scholarly journals The impact of disadvantage on higher education engagement during different delivery modes: a pre- versus peri-pandemic comparison of learning analytics data

2022 ◽  
pp. 1-11
Author(s):  
Robert Summers ◽  
Helen Higson ◽  
Elisabeth Moores
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Delivery Modes ◽  
The Impact

scholarly journals Student Vulnerability, Agency and Learning Analytics: An Exploration

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Paul Prinsloo ◽  
Sharon Slade
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Approaches To Learning ◽  
Fiduciary Duty ◽  
Power Relationship ◽  
Opting Out ◽  
Student Data ◽  
Individual Vulnerability ◽  
Regulatory Frameworks ◽  
The Impact

The increasing potential and practice of collecting, analysing and using student data necessitates that higher education institutions (HEIs) critically examine their assumptions, paradigms and practices regarding student data. There is a real danger that some current approaches to learning analytics within higher education ignore the fiduciary duty of HEIs and the impact and scope of the asymmetrical power relationship between students and the institution. In the light of increasing concerns regarding surveillance, higher education cannot afford a simple paternalistic approach to the use of student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analysed, used and shared. It is clear from literature that basic opting in or opting out does not sufficiently allow for many of the complex issues in the nexus of privacy, consent, vulnerability and agency. The notion of vulnerability (institutional and individual) allows an interesting and useful lens on the collection and use of student data. Though both institutional and individual vulnerability needs to be considered, this paper focuses specifically on student vulnerability. An earlier framework developed by Prinsloo and Slade provides tentative pointers to consider a range of responses to decrease students’ vulnerability, increase students’ agency and move students as participants in learning analytics from quantified selves to qualified selves.

A Study on the Impact of Learning Analytics to Evaluate Students' Performance in Higher Education

2018 ◽  
pp. 19-27
Author(s):  
Neerja Singh
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Learning Analytics ◽  
Educational Institutions ◽  
Massive Scale ◽  
Overall Performance ◽  
The Impact ◽  
The Way

Learning analytics is receiving increased awareness because it helps educational institutions in growing student retention, enhancing student fulfillment, and easing the burden of accountability. Although those massive-scale issues are worthy of attention, schools may additionally be inquisitive about how they can use learning analytics in their personal guides to assist their students. In this chapter, the authors define learning analytics, the way it has been used in educational establishments, what learning analytics tools are available, and how college can make use of facts in their publications to reveal scholar overall performance. Finally, the authors articulate several problems and uncertainties with the usage of learning analytics in higher education.

How Innovative Technologies Are Improving the Delivery of E-Assessment in Higher Education

2022 ◽  
pp. 137-161
Author(s):  
Paula Miranda ◽  
Pedro Isaías ◽  
Sara Pifano
Keyword(s):  
Higher Education ◽  
Mobile Technology ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Innovative Technologies ◽  
Core Practices ◽  
Technological Developments ◽  
Evolution Of Technology ◽  
The Impact

The impact of the swift evolution of technology has rippled across all areas of society with technological developments presenting solutions to some of society's greatest challenges. Within higher education, technology is welcomed with the necessary caution of a sector that is responsible for educating and empowering the future workforce. The progressive, and more recently accelerated, digitalisation of education causes the core practices and procedures associated with teaching and learning, including assessment, to be delivered in innovative formats. Technology plays a central role in the delivery of e-assessment, widening its possibilities and broadening its methods and strategies. This chapter aims to examine how innovative technologies are shaping and improving the delivery of e-assessment in the context of higher education. More specifically, it examines the role of artificial intelligence, gamification, learning analytics, cloud computing, and mobile technology in how e-assessment can be delivered.

Learning Analytics and Education Data Mining in Higher Education

2021 ◽  
pp. 108-126
Author(s):  
Samira ElAtia ◽  
Donald Ipperciel
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Large Scale ◽  
Educational Data Mining ◽  
Current Situation ◽  
Research Perspective ◽  
Education Data ◽  
The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

scholarly journals Improving the Student Experience with Learning Analytics in Construction Project Management Courses

10.29007/vrbv ◽  
2018 ◽  
Author(s):  
Peter Saunders ◽  
Ehsan Gharaie ◽  
Andrea Chester ◽  
Cathy Leahy
Keyword(s):  
Higher Education ◽  
Project Management ◽  
Self Efficacy ◽  
Learning Analytics ◽  
Student Experience ◽  
Post Intervention ◽  
Self Reflection ◽  
Related Data ◽  
The Impact ◽  
Learning Data

