After a comprehensive review of the research literature in the field of vocational education and training, Gessler and Siemer (2020) identified a lack of systematic knowledge synthesis or methodical literature reviews. The authors distinguish three purposes of methodical literature reviews: Interpretation, clarification, and aggregation. Our purpose is to aggregate ten years of research in vocational education and training. Within the aggregation category, two main types of reviews can be distinguished: Evidence Review and Scoping or Mapping Review. The review type we are using is the Scoping review. This review type is “used to map existing literature in a given field in terms of its nature, features, and volume” (Peters et al., 2015, p. 141).

Scoping reviews or mapping reviews are still rare in vocational education and training research, yet early systematic knowledge mapping approaches already exist: Bezerra et al. (2020) conducted a worldwide mapping of work-based learning research (period covered: all years, N=410). Very close to this Moosa and Shareefa (2020) did a mapping of the most-cited publications on workplace learning (period covered: 1970-2019, N=100). Further mapping reviews focused on digital technologies for situating vocational education and training (period covered: all years, N=17) by Dobricki et al. (2020), transversal competences (period covered: 2010-2019, N=34) by Calero López and Rodríguez-López (2020), the concept and use of microlearning in VET (period covered: 2005-2020, N=38) by Schall (2020), vocational education and training reform implementation (period covered: 1984-2017, N=177) by Caves et al. (2019), nordic research on educational and vocational guidance (period covered: 2003-2016, N=290) by Haug et al. (2019), collaborative technologies for initial vocational education (period covered: all years, N=26) by Schwendimann et al. (2018) and transfer of training (period covered: 1990-2015, N=79) by Tonhäuser & Büker (2016).

Each  mentioned mapping review captures a specific aspect of VET research with either smaller units of analysis / articles (N=17, 26, 34, 38, 79) or rather larger ones (N=100, 177, 290, 410). To date, there has been no study that has attempted to capture the entirety of the research discipline internationally. However, one national effort exists with a research approach similar to the techniques we used: Guangfen and Dongke (2017) mapped sixteen years of Chinese VET Research. Such discipline approaches necessarily leads large-scale studies with N=4,086 in the study of Guangfen and Dongke (2017) and N=5,473 in our own study.

Our overall research question: How has vocational education and training research evolved in the last decade? Within this wide focus we concentrate on four aspects: the actors, the knowledge base, the major themes and the evolution of the themes in time.

Three major databases for a bibliometric analysis are Google Scholar, Scopus and Web of Science (WoS). Google Scholar “doesn’t have a strong quality control process and simply crawls any information that is available on academic related websites” (Harzing & Alakangas, 2016, p. 802), which produce a large number of duplicate papers, called “stray citations”. This problem is not unique for Google Scholar. A cited reference search in WoS results for example also in “many stray citations, especially for academics in the Social Sciences and Humanities” (Harzing & Alakangas, 2016, p. 802). This problem is not reported for the Scopus database, so we decided to use Scopus as a source for selecting articles.

In the study, the following search terms were used either in the title or in the keywords: vocational education, vocational training, VET, TVET, skill formation, further education, further training, workplace learning, apprentice*, technical education, technical training, work-based learning, industrial education and industrial training. The search was limited to the document type article and review and the period 2011 to 2020. Another limitation was the subject area which was restricted to social sciences. Publications without country entries were excluded and the source type was limited to journals only. We limited the search to journals because only this type of document requires a peer review or quality control process to be indexed in Scopus. Trade journals, books, and conference papers may include a peer review process, but do not have to in order to be indexed. Differences in the quality standards of the documents could be a confounding variable for our study, so we included only peer reviewed journal articles (N=5,473). This study uses bibliometric analysis, a technique that is increasingly being used as a tool and basis for monitoring the research content and performance of scientific disciplines. We did a descriptive and a conceptual analysis: (1) The descriptive bibliometric analysis was carried out to identify, on the one hand, the actors, the contributing countries, institutions and authors, and, on the other hand, the knowledge base of the discipline, by means of the local most frequently used references. This analysis was done with biblioshiny for bibliometrix, a R-tool for science mapping analysis (Aria & Cuccurullo, 2017). (2) The conceptual science mapping analysis, based on a co-occurrence keyword network analysis, was performed to identify, firstly, thematic clusters within time periods. Secondly, the development of the themes over the years was recorded with an evaluation map to show the evaluation of themes between the time periods (2011-2015, 2016-2020). This analysis was done with SciMat, a software which performs science mapping analysis within a longitudinal framework (Cobo et al., 2012).

