research topics for tech voc students

Research Methods in Technical Vocational Education and Training

• First Online: 01 January 2014

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• Martin Fischer 6 , 7 , 8 ,
• Walter Jungmann 7 ,
• Anne Schreiber 7 &
• Magdalene Ziegler 7  

Part of the book series: New Frontiers of Educational Research ((NFER))

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TVET research is faced with the difficulty to justify and to develop an original research and development concept corresponding to the subject of research. Various interrelated research fields require a broad spectrum of TVET research methods. Since 1990s methodological discussion about the subject-related characteristics of TVET research and application of TVET research methods have been started. It showed that, when the research deals with the contents of occupational work and education, new subject-related research methods are needed. In this chapter, the authors present seven categories of research methods concretely: comprehension methods, survey methods, observation methods, case studies and studies of work, experimental methods, designing and developing methods, and evaluation methods. Particularly in the area of vocational scientific research, the subject-related research and development methods are of great importance.

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Fischer, M., Jungmann, W., Schreiber, A., Ziegler, M. (2014). Research Methods in Technical Vocational Education and Training. In: Zhao, Z., Rauner, F. (eds) Areas of Vocational Education Research. New Frontiers of Educational Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54224-4_11

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Technical and vocational education and training (TVET) for young people in low- and middle-income countries: a systematic review and meta-analysis

• Janice S Tripney 1 &
• Jorge G Hombrados 2  

Empirical Research in Vocational Education and Training volume  5 , Article number:  3 ( 2013 ) Cite this article

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The world is facing a worsening youth employment crisis. In response, technical and vocational education and training (TVET) is back on the development agenda after years of neglect. This systematic review examined the evidence from studies evaluating the impacts of TVET interventions for young people in low- and middle-income countries (LMICs). The 26 included studies evaluated 20 different interventions, predominantly from Latin America. Meta-analyses of the effectiveness of TVET on five outcome measure categories were conducted. The overall mean effects on overall paid employment, formal employment, and monthly earnings were small, positive, and significant; however, significant heterogeneity was observed. Moderator analysis was performed in an attempt to explain between-study differences in effects. The overall paucity of research in this area, together with specific gaps and methodological limitations, affirm the need for strengthening the evidence base. Implications for policy, practice and research are discussed.

Introduction

The global youth unemployment rate rose sharply between 2008 and 2009, from 11.8 to 12.7 per cent, reversing the pre-crisis trend of declining youth unemployment rates since 2002 (International Labour Organization 2011 ). By 2011, 74.8 million young people were unemployed. In some regions, young people are nearly three times as likely as adults to be unemployed; they are also more likely than older workers to be underemployed or work in the informal labour market, in poor quality jobs that require low levels of skills and offer limited socio-economic security, training opportunities, and working conditions (International Labour Organization 2012 ). Significant regional variation in youth unemployment exists; and, in many countries, young women are much more likely to be un/underemployed than young men (United Nations 2012 ). Over 40 per cent of all young people live on less than two US dollars a day, with youth in developing countries disproportionately among the working poor (International Labour Organization 2012 ). This enormous unlocked potential represents a substantial loss of opportunity for both individuals and society.

One billion young people, the majority from LMICs, are predicted to reach employment age within the next decade (International Labour Organization 2012 ), compounding what are already severely limited opportunities for integrating youth into the labour market. With increasing policy importance now placed on higher-order skills and their central role in the global knowledge-based economy, comes a renewed focus on the potential of TVET to equip youths with the abilities to seize available work opportunities, and, in turn, impact upon global poverty and social stability (African Union 2007 ; King and Palmer 2010 ; Péano et al. 2008 ; United Nations Educational, Scientific and Cultural Organization Scientific and Cultural Organization 2010 , 2012 ). Neglected by the World Bank and other donor agencies during much of the 1980s and 1990s, there has been growing investment in TVET since 2000. Deciding which programmes to implement requires an understanding not only of which models are effective, but for whom they are effective. Collecting evidence from studies that have analysed these issues is crucial for purposes of policy-making.

Technical and vocational education and training (TVET)

The term TVET, as used in this systematic review, follows the definition used by UNESCO as “those aspects of the educational process involving, in addition to general education, the study of technologies and related sciences and the acquisition of practical skills, attitudes, understanding and knowledge relating to occupation in various sectors of economic life”. It incorporates: technical education, vocational education, vocational training, on-the-job training, and apprenticeship training (or any combination thereof).

Prior reviews of TVET

Although a number of prior reviews have summarised the existing research in this area (see, for example, Angel-Urdinola et al. 2010 ; Betcherman et al. 2004 ; Betcherman et al. 2007 ; Card et al. 2010 ; Fares and Puerto 2009 ; Greenberg et al. 2003 ; Katz 2008 ; Kluve 2006 ; Kluve and Schmidt 2002 ; Puerto 2007 ; Stavreska 2006 ; Van Adams 2007 ), there are a number of limitations to these reviews. Some contain evidence from high-income countries only; others, whilst broader in geographical scope, do not focus specifically on developing countries, TVET, or young people. Very few existing reviews are based on a comprehensive, systematic search for literature, and almost all take either a traditional narrative approach to synthesis or use vote-counting methods where conclusions regarding effective interventions are made based on the number of studies found to demonstrate significant positive results. None of the previous reviews that specifically focus on TVET for youth in LMICs have computed effect sizes for the included studies or examined variability in effects by study, participant or intervention characteristics. These are problems that this review aims to remedy, thereby adding value to the existing body of research on this topic.

Purpose of the present study

This review sought to improve upon prior work by systematically examining the evidence base to provide a clearer picture of the types of TVET interventions being used to improve employment prospects for youth in LMICs, to identify the overall effectiveness of TVET, to examine whether study, participant and intervention characteristics help to explain variability in intervention effects, and to identify areas in which more research needs to be conducted. An extended version of this article is available as a technical report published in the Campbell Collaboration library (Tripney et al. 2013 ).

Systematic review methodology was utilised for all aspects of the search, selection and coding of studies. The review was conducted in accordance with Campbell Collaboration procedures and guidelines on systematic review methods, available at http://campbellcollaboration.org/ . Full details about the methods for this review can be found in the technical report, and review protocol (Tripney et al. 2012 ).

Eligibility criteria

Eligibility was restricted to studies that: were conducted in LMICs; were reported between 2000 and 2011; evaluated the impact of a TVET intervention; investigated outcomes for youth aged 15–24 years; included at least one quantifiable measure of employment or employability (broadly defined); and used an experimental or rigorous quasi-experimental design (such as the use of propensity scores to match on pre-tests and/or relevant demographic characteristics or regression analysis to control for differences between groups). Single group pre-post test studies were not eligible. No language or publication status restrictions were applied.

The conceptual definition of TVET used in this review cuts across education level, type of learning arrangement, mode of delivery, setting, and type of provider/regulator. It includes provision of (i) initial training for young people from the age of 15/16 years after compulsory school, but prior to entering work; (ii) continuing education and training for adults in the labour market leading to personal, flexible and/or vocational competencies; and (iii) training for unemployed persons currently available for and seeking work (including retraining for those made redundant). Single- and multi-service TVET interventions were eligible for inclusion in the review, as were interventions delivered for any length of time or frequency. LMICs were defined according to World Bank classification of economies (in effect 1 July 2011 until 30 June 2012).

Literature search and strategy

The review recognised the importance of using a comprehensive and diverse search strategy to locate all qualifying published and unpublished studies (Papaioannou et al. 2009 ; Schucan Bird and Tripney 2011 ). First, 10 major bibliographic databases were electronically searched (including ASSIA, Econlit, ERIC, IBSS, Medline, PsycINFO, and SSCI). Searches within each database combined controlled vocabulary and natural language terms, with appropriate wildcards, for three concepts: TVET, employment, and study design. Second, specialist databases and library catalogues were searched, including grey literature, regional and topic-specific sources. Third, the websites of government agencies, research centres, foundations, professional associations, and other relevant organisations were searched. Fourth, the reference lists of previous reviews and included studies were checked. Fifth, citation searches of included studies were conducted. Sixth, information about additional relevant studies was requested from authors of included studies and other key experts in the field.

