Stakeholder Perspectives (Staff and Students) on Institution-Wide Use of Learning Analytics to Improve Learning and Teaching Outcomes

2020 ◽  
pp. 177-200
Author(s):  
Ann Luzeckyj ◽  
Deborah S. West ◽  
Bill K. Searle ◽  
Daniel P. Toohey ◽  
Jessica J. Vanderlelie ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Learning And Teaching ◽  
Stakeholder Perspectives ◽  
Teaching Outcomes

Learning Analytics Tool for Bulgarian School Education

2021 ◽  
Vol LXIV (4) ◽  
pp. 410-424
Author(s):  
Silvia Gaftandzhieva ◽  
◽  
Rositsa Doneva ◽  
George Pashev ◽  
Mariya Docheva ◽  
...  
Keyword(s):  
Financial Management ◽  
Learning Analytics ◽  
School Education ◽  
Accounting Systems ◽  
Learning And Teaching ◽  
Digital Repositories ◽  
Stakeholder Groups ◽  
Data Driven Decisions ◽  
Learning Data

Nowadays, schools use many information systems to automate their activities for different stakeholders’ groups – learning management systems, student diary, library systems, digital repositories, financial management and accounting systems, document processing systems, etc. The huge amount of data generated by the users of these systems, led to increased interest in the collection and analysis of data to encourage students to achieve higher results, teachers to provide personalized support and school managers to make data-driven decisions at all levels of school, and stimulates research into the application of Learning Analytics (LA) in schools. The paper presents a LA model and a software prototype of the LA tool designed for the needs of Bulgarian school education from the perspective of different stakeholder groups (students, teachers, class teachers, parents, school managers, inspectors from evaluation agencies), aiming to improve school methods of approaching and analyzing learning data. The tool allows stakeholders to track data for students’ learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. Research and experiments with the model and the LA tool under consideration are conducted based on the information infrastructure of a typical Bulgarian school.

Forschungsfeld Learning Analytics

i-com ◽  
2012 ◽  
Vol 11 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Mohamed Amine Chatti ◽  
Anna Lea Dyckhoff ◽  
Ulrik Schroeder ◽  
Hendrik Thüs
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Future Trend ◽  
Educational Institutions ◽  
Technology Enhanced Learning ◽  
Research Opportunities ◽  
Learning And Teaching ◽  
Process Learning ◽  
Enhanced Learning

Summary Learning analytics has attracted a great deal of attention in technology enhanced learning (TEL) in recent years as educational institutions and researchers are increasingly seeing the potential that learning analytics has to support the learning process. Learning analytics has been identified as a possible key future trend in learning and teaching (Johnson et al., 2011). Analytics can be a powerful tool to support learning. There are, however, a number of issues that need to be addressed before starting analytics projects. In this paper, we identify various challenges and research opportunities in the emerging area of learning analytics.

scholarly journals A Modular and Extensible Framework for Open Learning Analytics

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Arham Muslim ◽  
Mohamed Amine Chatti ◽  
Muhammad Bassim Bashir ◽  
Oscal Eduardo Barrios Varela ◽  
Ulrik Schroeder
Keyword(s):  
Learning Analytics ◽  
End Users ◽  
Open Learning ◽  
Learning And Teaching ◽  
Personal Learning ◽  
Wide Range ◽  
Flexible Architecture ◽  
Easy Integration ◽  
Visualization Techniques ◽  
Learning Data

Open Learning Analytics (OLA) is an emerging concept in the field of Learning Analytics (LA). It deals with learning data collected from multiple environments and contexts, analyzed with a wide range of analytics methods to address the requirements of different stakeholders. Due to this diversity in different dimensions of OLA, the LA developers and researchers face numerous challenges while designing solutions for OLA. The Open Learning Analytics Platform (OpenLAP) is a framework that addresses these issues and lays the foundation for an ecosystem of OLA that aims at supporting learning and teaching in fragmented, diverse, and networked learning environments. It follows a user-centric approach to engage end users in flexible definition and dynamic generation of personalized indicators. In this paper, we address a subset of OLA challenges and present the conceptual and implementation details of the analytics framework component of OpenLAP, which follows a flexible architecture that allows the easy integration of new analytics methods and visualization techniques in OpenLAP to support end users in defining indicators based on their needs in order to embed the results into their personal learning environment.

Learning analytics performance improvement design (LAPID) in higher education: Framework and concerns

2019 ◽  
Vol 7 (2) ◽  
pp. 41-55 ◽  
Author(s):  
Amir Winer ◽  
Nitza Geri
Keyword(s):  
Higher Education ◽  
Performance Improvement ◽  
Learning Analytics ◽  
Teaching Practice ◽  
Learning And Teaching ◽  
Big Data Technologies ◽  
Pedagogical Intent ◽  
Visual Description ◽  
Context Specific ◽  
Visualization Techniques

Learning Analytics Dashboards (LAD) promise to disrupt the Higher Education (HE) teaching practice. Current LAD research portrays a near future of e-teaching, empowered with the ability to predict dropouts, to validate timely pedagogical interventions and to close the instructional design loop. These dashboards utilize machine learning, big data technologies, sophisticated artificial intelligence (AI) algorithms, and interactive visualization techniques. However, alongside with the desired impact, research is raising significant ethical concerns, context-specific limitations and difficulties to design multipurpose solutions. We revisit the practice of managing by the numbers and the theoretical origins of dashboards within management as a call to reevaluate the “datafication” of learning environments. More specifically, we highlight potential risks of using predictive dashboards as black boxes to instrumentalize and reduce learning and teaching to what we call “teaching by the numbers”. Instead, we suggest guidelines for teachers’ LAD design, that support the visual description of actual learning, based on teachers’ prescriptive pedagogical intent. We conclude with a new user-driven framework for future LAD research that supports a Learning Analytics Performance Improvement Design (LAPID).