Learning analytics is an emerging field that has been gaining momentum in higher education. Learning analytics is the analysis and reporting of learner related data. Research has examined the benefits of learning analytics in higher education but there has been limited research conducted about the impact of showing students their own learning data. The aim of this study was to provide students with their own learner data, obtain feedback about the usefulness of this information and investigate if providing learning data leads to an increase in self-efficacy and self-reflection. The sample consisted of 78 students studying construction management, project management, and property and valuation. Students were provided with weekly learner reports that included data about their behaviour in a learning management system, their level of interaction in lectures, and their performance on assessments. A suggested target was provided toward an individualised behaviour goal, as well as comparison with both the contemporary class average and previous class averages. Students completed measures of self-efficacy and self-reflection pre and post intervention and feedback about the reports was obtained through surveys and a focus group. Results showed no significant change in self-efficacy and self-reflection, however, students reported finding the learning analytics reports helpful, believed it helped them reflect on their own learning and wanted to see more analytics in other subjects. Results support the use of learning analytics in the classroom and suggest that they may enhance the student experience.

scholarly journals Learning Analytics: Pathways to Impact

2020 ◽  
Vol 36 (6) ◽  
pp. 1-6
Author(s):  
Linda Corrin ◽  
Maren Scheffel ◽  
Dragan Gašević
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Negative Impact ◽  
Evidence Base ◽  
Personalised Learning ◽  
The Past ◽  
Design For Learning ◽  
Context Of Learning ◽  
The Impact ◽  
Effective Design

The field of learning analytics has evolved over the past decade to provide new ways to view, understand and enhance learning activities and environments in higher education. It brings together research and practice traditions from multiple disciplines to provide an evidence base to inform student support and effective design for learning. This has resulted in a plethora of ideas and research exploring how data can be analysed and utilised to not only inform educators, but also to drive online learning systems that offer personalised learning experiences and/or feedback for students. However, a core challenge that the learning analytics community continues to face is how the impact of these innovations can be demonstrated. Where impact is positive, there is a case for continuing or increasing the use of learning analytics, however, there is also the potential for negative impact which is something that needs to be identified quickly and managed. As more institutions implement strategies to take advantage of learning analytics as part of core business, it is important that impact can be evaluated and addressed to ensure effectiveness and sustainability. In this editorial of the AJET special issue dedicated to the impact of learning analytics in higher education, we consider what impact can mean in the context of learning analytics and what the field needs to do to ensure that there are clear pathways to impact that result in the development of systems, analyses, and interventions that improve the educational environment.

Learning Analytics

2020 ◽  
pp. 308-335 ◽  
Author(s):  
Matthieu Hausman ◽  
Dominique Verpoorten ◽  
Valérie Defaweux ◽  
Pascal Detroz
Keyword(s):  
Higher Education ◽  
Professional Development ◽  
Learning Environment ◽  
Learning Analytics ◽  
Case Analysis ◽  
Development Model ◽  
Professional Development Of Teachers ◽  
Professional Development Model ◽  
The University ◽  
The Impact

This chapter discusses the impact of the use of Learning Analytics on the professional development of teachers in higher education. Learning Analytics allows teachers to obtain previously inaccessible information about their students' learning activities. Based on this information, it is possible for teachers to modify their teaching strategies and the learning environment they offer to students, and they can also offer better monitoring to them. After having shed a theoretical light on the concepts used in this chapter, authors propose a case analysis relating to the experience of a teacher from the Faculty of Medicine of the University of Liège. Using a professional development model, authors then propose an analysis of the impact of Learning Analytics on the professional development of this teacher. In this case, the Learning Analytics appear as a lever for the professional development of the teacher.

Modernisation of Higher Education – Willful Action or Wishful Thinking? The Case of Bulgaria

2020 ◽  
Vol 8 (3) ◽  
pp. 3-17
Author(s):  
Elena Blagoeva
Keyword(s):  
Higher Education ◽  
Economic Crisis ◽  
Negative Impact ◽  
National Level ◽  
Sustainable Growth ◽  
Research Approach ◽  
Case Study Methodology ◽  
The Impact ◽  
The Eu

The impact of the last global economic crisis (2008) on the European economy put a strain on higher education (HE), yet it also pushed the sector towards intensive reforms and improvements. This paper focuses on the “Strategy for the Development of Higher Education in the Republic of Bulgaria 2014-2020”. With a case study methodology, we explore the strategic endeavours of the Bulgarian government to comply with the European directions and to secure sustainable growth for the HE sector. Our research question is ‘How capable is the Bulgarian HE Strategy to overcome the economic and systemic restraints of Bulgarian higher education?’. Because the development of strategies for HE within the EU is highly contextual, a single qualitative case study was chosen as the research approach. HE institutions are not ivory towers, but subjects to a variety of external and internal forces. Within the EU, this is obviated by the fact that Universities obtain their funds from institutions such as governments, students and their families, donors, as well as EU-level programmes. Therefore, to explore how these pressures interact to affect strategic action on national level, the case method is well suited as it enabled us to study the phenomena thoroughly and deeply. The paper suggests the actions proposed within the Strategy have the potential to overcome the delay, the regional isolation and the negative impact of the economic crisis on the country. Nevertheless, the key elements on which the success or failure of this Strategy hinges are the control mechanisms and the approach to implementation. Shortcomings in these two aspects of strategic actions in HE seem to mark the difference between gaining long-term benefits and merely saving face in front of international institutions.

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