Higher technological education specialties and graduates’ vocational status and prospects

Kostoglou, Vassilis; Vassilakopoulos, Michael; Koilias, Christos

doi:10.1108/et-03-2012-0026

Cited by 5 publications

(5 citation statements)

References 20 publications

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Section: Resultsmentioning

confidence: 99%

“…For example, in Greece, there are significant differences amongst the graduates of different specialities regarding some employment characteristics such as job satisfaction and wages. ICT professionals are comparatively in the best position, as all their indexes have above-average values (Kostoglou et al, 2013). Graduating from the Faculty of International and Political Studies increased the chances of employment with above-average earnings and job satisfaction by ca.…”

Section: Summary Of Success Indicatorsmentioning

confidence: 99%

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What drives labour market success? Empirical analysis of university graduates in Poland

Hojda

Roszkowska

Trojak

2022

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PurposeThe study aims to examine the factors that determine the success of graduates in the labour market. This success is measured in several ways, namely, above-average wages in the economy, employment in accordance with the field of study and job satisfaction. The research explores the employment outcomes of graduates from Jagiellonian University, which is the oldest and one of the best in Poland. Not only does the analysis focus on degree-related predictors such as discipline or faculty, but it is also extended to include activities undertaken during studies and family background.Design/methodology/approachThe analysis is based on data collected in the graduates' career survey, which is conducted six months after graduation. For this article, a database of over 6,000 graduates from four subsequent editions of the survey was compiled. In order to identify the predictors of graduates' success, a logistic regression model was applied.FindingsThe study reveals that graduates' success in the labour market is influenced not only by their discipline, but also by the activities undertaken during their studies, the type of studies and demographic features. Studies in natural sciences give chance for higher earnings, job matching and satisfaction. The humanities and social sciences increase only graduates’ group satisfaction. The impact of activities during studies is diversified – those matched to studies increase the job matching and those unmatched lower the earnings, job matching and satisfaction. Parents' level of education resulted in salary increase. The results also confirm the premises of behavioural theory.Research limitations/implicationsFirstly, the main research limitation is that data of one particular university's graduates are used, so the results should be interpreted in the context of Poland only. Secondly, the authors propose a precise concept of success; however, it could be refined in any future study. This limitation could be mitigated by the flexibility of the model, which guarantees that other predictors of success could be involved. Furthermore, the article was based on data collected before the COVID-19 pandemic, when the labour market was completely different, so it also could be revised now.Practical implicationsThe results of the analysis can be used when preparing the University’s educational offer. Since various predictors are implemented in the model, the information could be useful not only for future students, but also for current students, so they can make a more data-driven decision about their future careers. Additionally, University careers’ advisors are provided with knowledge on the determinants of graduates' success. Taking a broader view, the research findings are also informative for labour market institutions.Originality/valueThe paper offers an advanced analysis in a field that is under-researched in many transition countries, including Poland, i.e. graduates' entry into the workplace. Although the study provides insight into the factors that determine the labour market success of one particular university's graduates, this modelling can be applied to the empirical data of another higher education institution’s (HEI's) graduates.

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Section: Resultsmentioning

confidence: 99%

Section: Summary Of Success Indicatorsmentioning

confidence: 99%

What drives labour market success? Empirical analysis of university graduates in Poland

Hojda

Roszkowska

Trojak

2022

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“…It can also predict graduate employment, unemployment, or undetermined situations about job opportunities (Jantawan & Tsai, 2013). Using big data can help stakeholders in the educational system better understand vocational prospects for students and better assess student learning programs for occupational compatibility (Kostoglou, Vassilakopoulos, & Koilias, 2013). In a global learning environment, this type of information not only can facilitate better educational and posteducation vocational planning, but also may prove useful to organizations as they make hiring and budgeting decisions for college graduates in different disciplines.…”

Section: Learning Analytics Benefits In Educationmentioning

confidence: 99%

Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review

Nunn¹,

Avella²,

Kanai³

et al. 2016

OLJ

183

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Higher education for the 21st century continues to promote discoveries in the field through learning analytics (LA). The problem is that the rapid embrace of of LA diverts educators' attention from clearly identifying requirements and implications of using LA in higher education. LA is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of LA in higher education. Few studies have synthesized previous studies to provide an overview of LA issues in higher education. To address the problem, a systemic literature review was conducted to provide an overview of methods, benefits, and challenges of using LA in higher education. The literature review revealed that LA uses various methods including visual data analysis techniques, social network analysis, semantic, and educational data mining including prediction, clustering, relationship mining, discovery with models, and separation of data for human judgment to analyze data. The benefits include targeted course offerings, curriculum development, student learning outcomes, behavior and process, personalized learning, improved instructor performance, post-educational employment opportunities, and enhanced research in the field of education. Challenges include issues related to data tracking, collection, evaluation, analysis; lack of connection to learning sciences; optimizing learning environments, and ethical and privacy issues. Such a comprehensive overview provides an integrative report for faculty, course developers, and administrators about methods, benefits, and challenges of LA so that they may apply LA more effectively to improve teaching and learning in higher education.

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“…Lastly, institutional analytics can be used to identify post-education employment opportunities for graduates. For instance, institutions can use analytics to target education that more closely aligns with labor market needs and can be used to predict graduate employment, unemployment, or undetermined situations about job opportunities (Jantawan & Tsai, 2013;Kostoglou, Vassilakopoulos, & Koilias, 2013).…”

Section: Perceived Value and Use Of Analytics In Higher Educationmentioning

confidence: 99%

The perceptions of the meaning and value of analytics in New Zealand higher education institutions

Mahroeian

Daniel

Butson

2017

Int J Educ Technol High Educ

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This article presents the current perceptions on the value of analytics and their possible contribution to the higher education sector in New Zealand. Seven out of eight research-intensive public universities in New Zealand took part in the study. Participants included senior management and those who have some role associated with decisionmaking within higher education (N = 82). The study found inconsistent understanding of the meaning of analytics across participants. In particular, three forms of perceptions of analytics were identified: structural; functional and structural-functional. It was evident that some participants viewed analytics in its structural elements such as statistics, metrics, trends, numbers, graph, and any relevant information/data to enhance better decision-making, whereas other participants perceived the notion of analytics in terms of functional aspect; as means to an end, a process to use the data to gain insights and taking action on complex problems, yet a third group viewed analytics from both structural-functional perspectives. These kinds of perceptions have to a larger extent influenced participants' views on the value of analytics in shaping policy and practice. Also, literature has addressed a number of possible challenges associated with the large-scale institutional implementation of analytics. These challenges were: difficulties in extracting data from multiple databases, maintaining data quality, ethical and privacy issues, and lack of professional development opportunities. This article aims to broadly contribute to a better understanding of current perception and value of analytics in higher education, and in particular within the New Zealand context.

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Higher technological education specialties and graduates’ vocational status and prospects

Cited by 5 publications

References 20 publications

What drives labour market success? Empirical analysis of university graduates in Poland

What drives labour market success? Empirical analysis of university graduates in Poland

Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review

The perceptions of the meaning and value of analytics in New Zealand higher education institutions

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