scholarly journals Learning Analytics: Learning to Think and Make Decisions

2019 ◽  
Vol 16 ◽  
Author(s):  
Airina Volungevičienė ◽  
Josep Maria Duart ◽  
Justina Naujokaitienė ◽  
Giedrė Tamoliūnė ◽  
Rita Misiulienė
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Learning Analytics ◽  
Online Teaching ◽  
Teacher Inquiry ◽  
Learning Design ◽  
Teaching Process ◽  
Research Results ◽  
Learning And Teaching ◽  
Specific Analysis

The research aims at a specific analysis of how learning analytics as a metacognitive tool can be used as a method by teachers as reflective professionals and how it can help teachers learn to think and come down to decisions about learning design and curriculum, learning and teaching process, and its success. Not only does it build on previous research results by interpreting the description of learning analytics as a metacognitive tool for teachers as reflective professionals, but also lays out new prospects for investigation into the process of learning analytics application in open and online learning and teaching. The research leads to the use of learning analytics data for the implementation of teacher inquiry cycle and reflections on open and online teaching, eventually aiming at an improvement of curriculum and learning design. The results of the research demonstrate how learning analytics method can support teachers as reflective professionals, to help understand different learning habits of their students, recognize learners’ behavior, assess their thinking capacities, willingness to engage in the course and, based on the information, make real time adjustments to their course curriculum.

Strategic value realization framework from learning analytics: a practical approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Riyaz Abdullah Sheikh ◽  
Surbhi Bhatia ◽  
Sujit Gajananrao Metre ◽  
Ali Yahya A. Faqihi
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Skill Training ◽  
Quality Data ◽  
Future Research ◽  
Content Type ◽  
Data Infrastructure ◽  
Learning And Teaching ◽  
Strategic Value ◽  
Value Realization

PurposeIn spite of the popularity of learning analytics (LA) in higher education institutions (HEIs), the success rate and value gained through LA projects is still little and unclear. The existing research on LA focusses more on tactical capabilities rather than its effect on organizational value. The key questions are what are the expected benefits for the institution? And how the investment in LA can bring tangible value? In this research, the authors proposed a value realization framework from LA extending the existing framework of information technology value.Design/methodology/approachThe study includes a detailed literature review focusing on the importance, existing frameworks and LA adoption challenges. Based on the identified research gap, a new framework is designed. The framework depicts the several constructs and their relationships focusing on strategic value realization. Furthermore, this study includes three case studies to validate the framework.FindingsThe framework suggests that leveraging LA for strategic value demands adequate investment not only in data infrastructure and analytics but also in staff skill training and development and strategic planning. Universities are required to measure the strategic role of LA and spend wisely in quality data, analytical tools, skilled staff who are aware of the latest technologies and data-driven opportunities for continuous improvement in learning.Originality/valueThe framework permits education leaders to design better strategies for attaining excellence in learning and teaching, and furnish learners with new data to settle on the most ideal decisions about learning. The authors believe that the appropriation of this framework and consistent efficient interest in learning analytics by the higher education area will prompt better results for learners, colleges and more extensive society. The research also proposes two approaches and eleven research agendas for future research based on the framework. The first is based on the constructs and their relationships in LA value creation, whereas the later one focusing on identifying problems associate with it.

Unpacking the ‘Learning' in Student-Facing Analytics

2019 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Joy Galaige ◽  
Geraldine Torrisi-Steele
Keyword(s):  
University Students ◽  
Knowledge Base ◽  
Learning Analytics ◽  
Zone Of Proximal Development ◽  
Early Research ◽  
Learning Processes ◽  
Proximal Development ◽  
Learning And Teaching ◽  
Effective Learning ◽  
Design And Implementation

Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to develop control over their learning, and subsequently supporting more effective learning. However, early research on the effectiveness of student-facing analytics is giving mixed results and is casting some doubt over the usefulness of student-facing learning analytics. One factor contributing to doubt over the value of student-facing learning analytics is that their design and implementation remains firmly rooted in the technical domain, with virtually no grounding in the knowledge base of learning and teaching. If the growing investment of resources into the development of student-facing learning analytics systems is to be fruitful, then there is an obvious, urgent need to re-position student-facing learning analytics within learning and teaching frameworks. With this in mind, we use Schraw & Dennison's model of metacognition and Vygotsky's zone of proximal development to unpack the ‘learning' in student-facing analytics and work towards an understanding of student-facing analytics that is more conducive to supporting metacognition and effective learning.

scholarly journals Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners

2020 ◽  
Author(s):  
Dirk Ifenthaler ◽  
David Gibson ◽  
Doreen Prasse ◽  
Atsushi Shimada ◽  
Masanori Yamada
Keyword(s):  
Delphi Study ◽  
Learning Analytics ◽  
Expert Panel ◽  
Panel Discussion ◽  
Research Gaps ◽  
Ethical Practices ◽  
Policy Makers ◽  
Learning And Teaching ◽  
The Impact ◽  
Further Development

AbstractThis paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field.

Sign in / Sign up

Export Citation Format

Share Document