Data extraction and critical appraisal

Data were independently extracted from all included studies by pairs of reviewers using a coding tool specifically designed for this review. Studies were quality assessed using a tool developed specifically for assessing risk of bias in experimental and quasi-experimental designs based on statistical methods (Hombrados and Waddington 2012 ).

Where data allowed, effect sizes were computed for each study and random-effects inverse-variance weighted meta-analytic methods were used to synthesise the evidence. We followed Petrosino et al. ( 2012 ) in the use of standardised mean differences (SMDs) for synthesising both continuous and dichotomous outcomes. The different formulas used in calculating effect sizes are detailed in the full technical report. The included studies were analysed by outcome and Hedges’ g effect sizes were reported. To visibly examine between-study variability in the effect size estimates, forest plots were used to display the estimated effect sizes from each study along with their 95 per cent confidence intervals (CIs). Heterogeneity was explored using both the Q test and the I 2 index (Deeks et al. 2001 ; Higgins and Green 2011 ). Moderator analyses were performed to examine possible reasons for variability in effects. Due to the small number of available studies, an analogue to the ANOVA analysis (univariate) approach was used (Lipsey and Wilson 2001 ). The categorical variables which identified the sub-groups used in this exercise were specified in advance.

Size of literature reviewed

Of the 8072 articles identified using the electronic search, 145 were deemed potentially relevant on the basis of title and abstract and the full papers were retrieved for a full text review. The full papers of a further 76 items identified through the non-database search were also manually examined. This process resulted in a total of 30 different studies, described in 55 separate publications, being identified as meeting the eligibility criteria. Of these, 26 studies were included in the review. a The study search and selection process is detailed in Figure  1 .

Study search and selection process flowchart.

Descriptive analysis

Publication dates of the 26 included studies ranged between 2001 and 2011, and over three-quarters were published as technical reports. Three studies were randomised experiments and 23 studies used a quasi-experimental design (QED). The QED evaluations used different econometric techniques to address selection bias and net out the impacts of other factors, most commonly propensity score matching (Rosenbaum and Rubin 1983 ). The majority of study samples included both male and female participants, and a single study focused exclusively on young women. The risk of bias assessments resulted in five studies graded medium quality (Attanasio et al. 2011 ; Card et al. 2011 ; Chong and Galdo 2006 ; Hicks et al. 2011 ; Espinoza 2010 ), 10 graded low/medium (Acero et al. 2011 ; Aedo and Nuñez 2004 ; Aedo and Pizarro 2004 ; Analítica Consultores 2006 ; Chong et al. 2008 ; Díaz and Jaramillo 2006 ; Elías et al. 2004 ; Ibarraran and Rosas-Shady 2006 ; Jaramillo et al. 2007 ; Ñopo et al. 2007 ) and 11 graded low (Alzuá and Brassiolo 2006 ; Alzúa et al. 2007 ; Benus et al. 2001 ; Bidani et al. 2002 ; Chun and Watanabe 2011 ; Delajara et al. 2006 ; Dmitrijeva 2009 ; López-Acevedo 2003 ; Medina and Nuñez 2005 ; Mensch et al. 2004 ; van Gameren 2010 ). d

Twenty different TVET programme interventions were evaluated. Settings include 10 upper-middle income countries (Argentina; Bosnia and Herzegovina; Brazil; Chile; China; Colombia; Dominican Republic; Latvia; Mexico; Panama and Peru); two lower-middle income countries (India and Bhutan); and one low-income country (Kenya). e Twelve programmes were specifically targeted at youth. The majority were targeted at disadvantaged populations, based on criteria such as household income, education level, and employment experience. A few were occupation- rather than age-focused (targeting, for example, demobilised soldiers or unemployed former employees of state-owned enterprises). A small number of interventions were directed exclusively at either urban or rural residents.

The 20 TVET interventions consisted of one or more of the different models or approaches to vocational skills development (see Table  1 ). The different types of TVET are not evenly represented, however; for instance, no apprenticeship training programmes were evaluated and a very small number of studies focused on technical education or vocational education. Many of the interventions in the included studies were complex, multi-component programmes. Alongside TVET-related training, some programmes incorporated labour mediation services (such as job readiness skills), some included training specifically for self-employment and some included other types of training (such as basic skills). However, the largest category of multi-component programme involved components consisting of different types of TVET. There were nine two-phase TVET interventions, most of which combined theoretical and practical training, usually in the format of classroom-based vocational training followed by a period of on-the-job training to provide beneficiaries with work experience. These interventions typically focused on short-term semi-skilled training in specific occupations demanded in the private sector.

Primary study authors often did not provide all the necessary data for computing effect sizes. It was possible to calculate at least one effect size for 10 of the studies included in the review (Acero et al. 2011 ; Aedo and Nuñez 2004 ; Aedo and Pizarro 2004 ; Attanasio et al. 2011 ; Card et al. 2011 ; Elías et al. 2004 ; Espinoza 2010 ; Hicks et al. 2011 ; Ibarraran and Rosas-Shady 2006 ; Medina and Nuñez 2005 ). The findings from these studies were statistically combined using meta-analytic techniques.

Overall mean effects of interventions

The overall mean effect sizes for five outcomes are presented in Table  2 . For three outcomes, an overall positive and statistically significant effect on TVET on youth was found: overall paid employment ( g  = 0.134; 95% CI [0.024, 0.243]); formal employment ( g  = 0.199; 95% CI [0.055, 0.344]); and earnings ( g  = 0.127; 95% CI [0.043, 0.21]). For working hours worked, the overall effect was also positive, but non-significant ( g  = 0.043; 95% CI [-0.017, 0.104]). In contrast, the overall effect on self-employment earnings was negative, and non-significant ( g  = -0.025; 95% CI [-0.11, 0.061]). b

Analysis of homogeneity

For three outcomes (overall paid employment, formal employment, and earnings) the grand mean provides some evidence that the TVET interventions were, on average, effective; although, effects are generally small and difficult to detect. Furthermore, in each case the result of the statistical test for homogeneity was statistically significant (Q = 23.8, p < 0.05; Q = 11.1, p < 0.05; Q = 25.5, p < 0.05), indicating that studies disagreed on the magnitude of effect. Our next step was to explore the reasons for this variability between studies.

Analysis of moderator effects

Moderator analyses were performed to examine independent variables that may be contributing to the heterogeneity. The results are presented in Table  3 .

Relationship of study characteristics to effect size

Two factors potentially associated with treatment effect were examined: study quality and length of follow up (i.e., timing of outcome measurement). As there were no high quality studies included in the review, studies rated medium quality were compared with those rated low or low-medium. c Short-term treatment effects (measured at approximately six months) were compared with medium-term effects (measured at approximately 12 months). The low quality studies were found to have consistently larger mean effects than the medium quality studies; and, for two outcomes, differences in mean effects observed between studies according to study quality were statistically significant: paid employment (Q b  = 6.49, p = 0.0108); formal employment (Q b  = 10.6, p = 0.0012). Although studies that measured short-term treatment effects consistently had slightly larger mean effects than studies in the medium-term follow up group, no statistically significant relationship between length of follow up and treatment effect was observed for any of the outcomes for which we had data.

Relationship of participant characteristics (gender) to effect size

For one of the five outcome categories (weekly hours), statistically significant differences in mean treatment effects were observed between studies according to gender (Qb = 10.1; p = 0.00151). Treatment effects for female youth were positive ( g  = 0.16; 95% CI [0.04, 0.28]), while those for male youth were negative ( g  = -0.09; 95% CI [-0.2, 0.01]). However, male youth participation in TVET demonstrated effects that were not statistically different from zero, lessening our confidence in this result.

