Combining Learning Analytics with Job Market Intelligence to Support Learning at the Workplace

Berg, Alan; Braňka, Jiří; Kismihók, Gábor

doi:10.1007/978-3-319-46215-8_8

Cited by 5 publications

(3 citation statements)

References 59 publications

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“…While standards for data models and data collection, such as xAPI (Experience API), exist (Kevan and Ryan 2016), learning analytics research and development need to clearly define standards for reliable and valid measures, informative visualisations, and design guidelines for pedagogically effective learning analytics interventions (Seufert et al 2019). In particular, personalised learning environments are increasingly in demand and are valued in higher education institutions for creating tailored learning packages optimised for each individual learner based on their personal profile, containing information such as their geo-and socio-demographic backgrounds (Lacave et al 2018), previous qualifications (Daud et al 2017), their engagement in the recruitment journey (Berg et al 2018), activities on websites (Seidel and Kutieleh 2017), and tracking information on their searches (Macfadyen and Dawson 2012).…”

Section: Discussionmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

200

109

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Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

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Section: Discussionmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

200

109

View full text Add to dashboard Cite

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“…Methods need to be in place to co-develop these course objectives with the students, which require more flexible curricula. Nevertheless, the design of GS interventions may share some similarities with other educational approaches such as the use of e-Portfolios (Berg et al, 2018) to develop a 'learning journey'.…”

Section: Bottlenecks For Goal Setting In Higher Educationmentioning

confidence: 99%

“…Therefore, it may be important to make the GS intervention part of the curriculum, so both students and educational institutions benefit from the positive outcomes (Schippers, 2020;Clonan et al, 2004). GS may be notably useful when learners are in a transitional period of their lives, as for instance progressing from school to higher education (Schippers & Ziegler 2019;Wilson, 2011), or from higher education to the labour market (Schippers, 2020;Berg et al, 2018). However the effects of making GS mandatory should be further investigated, as ownership is critical to the success of GS.…”

Section: Integrating Goal Setting In the Academic Programmentioning

confidence: 99%

Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?

Kismihók

Zhao

Schippers

et al. 2020

Proceedings of the 12th International Conference on Computer Supported Education

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This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper shows that the main barriers for goal setting applications in education are not related to the technology, the available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack of students' goal setting skills and abilities, and the current curriculum design, which, especially in the observed higher education institutions, provides little support for goal setting interventions.

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Changing Job Skills in a Changing World

Napierała

Kvetan

2023

Handbook of Computational Social Science for Policy

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Digitalization, automation, robotization and green transition are key current drivers changing the labour markets and the structure of skills needed to perform tasks within jobs. Mitigating skills shortages in this dynamic world requires an adequate response from key stakeholders. However, recommendations derived from the traditional data sources, which lack granularity or are available with a significant time lag, may not address the emerging issues rightly. At the same time, society’s increasing reliance on the use of the Internet for day-to-day needs, including the way individuals search for a job and match with employers, generates a considerable amount of timely and high granularity data. Analysing such nontraditional data as content of online job advertisements may help understand emerging issues across sectors and regions and allow policy makers to act accordingly. In this chapter, we are drawing on experience setting the Cedefop project based on big data and presenting examples of other numerous research projects to confirm the potential of using nontraditional sources of information in addressing a variety of research questions related to the topic of changing skills in a changing world.

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Combining Learning Analytics with Job Market Intelligence to Support Learning at the Workplace

Cited by 5 publications

References 59 publications

Utilising learning analytics to support study success in higher education: a systematic review

Utilising learning analytics to support study success in higher education: a systematic review

Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?

Changing Job Skills in a Changing World

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