester ◽  
Carrie Klein ◽  
Huzefa Rangwala ◽  
Aditya Johri
Keyword(s):  
Higher Education ◽  
Learning Analytics

A four-country cross-case analysis of academic staff expectations about learning analytics in higher education

2021 ◽  
Vol 49 ◽  
pp. 100788
Author(s):  
Kaire Kollom ◽  
Kairit Tammets ◽  
Maren Scheffel ◽  
Yi-Shan Tsai ◽  
Ioana Jivet ◽  
...  
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Academic Staff ◽  
Case Analysis ◽  
Cross Case Analysis

Mapping the study of learning analytics in higher education

2018 ◽  
Vol 37 (10-11) ◽  
pp. 1142-1155 ◽  
Author(s):  
Jinzhuo Zhang ◽  
Xi Zhang ◽  
Shan Jiang ◽  
Patricia Ordóñez de Pablos ◽  
Yongqiang Sun
Keyword(s):  
Higher Education ◽  
Learning Analytics

scholarly journals Widening the net to reduce the debt: Reducing student debt by increasing identification of completely disengaged students

2020 ◽  
Author(s):  
Neil Van Der Ploeg ◽  
Kelly Linden ◽  
Ben Hicks ◽  
Prue Gonzalez
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Predictive Model ◽  
Hybrid Model ◽  
Learning Analytics ◽  
Teaching Staff ◽  
Student Debt ◽  
Education Standards ◽  
Performance Based Funding ◽  
Panel Review

Student Retention and Attrition guidelines are part of the Federal Government’s performance based funding framework. One of the recommendations from the Higher Education Standards Panel review is to consider changing students’ enrolment prior to census date when a certain level of engagement is not met. This study investigates this recommendation by trialing and testing a model to see if completely disengaged students are able to be retrospectively identified as at risk of failing all subjects. Using learning analytics alone to create a predictive model at scale proved to be very difficult. When applied to session 1 of 2019, even the strictest criteria included five false positives out of 17 identified students. There is promise, however, that a hybrid model of learning analytics with additional oversight from teaching staff could be a solution, but this needs further research.

Use of Learning Analytics in China

2019 ◽  
pp. 158-173
Author(s):  
Konstantinos Skampagiannis ◽  
Athanasios Pletsas
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Educational Environment ◽  
Chinese Higher Education ◽  
General Definition ◽  
Definition Of ◽  
Higher Institutions ◽  
Technical Basis ◽  
Successful Students ◽  
Shed Light

This chapter per the authors examines the feasibility of a learning analytics tool in the Chinese Higher Institutions educational environment. At first, the general definition of learning analytics is examined. Additionally, the authors shed light on case studies of universities that have already implemented learning analytics. Moreover, the Chinese educational environment is examined through a thorough analysis of the learning analytics necessity. Based on the literature review, a learning analytics tool is proposed. In a technical basis, the tool is a combination of ELLI or Effective Lifelong Learning Inventory, a dispositional learning analytics tool and a recommender system. The ultimate function of the tool is that it links students with a specific educational profile with successful students with similar profiles. Finally, the author identified the key limitations of the prototype and performed a general analysis on the tools goals and expectations in the Chinese Higher Education Institutions.

Developing a Framework for the Effective Use of Learning Analytics

2016 ◽  
pp. 88-118
Author(s):  
Collette Gavan
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Learning Analytics ◽  
Best Practice ◽  
Predictive Analytics ◽  
Data Visualisation ◽  
The United Kingdom ◽  
Current Impact ◽  
Effective Use ◽  
Initial Framework

Research and experimentation is uncovering forms of best practice and possible factors on which to centre the analysis of students in an effective way, however learning analytics has yet to be comprehensively implemented country-wide in the United Kingdom. The chapter explores the current impact of learning analytics in higher education at mome discusses and observes the current vacancies with which a framework enabled to function with data visualisation could be utilised. The deliverable seeks to design an initial framework that has the potential to be utilised in a higher education setting for more effective and insightful decision making with regards to learner retention and engagement. This framework will combine the theory and scientific action of predictive analytics with a comparison of the most suitable data visualisation toolsets that are currently available in open-source software.

Here Be Dragons

Big Data ◽  
2016 ◽  
pp. 1717-1735
Author(s):  
Paul Prinsloo ◽  
Sharon Slade
Keyword(s):  
Higher Education ◽  
Student Engagement ◽  
New Deal ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Student Experience ◽  
Open University ◽  
Student Responsibility ◽  
Student Data ◽  
General Data

Learning analytics is an emerging but rapidly growing field seen as offering unquestionable benefit to higher education institutions and students alike. Indeed, given its huge potential to transform the student experience, it could be argued that higher education has a duty to use learning analytics. In the flurry of excitement and eagerness to develop ever slicker predictive systems, few pause to consider whether the increasing use of student data also leads to increasing concerns. This chapter argues that the issue is not whether higher education should use student data, but under which conditions, for what purpose, for whose benefit, and in ways in which students may be actively involved. The authors explore issues including the constructs of general data and student data, and the scope for student responsibility in the collection, analysis and use of their data. An example of student engagement in practice reviews the policy created by the Open University in 2014. The chapter concludes with an exploration of general principles for a new deal on student data in learning analytics.

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