Relationship of intervention characteristics (type) to effect size

Slight differences in mean effects between two-phase models of TVET and other models were observed, with two-phases usually, but not always, producing the larger effects. However, these differences were not statistically significant.

Conclusions

This review improves upon prior work by statistically synthesising TVET intervention research, strengthening the evidence base on which current policies and practices can draw. However, the nature of the available evidence, and limitations in the review itself, are such that drawing strong inferences from the results of the analyses is not recommended and considerable caution should be used when interpreting and applying the findings of the review.

Despite renewed investment in TVET and increased pressure for evidence-based decision-making, there remains an overall scarcity of research in this area and specific knowledge gaps. The interventions included in this systematic review are not representative of all TVET programmes in existence. Studies were undertaken in a relatively small number of counties, predominantly in Latin America and the Caribbean. Some types of TVET are poorly represented in the review; for example, no studies examined the effectiveness of apprenticeship training. The quality of the existing evidence base is also a concern. Only three RCTs were identified, and methodological and reporting shortcomings of the included studies were such that none provided high quality evidence to help answer the review questions.

Overall, interventions included in the meta-analyses were found to demonstrate a significant, though small, positive effect on a limited range of labour market outcomes: overall paid employment ( g  = 0.134; 95% CI [0.024, 0.243]); formal employment ( g  = 0.199; 95% CI [0.055, 0.344]); and monthly earnings ( g  = 0.127; 95% CI [0.043, 0.21]). However, the observed heterogeneity was also significant, indicating that different studies point to somewhat different conclusions. Attempts to explain the heterogeneity in overall mean effects suggest that methods matter. The low quality studies have consistently larger mean effects than the medium quality studies. For two outcomes, evidence of a statistically significant relationship between study quality and effect size magnitude suggest that the overall mean effect may be inflated and that our conclusions about treatment effect should be based only on those studies rated medium quality: paid employment ( g  = 0.06; 95% CI [-0.01, 0.12]); formal employment ( g  = 0.12; 95% CI [0.05, 0.19]). Treatment effects for both self-employment earnings and weekly hours worked were non-significant. On the whole, the other independent variables that were tested were unable to explain variability in effects. Length of follow up did not demonstrate a significant relationship with treatment effect. Two-phase TVET interventions (classroom-based vocational training followed by on-the-job training) produced statistically similar effects to other TVET models. Gender may have some influence, as an increase in the number of weekly hours worked by youth following participating in TVET appears to be driven by the effect on young women. It is important to note, however, that due to an insufficient number of studies reporting relevant data, only some of the moderator analyses that had been planned a priori could be performed. Furthermore, due to the very small number of studies for most of the variables tested, and thus low statistical power, we may not have been able to detect moderator effects that may indeed be present. In addition, there may be other moderating variables that either were not tested in this study or measured in the primary reports, such as implementation fidelity, whether the intervention was theoretically informed, and so on, which could account for the differences in effects between studies.

While a key strength of this study is its application of systematic review principles to improve upon prior work. There are several important limitations to this review that should be acknowledged. First, not all eligible studies are included in the meta-analysis. Second, the methods for calculating comparable effect sizes from studies using complex econometrics methods, as used in this review, are under-developed and require further research (for a complete discussion, see Becker and Wu 2007 ; Duvendack et al. 2012 ; Lipsey and Wilson 2001 ). Third, no high quality studies were identified and some of the methodological concerns associated with those that were included may mean that the studies have yielded biased estimates of treatment effect. All conclusions from the current review are therefore sensitive to the possibility that the results from the meta-analyses may be over- or under-estimating the effects of TVET on the outcome variables of interest.

Limitations notwithstanding, this systematic review provides some evidence to support the claim that participation in TVET improves the labour market situation of youth in LMICs, on average, when compared to youth who do not participate, with the strength of the evidence strongest for formal employment and monthly earnings. Thus, it is reasonable to conclude that it is worthwhile to continue investment in TVET provision. Although, statistically, the effects of TVET on youth may be small, even a slight increase in the employment rate, for example, could potentially translate into fairly large numbers of young people entering the labour market, especially where programmes are delivered nationally. Through its use of statistical meta-analysis, this review has improved upon previous work; nevertheless, there are two main related areas of likely disappointment for policymakers and programme implementers. First, not only were the observed effects generally small and difficult to detect, the review does not provide conclusive evidence of the effectiveness of TVET on youth employment outcomes. This suggests an urgent need to improve the rigour of the evidence base, and will require policymakers, practitioners and other relevant stakeholders engaging critically with the barriers to effective research production and dissemination in this area. The commissioning of RCTs and robust QEDs is crucial for generating evidence capable of supporting causal claims. To improve potential generalisability, it is also important that appropriate support, financial and otherwise, is given to rigorous outcome research on all types of TVET that have been implemented internationally for young people, and in a broader range of geographical settings than covered by the current evidence base. A cost-benefit analysis was outside the scope of the current review; however, attention should be given to the collection of data regarding the costs of TVET interventions, and examinations of cost-effectiveness should be a priority in future impact evaluations and systematic reviews. Second, the available evidence does not allow recommendations to be made for, or against, investments in a particular model of TVET. Although claims have been made in the literature and media for the success or effectiveness of a particular format of TVET widely implemented in Latin America and the Caribbean, the evidence from this systematic review suggests that such claims are premature. In the absence of evidence in support of any one model or approach to vocational skills development, it seems reasonable in the meantime for policymakers and other stakeholders to select the least costly, most culturally acceptable, and/or most easily implemented programmes, while at the same time becoming good consumers of evidence and working towards the strengthening of the evidence base on TVET. This review has demonstrated that not only is there is a clear need for additional primary research in this area, but that more of the same will not suffice. The methodological shortcomings of the current evidence base, and specific knowledge gaps, suggest a number of future research priorities. These are summarised in Table  4 .

a Four eligible Spanish-language studies could not be included in the review. Of these, one study was judged as meeting the review selection criteria on the basis of detailed descriptions in previous literature reviews, but efforts to obtain a copy of the publication were unsuccessful. The three remaining eligible non-English language studies were not included in the review due to exhaustion of financial resources. All four studies evaluated ProJoven, a Peruvian programme that was evaluated by a number of studies that were included in the review.

b A preliminary assessment of the ‘direction of effects’ of the 16 studies not included in the meta-analyses suggests that these studies generally support the meta-analytic findings.

c For this exercise, all of these studies were grouped together into a single category labelled low.

d One study (Espinoza 2010 ) was graded medium quality overall; however, individual assessments for the different outcome categories varied. For employment outcomes, this study was graded as proving medium quality evidence; for monthly earnings, the grading was low/medium.

e World Bank country classifications have been revised twice since the conduct of this review, and Chile and Latvia are currently ranked as high income economies.

Authors’ information

JT is a lecturer in Social Policy, based at the EPPI-Centre (Evidence for Policy and Practice Co-ordinating Centre), Social science Research Unit, Institute of Education, University of London. JH is a research analyst at J-PAL (Abdul Latif Jameel Poverty Action Lab), based at its regional office in Latin America at the Pontificia Universidad Católica de Chile.

Abbreviations

Low- and middle-income country

Quasi-experimental design

Randomised controlled trial

Standardised mean difference

Technical and vocational education and training.

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Acknowledgements

This manuscript arose from a systematic review commissioned in 2011 by the Australian Agency for International Development (AusAID), in response to the International Initiative for Impact Evaluation (3ie) systematic reviews call 3. JT was the grant holder and principal investigator. JH participated in the full review on an un-funded basis. JT and JH wish to acknowledge the contributions of those who helped in the creation of the full review: Mark Newman was involved in data collection and advised on the statistical analysis; Kimberley Hovish, Chris Brown, Katarzyna Steinka-Fry and Eric Wilkey participated in the acquisition of data. Special thanks are due to Sandra Jo Wilson, editor of the Education Coordination Group (ECG), for her guidance throughout the initial project. The funding agency informed the scope and development of the full review. The preparation of this manuscript was unfunded, and the funding agency had no role in the writing of this manuscript, or the decision to submit the manuscript for publication. The opinions expressed in this manuscript are those of the authors and do not necessarily reflect those of the funding agency. The full version of the review is published in the Campbell library ( http://www.campbellcollaboration.org/lib/project/227/ ), and the 3ie database of systematic reviews ( http://www.3ieimpact.org/en/evidence/systematic-reviews/details/152/ ).

