Editorial learning analytics in higher education – Stakeholders, strategy and scale

2021 ◽  
pp. 100833
Author(s):  
Dragan Gašević ◽  
Yi-Shan Tsai ◽  
Hendrik Drachsler
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Education Stakeholders

scholarly journals Higher Education Stakeholders’ Views on Learning Analytics Policy Recommendations for Supporting Study Success

2019 ◽  
Vol 1 (1) ◽  
pp. 28 ◽  
Author(s):  
Dirk Ifenthaler ◽  
Jane Yin-Kim Yau
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Research Question ◽  
Successful Implementation ◽  
Policy Recommendations ◽  
Study Success ◽  
Main Research ◽  
Success In Higher Education ◽  
Education Stakeholders

<p class="0abstract"><span lang="EN-AU">Learning analytics show promise to support study success in higher education. Hence, they are increasingly adopted in higher education institutions. This study examines higher education experts’ views on learning analytics utilisation to support study success. Our main research question was to investigate how ready higher education institutions are to adopt learning analytics. We derived policy recommendations from an international systematic review of the last five years of learning analytics research. Due to the lack of rigorous learning analytics research and adoption in Germany, this study focusses on the German university context and examines how ready German university stakeholders are to adopt learning analytics. In order to validate the policy recommendations, we conducted an interview study from June to August 2018 with 37 German higher education stakeholders. The majority of participants stated that their institutions required further resources in order to adopt learning analytics but were able to identify what these resources were in order for successful implementation.</span></p>

Challenges of Learning Analytics Execution in the Educational System

2018 ◽  
pp. 28-40
Author(s):  
Manoj Kumar Singh
Keyword(s):  
Higher Education ◽  
Educational System ◽  
Learning Analytics ◽  
First Century ◽  
Teachers And Students ◽  
Twenty First Century ◽  
Essential Set ◽  
Educational Enterprise ◽  
Education Stakeholders

Education for the twenty-first century continues to promote discoveries in the field through learning analytics. The problem is that the rapid embrace of learning analytics diverts educators' attention from clearly identifying requirements and implications of using learning analytics in higher education. Learning analytics is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of learning analytics in higher education. This chapter addresses the above problem and design of learning analytics implementations: the practical shaping of the human tactics involved in taking on and using analytic equipment, records, and reviews as part of an educational enterprise. This is an overwhelming but equally essential set of design choices from the ones made within the advent of the learning analytics structures themselves. Finally, this chapter's implications for learning analytics teachers and students and areas requiring further studies are highlighted.

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.

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