Instead of findings, we want to illustrated the central research approach and the therefore resulting findings: the conceptual analysis. The concepts (keywords) were grouped with the co-occurence network analysis to cluster networks (or themes). These clusters were compared with the means of their density and centrality parameters to identify (1) highly developed and isolated clusters, (2) emerging or declining clusters, (3) basic or transversal clusters and (4) motor clusters. With an overlapping-items graph the ending, emerging, splitting and combing of themes in time was identified. We used the Salton´s cosine index for the normalisation and the simple center algorithm as the cluster algorithm. For the evoluation and overlapping map we used the Jaccard´s index (Leydesdorff, 2008).

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. https://doi.org/10.1016/j.joi.2017.08.007 Bezerra, J. et al. (2020). A worldwide bibliometric and network analysis of work-based learning research. https://doi.org/10.1108/HESWBL-03-2020-0035 Calero López, I., & Rodríguez-López, B. (2020). The relevance of transversal competences in vocational education and training: A bibliometric analysis. https://doi.org/10.1186/s40461-020-00100-0 Caves, K. M., Baumann, S., & Renold, U. (2019). Getting there from here: A literature review on vocational education and training reform implementation. https://doi.org/10.1080/13636820.2019.1698643 Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. https://doi.org/10.1002/asi.22688 Dobricki, M., Evi-Colombo, A., & Cattaneo, A. (2020). Situating Vocational Learning and Teaching Using Digital Technologies - A Mapping Review of Current Research Literature. https://doi.org/10.13152/IJRVET.7.3.5 Gessler, M., & Siemer, C. (2020). Umbrella review: Methodological review of reviews published in peer-reviewed journals with a substantial focus on vocational education and training research. https://doi.org/10.13152/10.13152/IJRVET.7.1.5 Guangfen, Y., & Dongke, Z. (2017). An Analysis Based on Citespace III Knowledge Maps of Chinese Vocational Education Research. https://doi.org/10.1080/10611932.2017.1408304 Haug, E. H., Plant, P., Valdimarsdóttir, S., Bergmo-Prvulovic, I., Vuorinen, R., Lovén, A., & Vilhjálmsdóttir, G. (2019). Nordic research on educational and vocational guidance: A systematic literature review of thematic features between 2003 and 2016. https://doi.org/10.1007/s10775-018-9375-4 Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. https://doi.org/10.1007/s11192-015-1798-9 Leydesdorff, L. (2008). On the normalization and visualization of author co-citation data: Salton’s Cosineversus the Jaccard index. https://doi.org/10.1002/asi.20732 Moosa, V., & Shareefa, M. (2020). Science mapping the most-cited publications on workplace learning. https://doi.org/10.1108/JWL-10-2019-0119 Peters, M. D. J., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015). Guidance for conducting systematic scoping reviews. https://doi.org/10.1097/XEB.0000000000000050 Schall, M. (2020). Entstehung und Verwendung von Microlearning im Kontext des beruflichen Lernens: Ein Literatur-Review. https://doi.org/10.25162/zbw-2020-0010 Schwendimann, B. A., De Wever, B., Hämäläinen, R., & Cattaneo, A. A. P. (2018). The State-of-the-Art of Collaborative Technologies for Initial Vocational Education: A Systematic Literature Review. https://doi.org/10.13152/ijrvet.5.1.2 Tonhäuser, C., & Büker, L. (2016). Determinants of Transfer of Training: A Comprehensive Literature Review. https://doi.org/10.13152/ijrvet.3.2.4