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Tripney, J.S., Hombrados, J.G. Technical and vocational education and training (TVET) for young people in low- and middle-income countries: a systematic review and meta-analysis. Empirical Res Voc Ed Train 5 , 3 (2013). https://doi.org/10.1186/1877-6345-5-3

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• Published: 15 December 2023

Knowledge mapping of vocational education and training research (2004–2020): a visual analysis based on CiteSpace

• Yumi Tian 1 ,
• Jiayun Liu 1 ,
• Xin Xu 1 &
• Xueshi Wu 1  

Scientific Reports volume  13 , Article number:  22348 ( 2023 ) Cite this article

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The study aims to analyze the leading researchers of vocational education and training from dimensions of individuals, institutions and countries. This article utilises the scientific information measurement software—CiteSpace—to conduct a scientometric analysis of 2,024 articles on vocational education and training from the Web of Science (W.o.S.). According to the research results, some useful conclusions can be drawn as follows: (1) vocational education and training research has become interdisciplinary and subject involved are “psychology”, “sociology”, “economics” and “pedagogy”; (2) the United States, the Netherlands and Australia make the majority of contributions and there are numerous collaborations among countries; (3) Univ Amsterdam, Univ Utrecht and Univ Melbourne were the main research institutions; (4) J Vocat Educ Train, Rev Educ Res, Thesis Elev, Econ Educ Rev and J Educ Work were the top 5 highly cited journals; (5) “Engagement”, “Program”, “Self-efficacy”, “High school”, “Predictor” and “Labor market” have become major research hotspots currently.

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Introduction.

Nowadays the importance of vocational education and training has been highlighted by the rapid economic and social development with relatively mature vocational education and training systems established in countries like the United States, Germany, Australia, the United Kingdom and the Netherlands. Subsequently, researchers with academic backgrounds in economics 1 , 2 , 3 , management 4 , 5 , 6 , and information technology 7 , 8 , 9 around the world have paid close attention to the courses 10 , 11 , professions 12 , entrepreneurship 13 , 14 , skills 15 , 16 and evaluations 17 of this field.

Social changes are manifested in greater mobility of workers, shifting labor markets, frequent changes of professions, the disappearance of several professions and the emergence of new ones 18 , 19 . The emergence of artificial intelligence tools, which are transforming the entire landscape of the labor market, is becoming a significant risk and challenge. The need to learn constantly and throughout life and the instability of professional development make vocational education and training the most important and defining sector of education 20 , 21 . Many researchers agree that its role will constantly grow and cover all sectors of professional implementation 22 , 23 . Bibliographic analysis of the field of research in the field of vocational education is rapidly evolving, but there are fewer review works on this sector than it requires 24 .

Vocational education and training are studied from the point of view of assistance from the state administration or municipalities in the retraining of employees 6 , 16 ; many country case studies provide insight into the differences in the educational context of individual countries 2 , 3 , 11 , 14 . Experimental studies on combining vocational education and work or other types of employment make it possible to assess the potential of problems that require solutions 25 , 26 , 27 . The use of technical and digital tools within vocational education is also being devoted to more and more research 9 , 28 , 29 .

The existing research results have laid an important foundation for the reform and development of vocational education and training. However, vocational education and training still have a series of problems that need to be solved, such as the large gap between the skill supply of vocational education and the skill demand of the labor market, and the low enthusiasm of enterprises to participate in vocational education and training 4 , 9 . In addition, although many scholars use a variety of research methods from different dimensions to explore related issues of vocational education and training, few studies have investigated vocational education and training comprehensively and systematically 23 , 30 . There is an urgent need for bibliometric analysis to identify areas of development, areas of greatest interest among researchers, and stratification of research by country, institution, and area. This will allow the efforts of new researchers to be more targeted and their quality improved.

The objectives of this study are as follows:

Analyze the leading researchers of vocational education and training from dimensions of individuals, institutions and countries;

Figure out the distribution of journals related to vocational education and training;

Delve into the main research topics and knowledge structure in this field;

Aggregate the research hotspots and frontiers in this field.

The data used in the study were obtained through advanced retrieval from the Web of Science Core Collection (WOSCC): "Science Citation Index Expanded (SCI-E) (2004–2020)"; Social Science Citation Index (SSCI) (2004–2020)"; "Conference Proceedings Citation Index-Science (CPCI-S) (2004–2020)". These data are intended to provide comprehensive, scientific and systematical research on the existing literature related to vocational education and training. The retrieval strategy was as follows: TS = ("Vocational Education" or "Technical and Vocational Education and Training" or "Technical Education" or "Technical and Further Education" or "Technical and Further Education" or ((VET) or (TVET) and (education))), and with language options of "English" and literature type selection "Article". Here VET is the "Vocational Education and Training" abbreviation and TVET "Technical and Vocational Education and Training" abbreviation.

Indeed, 644 data sources were retrieved from SCI-E, 697 from SSCI, and 693 from CPCI-S. A total of 118 duplicate articles were identified. The cleansing of a sample of data sources was carried out manually by carefully studying the content of abstracts and excerpts from the sources or full versions if they were available. Finally, a total of 2,024 bibliographic citations were obtained (Fig.  1 ).

Literature related to vocational education and training from 2004 to 2020.

Research tools

Information visualization is the process of representing and visualizing abstract data with the help of computer software, which can enhance researchers' perception of abstract information 31 . Based on the existing literature, visualization analysis can adopt the method of dynamic graphic visualization to reveal the trends, hotspots and frontiers of scientific research. Therefore, information visualization can facilitate researchers to understand and predict the frontiers and trends of scientific research opportunely and break new grounds for new ideas amid complex information.

In this study, the analysis software CiteSpace 5.7 was used to conduct research cooperation, cited references and co-occurrence analysis of keywords in literature pertinent to vocational education and training. CiteSpace is an important software in bibliometrics 32 . CiteSpace visualized knowledge maps can be used to identify, display and predict research trends and elucidate knowledge structure and development 33 . Therefore, CiteSpace's visualized knowledge map was adopted in this study to analyze literature in the field of vocational education and training. The analysis elements included Author, Institution, Country, Cited authors, Cited journals, Cited journals, and Cited references.

A network of cited references, co-authors and keywords co-occurrence can represent the scientific knowledge domains 34 . The network provides a systematic and scientific description of the evolving field of scientific knowledge through knowledge mapping, a novel method of literature analysis, enabling researchers to better understand knowledge structures, research collaborations and the hotspots and trends of research 35 .

Research process

In this study, a visual analysis of the bibliography of vocational education and training was conducted through CiteSpace. The research process was as follows: Firstly, the basic knowledge cluster of vocational education and training was constructed according to the reference literature of the field. This cluster is necessary for the next steps to identify the main clusters in the knowledge graph and highlight the most influential literature in this field of knowledge. Also, the basic knowledge cluster will help to study the evolution of each cluster, and future trends and identify key literature from a timeline perspective.

Secondly, the hotspots and frontiers of vocational education and training can be identified based on the frequency of the keywords in the related literature. Meanwhile, keyword bursts can also reveal the evolution of vocational education and training and determine the latest research trends. Burst refers to the significant change in the value of a variable over a relatively short period, which is adopted by Citespace to identify research frontiers.

Finally, the researchers, research institutions and countries were visualized to identify the major contributors to the evolution of knowledge in vocational education and training.

