Learning Analytics for Professional and Workplace Learning: A Literature Review (original) (raw)
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Examining the value of learning analytics for supporting work-integrated learning
Proceedings of the Seventh National Conference on Work-integrated Learning (ACEN’18), 2018
Among different approaches for increasing students' workplace readiness, Work-integrated Learning received significant attention by both industry and academia. However, despite its benefits, recent reports highlight many obstacles and challenges associated with the adoption of Work-integrated learning, which negatively affect students' skills development. However, increased availability of educational data and recent developments within Learning Analytics field highlighted the potentials of using analytical methods and collected data to improve Work-integrated learning adoption. In this paper, we briefly review Learning Analytics and ways in which it can be used to support Work-integrated learning adoption and practices.
Learning Analytics Dashboards for Professional Training - Challenges and Proposal
2018
Exploiting the large quantities of traces left by learners in Virtual Learning Environments (VLE) allows educators, learners and administrators to gain new insights into the learning process. Learning Analytics (LA) aims to leverage data collection, measurement, analysis and reporting data which can help users to improve the learning process. This paper presents the first results of the work we are conducting in a professional learning context to design an effective learning analytics dashboard. We show the particularities and explain the different challenges of our context that have led us to propose models to tackle it. We discuss how these models meet the requirements of our domain, and we finally give an example of indicators, measures and visualization built with educators to help them better understand the learner’s behavior.
Framing Professional Learning Analytics as Reframing Oneself
IEEE Transactions on Learning Technologies
Central to imagining the future of technologyenhanced professional learning, is the question of how data is gathered, analysed and fed back to stakeholders. The field of Learning Analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centred and instructional design, and organisational change, and so could in principle inform how data can be gathered and analysed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this paper proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of "reframing oneself" holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: (1) analysing personal and team skills profiles (skills analytics); (2) making sense of challenging workplace experiences (reflective writing analytics); and (3) reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically.
Learning Analytics in Education: Literature Review and Case Examples From Vocational Education
Scandinavian Journal of Educational Research, 2019
Vocational education and training (VET) remain overlooked in learning analytics (LA) research. This systematic literature review, using four databases and other sources, was carried out by analyzing selected 60 articles (2012-2017) to study the levels and stages of education that the reviewed LA literature examined. The review indicated that most of the analyzed papers focused on the course level, followed by student and institution levels in higher education. Few empirical studies have addressed LA use during the VET stage, particularly at the governmental level. We also considered ethical concerns and recommendations for further LA development in VET. It is suggested to use LA in knowledge transfer and integration between the classroom and workplace.
Learning analytics: Prospects and challenges
Strategic Management
Owing to its high promises for improving learning support, teaching, and learning outcomes in higher education, learning analytics has captured much interest from both academics and practitioners over the last several years. Considering that it is rooted in several disciplines, researchers and practitioners have approached learning analytics from a range of perspectives. Although many studies concerning learning analytics have highlighted its great potential for improving learning practice, there is little evidence of successful transfer of the suggested potential into the practice of higher education happening. This clearly indicates a need for rethinking many aspects of learning analytics usage: first, the goals that can be achieved, but also the actions necessary to attain these goals. The aim of the descriptive research presented in this paper is to provide an updated and realistic view of the state of the art in learning analytics, its potential benefits, and tangible challenge...
LEARNING ANALYTICS CHALLENGES TO OVERCOME IN HIGHER EDUCATION INSTITUTIONS
In book: Utilizing Learning Analytics to Support Study Success, 2019
While a large number of scientific publications explain the development of prototypes or the implementation of case studies in detail, descriptions of the challenges and proper solutions when implementing learning analytics initiatives are rare. In this chapter, we provide a practical tool that can be used to identify risks and challenges that arise when implementing LA initiatives and discuss how to approach these to find acceptable solutions. In this way, implementers are given the opportunity to handle challenges early on and avoid being surprised at a critical moment in the project, which will save time, resources and effort. We are aware that all aspects needed to successfully carry out learning analytics initiatives are co-dependent. Nonetheless, we identified and categorized the criteria necessary for implementing successful LA initiatives. We conclude this chapter with an overview of the challenges faced and possible approaches that can be taken to facilitate the successful implementation of Learning Analytics.
Special issue on: Learning Analytics (Editorial)
International Journal of Technology Enhanced Learning, 2013
Biographical notes: Verónica Rivera-Pelayo received a MSc degree in Informatics Engineering from the Universitat Politècnica de Catalunya (UPC). Currently she is a research scientist at FZI Research Center for Information Technology, working within the competence areas Knowledge & Learning and Semantic Technologies. She is further a PhD student of Prof. Rudi Studer at Karlsruhe Institute of Technology (KIT). She is involved in the EU Project MIRROR and her research is focused on the use of self-tracking tools to support reflective learning on the job. Her main interests include technology enhanced learning, learning analytics, human computer interaction and mobile technologies. María Jesús Rodríguez-Triana is currently working towards her PhD dissertation at the GSIC-EMIC interdisciplinary research group, which specialises in computer-supported collaborative learning (CSCL). She holds an MSc in Computer Science at the University of Valladolid. Her main research interests include developing technological and conceptual tools to support the management of learning situations, especially collaborative learning in authentic higher education contexts.
The current landscape of learning analytics in higher education
Learning analytics can improve learning practice by transforming the ways we support learning processes. This study is based on the analysis of 252 papers on learning analytics in higher education published between 2012 and 2018. The main research question is: What is the current scienti c knowledge about the application of fi learning analytics in higher education? The focus is on research approaches, methods and the evidence for learning analytics. The evidence was examined in relation to four earlier validated propositions: whether learning analytics i) improve learning outcomes, ii) support learning and teaching, iii) are deployed widely, and iv) are used ethically. The results demonstrate that overall there is little evidence that shows improvements in students' learning outcomes (9%) as well as learning support and teaching (35%). Similarly, little evidence was found for the third (6%) and the forth (18%) proposition. Despite the fact that the identi ed potential for fi improving learner practice is high, we cannot currently see much transfer of the suggested potential into higher educational practice over the years. However, the analysis of the existing evidence for learning analytics indicates that there is a shift towards a deeper understanding of students learning experiences for the last years. '
Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Before the emergence of computer-based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014-2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state-of-the-art and an acumens vision to inspire future labor.
Scandinavian Journal of Educational Research, 2021
Learning analytics (LA) is a fast-growing field but adoption by teachers remain limited. This paper presents the results of a review of 18 LA frameworks and discusses how they have tried to address prominent challenges in LA adoption. The results show that researchers have made significant advances in developing appropriate frameworks to conceptualize LA adoption among teachers, and have advanced considerably in connecting LA and learning theory. However, few frameworks are concretized into technological artefacts and concrete data streams. Moreover, there is a need to empirically validate and put into use the most promising existing frameworks. We hope that this review will be informative for teachers who have little LA experience but are interested in adopting LA in authentic practice.