Research limitations

The results obtained may be limited to searching only the Web of Science Core Collection (WOSCC) database, without considering Scopus or other relevant data sources. Also, the sample may not contain sources that directly relate to the topic under study but did not use the corresponding keywords in the article description or other identifiers, and this happens.

Knowledge clustering of vocational education and training research

The emergence and development of any new knowledge are based on existing research and findings, and so are vocational education and training. In general, the frontiers of research in a particular discipline can be represented by journal papers to a certain degree, and the cited references form the knowledge base for the journal paper. The important references can be clustered and the co-cited clustering can be determined with the help of specialized computer software, an important step in figuring out the knowledge base for vocational education and training.

The distribution of selected bibliographic citations by year is presented in Fig.  1 and Table 1 . First, one should evaluate the relatively uniform growth in the number of works devoted to the topic under study throughout the entire period under study. The only exception is the sharp increase in the number of studies in 2012.

In Eq.  1 \({c}_{ij}\) represents the number of co-occurrences of i and j, \({s}_{i}\) is the frequency of occurrence of i, \({s}_{j}\) is the frequency of occurrence of j.

After running CiteSpace, the knowledge mapping was obtained.

Cluster names related to the field of vocational education and training were extracted with the application of MI (Mutual Information). The formula is calculated as Eq.  2 , where \({g}_{st}\) is the number of shortest paths from node s to node t; \({n}_{st}^{i}\) is the number of shortest paths through node i among the \({g}_{st}\) shortest paths from node s to node t. 166 clusters were generated based on the co-primer clustering information with 10 main clusters.

Modularity Q is a measure of visual networks ranging from 0 to 1. The formula is calculated as Eq.  3 ; \(P(w|{c}_{i})\) is the co-occurrence probability of w and c, \(P(w)P({c}_{i})\) is the frequency of occurrence of w, \(P({c}_{i})\) is the frequency of occurrence of i-type values). The higher the value, the better the network clustering. In general, Modularity Q ranging from 0.3 to 0.8 indicates that network clustering is acceptable. Weighted Mean Silhouette S is a homogeneous indicator of network clustering ranging from − 1 to 1. The larger the Weighted Mean Silhouette S, the higher the clustering homogeneity. In general, Weighted Mean Silhouette S below 0.5 means that the clustering results are acceptable, and above 0.7 means that the clustering results are more reliable 36 . Figure  2 shows the Modularity Q value of 0.392 and the Weighted Mean Silhouette S value of 0.9641 for the visual network in the field of vocational education and training. Weighted Mean Silhouette S values of all 10 major clusters are above 0.8. The above data demonstrate that knowledge mapping is a high-quality clustering of the knowledge domain of vocational education and training.

Co-cited literature clusters.

Table 2 further provides a more detailed description of each of the knowledge clusters depicted.

It can be concluded from Table 2 that Vocational Interests (#0) ranked first in the knowledge cluster that includes job quality, linkage, occupation, completion, ring-biased Technological change, Labour market entry and dual training system, including 62 literature, most of which were published around 2014. The Weighted Mean Silhouette S value of the cluster is 0.966, indicating the high homogeneity of the 62 literature in the cluster. Among them, General Education, Vocational Education, and Labor-Market Outcomes over the Life-Circle, by Hanushek et al. 37 on Journal of Human Resources , are the articles with the highest citation (60% of the articles in this cluster cited this article); Vocational Education and Employment over the Life Cycle by Forster et al. 38 on Sociological Science , ranks the second (34% of the studies in the cluster cited this article); Educational Systems and the Trade-Off between Labor Market Allocation and Equality of Educational Opportunity by Bol and Van de Werfhorst 18 on Comparative Education Review ranks the third (27% of the studies in the cluster cited this article).

The second clustering is Workplace Simulation (#1) with 55 articles, and the Weighted Mean Silhouette S value of the cluster is 0.879. The most cited article was Students' Learning Processes during School-based Learning and Workplace Learning in Vocational Education: A Review by Schaap et al. 40 on Vocations and Learning . Forty percent of the studies in the cluster cited this article. The third cluster, Task Shifting (#2), consisted of 42 articles. The Weighted Mean Silhouette S value of the cluster is 0.967, a high homogeneity. The detailed information of each major cluster is shown in Table 3 .

In addition, widely recognized studies can be identified based on the number of citations. The top 3 studies with the highest citations (over 20 citations) are as follows: General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle published by Hanushek et al. 37 on Journal of Human Resources (37 citations); Students' Learning Processes during School-based Learning and Workplace Learning in Vocational Education: A Review by Schaap et al. 40 (22 citations) on Vocations and Learning ; Vocational Education and Employment over the Life Cycle by Forster et al. 38 on Sociological Science (20 citations).

Given the lack of expansion of the thematic field of research after 2013, as shown further in tables, it can be assumed that the Vocational Interests knowledge cluster has focused the most interest of researchers and perhaps provided the most topics for further in-depth research. It is the state of the labor market and the relationship with employment that has received the most attention. researchers in connection with vocational education.

Distribution of countries

The number of papers published by different countries and their academic influence can be elaborated on in Table 4 . It can be inferred that the United States was the most productive among the top 10 countries followed by the Netherlands and Australia in the field of vocational education and training with 260 papers published from 2004 to 2020, accounting for about 14% of all literature. However, China ranked sixth with 88 papers, accounting for 4.8% of all papers, far lower than that of the United States. In terms of betweenness centrality value, the USA (0.67), England (0.36) and Germany (0.27) ranked in the top three, indicating a significant academic influence on the field of vocational education and training. Betweenness centrality indicates the strength of a node's influence on the flow of information in the graph. This is a measure of the influence of a separate node in a whole network 33 . The United States ranked first regarding the academic influence in the field of vocational education and training research while China is still in a relatively disadvantaged position. A more complete visual representation of the distribution of academic influence by country is presented in Fig.  3 .

Network of countries distribution for VET.

Burstiness (Table 4 the last section) is an increase and decrease in activity or frequency of publications that disrupts the continuity or pattern of distribution. In this case, the higher this indicator, the more uneven the participation of publications from this university in the research field being studied. The Burstiness results of countries more active in the field of vocational education and training (Table 4 , Burstiness section) showed that: Turkey ranked first with a value of 12.38, followed by England which has been more active in this field. However, although Sweden and New Zealand are not very active in this field, academic attention has been drawn to them.

Distribution of research institutions

In terms of the production of research institutions in the field of vocational education and training, Univ Amsterdam ranked first with 30 articles, followed by Univ Utrecht (28 articles) and Univ Melbourne (22 articles) (Fig.  4 ).

Network of institutions for VET research.

However, the ranking of research institutions based on betweenness centrality demonstrated significant influence from other research centers. The top three universities with betweenness centrality were IZA (0.09), Univ Turku (0.09) and Univ Helsinki (0.07), indicating the importance and influence of these three universities in the field of vocational education and training. Regarding the post surge capacity, Gazi Univ was in the lead with a surge of 5.52, followed by Leiden Univ (5.02) and Univ Utrecht (4.24). See Table 5 for details.

Cited journals

In the citation network of journals, the larger the circle, the higher the citation frequency (Fig.  5 ).

Cited journals network.

Totally 233 pieces of literature on J Vocat Educ Train were cited; 208 on Rev Educ Res ; 181 on Thesis Elev ; and 156 on Econ Educ Rev (Table 6 ).

However, from 2004 to 2020, Thesis Elev ranked first in burst detection with a burst value of 31.74. Other journals with relatively high emergent detection values include SOC SCI Res, Comp Educ, Teach Teach, Econ J, J Labor Econ, etc. These journals mainly come from the fields of psychology, sociology, economics and pedagogy, the source of knowledge in the field of vocational education and training.

Hotspots and trends of the research on vocational education and training

The research hotspot is the focus of researchers' attention shared by a group of interrelated papers in a relatively short period. Keywords are the gist and soul of an academic paper, a highly summarized and refined research problem, and an important index of research hotspots. Therefore, the research hotspots and main characteristics of a certain field can be abstracted from the change in keyword frequency. In this study, "Keyword" was selected from the CiteSpace node types for Keyword co-occurrence network analysis. The larger the node, the more important the node.

In terms of keywords frequency (Table 6 ), related research mainly focused on vocational education and training in vocational education, the transition, inequality, gender, perception, attitude, and the program, work, school, and skill, among which, the keyword "vocational education" ranked first for appearing 399 times, followed by education (234 times) and vocational education and training (181 times). Betweenness centrality higher topics include health, adolescent, perspective, gender, employment, model, etc.

Meanwhile, keyword selection was carried out to clearly show the research hotspots in different years and their interrelation and evolution. Since none of the keywords identified during the study were localized for the period after 2013, we can conclude that the thematic field of research after this time developed almost exclusively intensively, and not extensively, that is, the research hotspots that had already been emphasized earlier were explored (Fig.  6 ).

Timeline of co-citation clusters from 2004 to 2020.

Keywords that appeared more than 25 times were selected and checked for betweenness centrality, as shown in Table 7 .

Compared with previous research 23 , 24 , this research uses CiteSpace V to analyze the research hotspots and research frontiers of vocational education and training from 2004 to 2020, and finds that:

First, the annual volume of research literature is steadily increasing, but the growth rate is relatively low. This is the same as the result of Hui's research 23 . The reasons for this result are as follows: The first is that the academic level and subject status of vocational and technical education are not yet mature, and its knowledge fields and subject boundaries are not clear enough, which causes the subject of vocational and technical education to face multiple identity crises 27 , 28 . The second is that the interdisciplinary nature of vocational education makes its research power scattered in many disciplines such as pedagogy, economics, management, and sociology, while there are fewer academic groups specializing in vocational and technical education 3 , 7 .

Second, from the perspectives of research countries, institutions, authors and journals, the main drivers of research in the field of vocational education and training come from the United States, the Netherlands and Australia, with Univ Amsterdam, Univ Utrecht and Univ Melbourne as the leading institutions. De Bruijn from Utrecht University, Christopher Winch from University of Westminster Univ Westminster, Pietty Runhaar from Deakin University, Martin Mulder from King's College, and Derek G Shendell from Rutgers State University, were the major contributors to vocational education and training. Literature on J VOCAT Educ Train was the most highly cited (233 times), followed by the top five journals including Rev Educ Res (208 times), Thesis Elev (181 times), Econ Educ Rev (156 times) and J Educ Work (150 times). This is different from Yu and Zhou's research results 24 . Through analysis of 719 literature titles, Li proposed that the main research countries for vocational education and training are European countries and the United States 12 . The reasons for the difference between the two may be: The first is the sample size. This study uses 3844 literature titles in the Web of Science database, which has a larger sample size coverage and more effective results; while Li's research has only 719 literature titles and a smaller sample range. The second is the time frame. This research uses 15 years of literature from 2004 to 2020, which represents the latest research characteristics in the field of vocational education and training; while Li uses literature from 2000 to 2009, which can only represent the characteristics of previous research.

Third, in terms of the most popular research topics, growth, vocational education and training, politics, university, secondary education, the environment, China, and other aspects of inequality took the lead from 2004 to 2015, and after 2015 20 , 24 . Other researchers agree with the results obtained in that study showing that the field started to focus on inequality, the teacher, professional development, engagement, program, self-efficacy, high school, the predictor and labor market, among which, the fields of engagement, program, self-efficacy, high school, predictor and labor market are still active and may become future research directions 16 , 24 . This is consistent with Hui's research results 23 . Technological changes and socio-economic development require vocational education and training to gradually shift the focus to students’ cross-industry abilities, and to pay close attention to the dynamic needs of the labor market. In addition, this has a certain relationship with the gradual change of vocational education research from macro to meso and micro.

Although an effective visual analysis of the relevant studies in the field of international vocational education and training from 2004 to 2020 was conducted, the obtained data cannot fully represent the overall picture of the development of international vocational education and training. Limited by research conditions, the related studies of international vocational education and training from the Web of Science were downloaded from 2004 to 2020. Significant potential for future research is to explain the observed spillovers in the influence and contributions of different countries and institutions over significant periods and how they change due to market influences, changes in technology, and other possible factors. Future researchers are encouraged to use a wider range of journals over a longer period.

Conclusions

By drawing the scientific knowledge map of international Vocational Education and Training from 2004 to 2020, this paper intuitively demonstrates the growth law of papers, knowledge sources, author contributions, institutional cooperation and national cooperation in this research field. It also analyzes the research hotspots in the field of vocational education and training, and draws the following conclusions from a comprehensive perspective:

Paper growth law. From 2004 to 2009, the development of Vocational Education and Training research was relatively slow. Since 2010, new Vocational Education and Training research has shown a vigorous development trend. The amount of new media research will reach its peak in 2020. The author predicts that in the future, Vocational Education and Training research will continue to show a trend of vigorous development.

Knowledge source. In the field of Vocational Education and Training, 12 journals have been cited more than 120 times. These journals mainly focus on psychology, sociology, economics and pedagogy. This shows that the knowledge in the field of Vocational Education and Training mainly comes from the above four disciplines.

Author contribution. Hanushek Ea, Forster AG, Bol T, Schaap, Akkerman, McGrath, Brockmann, Kuijpers, Nylund and other highly cited authors have provided high-quality papers and belong to high-impact authors.

Institutional cooperation. The most researched institution in the field of Vocational Education and Training is Univ Amsterdam (30 articles), followed by Univ Utrecht (28) and Univ Melbourne (22 articles), Maastricht Univ (22 articles), and Univ Helsinki (22 articles). On the whole, there is a lack of cooperation and exchanges between institutions, and no large-scale cooperation network has been formed.

Country cooperation. The country with the most research in the field of Vocational Education and Training is the USA (260 articles), followed by NETHERLANDS (251) and AUSTRALIA (217). Although there are many research results in the field of Vocational Education and Training in various countries, the cooperation network between countries needs to be strengthened urgently.

Research hotspots. The relatively high intermediary centrality in the field of Vocational Education and Training is health (0.13), adolescent (0.13), gender (0.1), employment (0.1), and model (0.1). This shows that the above content is a research hotspot in this field.

Data availability

Data will be available from the corresponding author (Xueshi Wu) on request.

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Tian, Y., Liu, J., Xu, X. et al. Knowledge mapping of vocational education and training research (2004–2020): a visual analysis based on CiteSpace. Sci Rep 13 , 22348 (2023). https://doi.org/10.1038/s41598-023-49636-7

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A Study of the Impact of the ICT Strand As Part of Tech-VocStrand of Senior High School Students of Southeast Asian College Inc. In partial fulfillment for the requirements in Practical Research II Researchers

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The Department of Education (DepEd) is now in four (4) years of implementing the K to 12 Curriculum, Basic Education Program (BEP). Since the curriculum was changed, different strategies & methodologies was also changed. Varieties of trainings, seminars, and workshops were provided to all educators from the top up to the last level, one among which innovations, the additional two (2) years in high school level, and kindergarten as prerequisite to enroll in grade-I. K to 12 curriculum aimed to equip students with necessary trainings, hands-on to develop and improved individual’s skills for them to become globally competitive citizen. Such trainings will be of help for them in seeking better job in our country and abroad. Specifically those students who will just graduate from high school and could not afford to enroll in college due to poverty. Now, in K to 12, Tech-Voc courses are offered in accordance with the Training Regulations (TR ) granted by TESDA, as primary partner of DepEd. These courses will be undertaken by the T.L.E subject. In grade 7 and 8, exploratory mini courses will be introduced once every grading period. No duplication of mini course will be given to the students. This is in preparation for them to Junior and Senior high school. In grade 9, students have the right to choose the course depending on their skills. Teachers who will assist and guide our students in grade 9 up to 12 must also be a National Certificate Holder (NC). as one of the major requirements of the curriculum. In line with the new changes aforementioned, Technology and Livelihood Education (T.L.E) plays important part on this new curriculum. The subject comprises of four areas; Home Economics, Agriculture, Industrial Arts, and ICT. Graduates in Senior High School under Tech-Voc courses would be ready to be employed through actual application of their trainings. The T.L.E will serve as their stepping stone in seeking better life. In effect, T.L.E subject serves as the Key to K to 12 Curriculum. Although the aim of K to 12 has offered long-term benefits among the students, yet, there are limitations that have been foreseen among schools, such as facilities, resources, and others in order to ideally implement the four areas entrusted in T.L.E classes, and Baybay National High School at Baybay City Leyte is not immune to these limitations. Thus, it is important that the school must conduct survey on the preference of students on the four areas to be offered in T.L.E. classes and perform needs analysis so as that the school itself would be able to focus their resources based on priorities and availability. By doing so, the school would eventually structure a program to address the preferences of the students as well as gradually introduce to the students the in-demand skills that they should have so as to be offered employment, most especially those who could not proceed right away into college education.

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High school student helps transform 'crazy idea' into innovative research tool

Like many good ideas in science, it started with a walk in the woods.

During a stroll through the Berlin Botanic Garden in 2019, HHMI Janelia Research Campus Group Leader Jan Funke and some of his scientific colleagues started chatting about a familiar topic: how to get more information out of insect connectomes.

These wiring diagrams give researchers unprecedented information about brain cells and how they connect to each other, but they don't tell scientists how the signal from one neuron affects the other neurons in its network.

The group wondered if they might be able to use information from previous experiments identifying the neurotransmitters released from some neurons to predict the neurotransmitters released from others in the connectome. Neurons use neurotransmitters to communicate with each other, with different chemicals responsible for different signals.

The human eye can't tell the difference between the synapses on neurons where different neurotransmitters are released, but perhaps a computer model could. Funke and his colleagues were skeptical, but they thought it might be worth giving it a try.

"This is basically where we left it: we have the data, I guess we could try," Funke says. "We were not particularly optimistic."

Back at Janelia, Funke decided to give the project to Michelle Du, a high school student who was starting a summer internship in his lab. The project would allow Du to learn how to train a neural network to recognize images -- a useful skill for a budding computer scientist even if the project did not yield results.

A few days into her internship, Du showed up in Funke's office having trained the model on published data and evaluated its performance on test data. Though Funke had little hope it would work, the model was more than 90 percent accurate in predicting some neurotransmitters.

"I couldn't believe it," Funke says. "The numbers were way too good."

After checking the data and the model, Funke, Du, and their colleagues were convinced that the numbers weren't a mistake: The model could predict neurotransmitters. But the team was still cautious, and they didn't have a good grasp on how the network was making the predictions.

"I should have been very happy, but instead I was worried because we didn't understand what was going on," Funke says.

After ruling out possible confounders that could be skewing their results, the team developed a way to understand what the network was seeing that allowed it to make predictions.

First, they used their network to predict a neurotransmitter from a known image, which it did successfully. Then, they asked a separate network to take that known image and change it slightly to create an image corresponding to the release of a different neurotransmitter -- essentially identifying the minimum traits that need to be changed for the model to predict one neurotransmitter over 4another. Lastly, the team developed a separate method to identify these distinct traits.

From this information, the team understood the different features their original network used to make predictions. This gave them confidence to release their method to the wider neuroscience community in 2020.

"What most of the neuroscience community has seen from this work is the predictions," Funke says. "They were happy to use it, but for us it was very important to make sure it was actually working."

Five years later, Du is now an undergraduate at Duke University, and the method she helped develop has been used to predict neurotransmitters in connectomes of the fruit fly hemibrain, ventral nerve cord, and optic lobe created by Janelia researchers and collaborators, as well as the adult fly brain connectome created by FlyWire.

The information helps scientists understand how neurons in a circuit affect each other so they can then form hypotheses about the function of brain circuits that can be tested in the lab.

"It really all started with a bit of a crazy idea, something that no one was really too optimistic about. And what do you do with a crazy idea? You give it to a high school student as a learning experience," Funke says. "We were very fortunate that Michelle was extremely talented."

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• Nils Eckstein, Alexander Shakeel Bates, Andrew Champion, Michelle Du, Yijie Yin, Philipp Schlegel, Alicia Kun-Yang Lu, Thomson Rymer, Samantha Finley-May, Tyler Paterson, Ruchi Parekh, Sven Dorkenwald, Arie Matsliah, Szi-Chieh Yu, Claire McKellar, Amy Sterling, Katharina Eichler, Marta Costa, Sebastian Seung, Mala Murthy, Volker Hartenstein, Gregory S.X.E. Jefferis, Jan Funke. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster . Cell , 2024; 187 (10): 2574 DOI: 10.1016/j.cell.2024.03.016

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Student-developed ai chatbot opens yale philosopher’s works to all.

Nicolas Gertler and Luciano Floridi (Portraits by Mara Lavitt)

The public is often closed off from scholarly perspectives on the potential benefits of generative artificial intelligence (AI). Studies often reside behind pricey paywalls. And even if they are accessible, they are frequently written in esoteric language that non-academics struggle to parse.

Nicolas Gertler, a first-year student in Yale College, saw a potential solution to these obstacles, through generative AI’s own capabilities.

Gertler, a research assistant at Yale’s Digital Ethics Center (DEC) , has spearheaded an experiment using generative AI to make bodies of knowledge broadly accessible. With Rithvik “Ricky” Sabnekar, a high school junior and skilled developer from Texas, he created the Luciano Floridi Bot , also known as LuFlot, a free AI-powered online educational tool designed to foster engagement with the works of Yale philosopher and DEC Director Luciano Floridi, a pioneer in the philosophy of information and one of the most-cited living philosophers.

The developers believe it’s the first time a chatbot has been trained on an academic’s corpus of literature and released to the public for free.

“ The idea was to democratize access to Professor Floridi’s work,” Gertler said, who undertook the project after discussing its possibilities with Floridi. “The issues he has written about touch everyone’s lives, and more and more people are becoming aware of AI. LuFlot provides an AI-driven platform for much broader engagement with the ethical questions surrounding this transformative technology.”

Meant to facilitate teaching and learning, the chatbot is trained on all the books that Floridi has published over his more than 30-year academic career. Within seconds of receiving a query, it provides users detailed and easily digestible answers drawn from this vast work.

It’s able to synthesize information from multiple sources, finding links between works that even Floridi might not have considered.

The tool features in-text citations, allowing users to trace the origins of the information provided directly to the original texts. If asked a question outside its knowledge base, the bot will politely respond that the query falls outside the scope of Floridi’s expertise. But relevant questions receive prompt and thorough answers. For example, a question asking how to use AI ethically quickly generated a clear, eight-point response with sources cited.

The interface also allows users to ask follow-up questions.

“ Anyone, regardless of their knowledge of AI, can visit the website, ask a question, and have a conversation with the founder of the philosophy of information,” Gertler said. “I think that’s remarkable.”

The tool warns users to evaluate its answers critically, as it may generate incorrect or biased information.

Floridi, professor in the practice in the Cognitive Science Program in Yale’s Faculty of Arts and Sciences, is impressed by the chatbot and its young developers.

“ The center is focused on the impact of digital technology and the ethics of AI, so it makes a lot of sense to have a bot available to answer people’s questions,” Floridi said. “The bot is an amazing tool. Nicolas and Ricky deserve all the credit. I’m just the supporting band.”

He has also become a user. For background on a paper he is writing, Floridi asked the chatbot about asymmetry between good and evil.

“ It gave me an amazing answer, accurately referencing concepts and ideas I had completely forgotten that I’d written about,” he said. “It can instantly draw interesting connections between something I published last year and something I published in 1991, which is incredible.”

Dynamic knowledge

Gertler, who is from Los Angeles, became interested in AI’s potential to benefit society about five years ago after watching YouTube videos on the topic.

“ I love thinking about the societal and ethical implications of new technologies and how they can be leveraged to help people, especially those in underserved and marginalized communities,” he said.

He is involved with two youth-led organizations devoted to harnessing new technology for the greater good: He serves as AI and education advisor of Encode Justice, an organization of more than 1,000 high school and college students across the world that advocates for steering AI in directions that benefit society. He also is vice president of Fidutam, a civil-society group that mobilizes its more than 1,600 members to advocate for and build responsible technology.

At the DEC, Gertler explores how to use generative AI to create educational frameworks that can provide enriched learning experiences. He also serves as the first-ever “AI student ambassador” at Yale’s Poorvu Center for Teaching and Learning, engaging with faculty on integrating AI into instruction and developing resources for students and faculty on the ethical use of AI in pedagogy.

The LuFlot project came together fast. Earlier this year, Gertler showed Floridi a chatbot he had created in his cognitive science class, intended to help students learn the course material. After a few conversations, the idea for LuFlot was born.

Gertler and Floridi decided to build from scratch a custom chatbot with its own user interface, rather than rely on existing tools like ChatGPT. Gertler brought on board Sabnekar, who attends Plano East Senior High School in Plano, Texas, to help develop the bot. The two met through Fidutam, where Sabnekar leads the technical development of the organization’s projects. 

“ Ricky is technically brilliant,” Gertler said.

The pair founded Mylon Education, a startup company seeking to transform the educational landscape by reconstructing the systems through which individuals generate and develop their ideas. LuFlot is the startup’s first project. 

“ Our goal is finding ways to harmonize human agency and creativity with AI-enabled structural support,” Gertler said. “That involves incorporating AI tools into the writing process without inhibiting people’s agency. It’s not about using the chatbot to write essays. It’s about using this technology to deepen your knowledge and sharpen your creativity and critical thinking skills.”

Generative AI and other innovations are changing how people learn, Floridi said. So there needs to be new approaches to teaching.

LuFlot demonstrates that it is cheap, feasible, and efficient to train an AI chatbot on a scholar’s corpus and have it produce high-quality answers to users’ prompts, he said. Chatbots could be trained on other scholars’ work or on an instructors’ entire course content, which could help students learn and retain information. 

“ It’s much more useful than just making my lecture notes available online,” he said.

Floridi says he is reminded of Plato’s criticism of the invention of writing — that it is not a dynamic means of sharing knowledge because reading it always conveys the same answer.

“ The bot is dynamic,” he said. “It will not give you the same answer if you ask the same question even slightly differently. And you can ask it to be more specific. And it will grow as you train it with more information.”

Visit this link to converse with LuFlot about the ethics of digital technologies.

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From steel engineering to ovarian tumor research

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Ashutosh Kumar is a classically trained materials engineer. Having grown up with a passion for making things, he has explored steel design and studied stress fractures in alloys.

Throughout Kumar’s education, however, he was also drawn to biology and medicine. When he was accepted into an undergraduate metallurgical engineering and materials science program at Indian Institute of Technology (IIT) Bombay, the native of Jamshedpur was very excited — and “a little dissatisfied, since I couldn’t do biology anymore.”

Now a PhD candidate and a MathWorks Fellow in MIT’s Department of Materials Science and Engineering, Kumar can merge his wide-ranging interests. He studies the effect of certain bacteria that have been observed encouraging the spread of ovarian cancer and possibly reducing the effectiveness of chemotherapy and immunotherapy.

“Some microbes have an affinity toward infecting ovarian cancer cells, which can lead to changes in the cellular structure and reprogramming cells to survive in stressful conditions,” Kumar says. “This means that cells can migrate to different sites and may have a mechanism to develop chemoresistance. This opens an avenue to develop therapies to see if we can start to undo some of these changes.”

Kumar’s research combines microbiology, bioengineering, artificial intelligence, big data, and materials science. Using microbiome sequencing and AI, he aims to define microbiome changes that may correlate with poor patient outcomes. Ultimately, his goal is to engineer bacteriophage viruses to reprogram bacteria to work therapeutically.

Kumar started inching toward work in the health sciences just months into earning his bachelor's degree at IIT Bombay.

“I realized engineering is so flexible that its applications extend to any field,” he says, adding that he started working with biomaterials “to respect both my degree program and my interests."

“I loved it so much that I decided to go to graduate school,” he adds.

Starting his PhD program at MIT, he says, “was a fantastic opportunity to switch gears and work on more interdisciplinary or ‘MIT-type’ work.”

Kumar says he and Angela Belcher, the James Mason Crafts Professor of biological engineering and materials science, began discussing the impact of the microbiome on ovarian cancer when he first arrived at MIT.

“I shared my enthusiasm about human health and biology, and we started brainstorming,” he says. “We realized that there’s an unmet need to understand a lot of gynecological cancers. Ovarian cancer is an aggressive cancer, which is usually diagnosed when it’s too late and has already spread.”

In 2022, Kumar was awarded a MathWorks Fellowship. The fellowships are awarded to School of Engineering graduate students, preferably those who use MATLAB or Simulink — which were developed by the mathematical computer software company MathWorks — in their research. The philanthropic support fueled Kumar’s full transition into health science research.

“The work we are doing now was initially not funded by traditional sources, and the MathWorks Fellowship gave us the flexibility to pursue this field,” Kumar says. “It provided me with opportunities to learn new skills and ask questions about this topic. MathWorks gave me a chance to explore my interests and helped me navigate from being a steel engineer to a cancer scientist.”

Kumar’s work on the relationship between bacteria and ovarian cancer started with studying which bacteria are incorporated into tumors in mouse models.

“We started looking closely at changes in cell structure and how those changes impact cancer progression,” he says, adding that MATLAB image processing helps him and his collaborators track tumor metastasis.

The research team also uses RNA sequencing and MATLAB algorithms to construct a taxonomy of the bacteria.

“Once we have identified the microbiome composition,” Kumar says, “we want to see how the microbiome changes as cancer progresses and identify changes in, let’s say, patients who develop chemoresistance.”

He says recent findings that ovarian cancer may originate in the fallopian tubes are promising because detecting cancer-related biomarkers or lesions before cancer spreads to the ovaries could lead to better prognoses.

As he pursues his research, Kumar says he is extremely thankful to Belcher “for believing in me to work on this project.

“She trusted me and my passion for making an impact on human health — even though I come from a materials engineering background — and supported me throughout. It was her passion to take on new challenges that made it possible for me to work on this idea. She has been an amazing mentor and motivated me to continue moving forward.”

For her part, Belcher is equally enthralled.

“It has been amazing to work with Ashutosh on this ovarian cancer microbiome project," she says. "He has been so passionate and dedicated to looking for less-conventional approaches to solve this debilitating disease. His innovations around looking for very early changes in the microenvironment of this disease could be critical in interception and prevention of ovarian cancer. We started this project with very little preliminary data, so his MathWorks fellowship was critical in the initiation of the project.”

Kumar, who has been very active in student government and community-building activities, believes it is very important for students to feel included and at home at their institutions so they can develop in ways outside of academics. He says that his own involvement helps him take time off from work.

“Science can never stop, and there will always be something to do,” he says, explaining that he deliberately schedules time off and that social engagement helps him to experience downtime. “Engaging with community members through events on campus or at the dorm helps set a mental boundary with work.”

Regarding his unusual route through materials science to cancer research, Kumar regards it as something that occurred organically.

“I have observed that life is very dynamic,” he says. “What we think we might do versus what we end up doing is never consistent. Five years back, I had no idea I would be at MIT working with such excellent scientific mentors around me.”

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• Ashutosh Kumar
• Department of Materials Science and Engineering
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• Graduate, postdoctoral
• Health sciences and technology
• Artificial intelligence
• Bioengineering and biotechnology
• Materials science and engineering

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