Analysing further education recruitment trends using a large language model

  • We analysed over 36,000 adverts posted between 1st May 2022 and 31st August 2024 by 134 different FE colleges (representing nearly 70% of all those in England). There was some seasonality, with peaks in the summer, but much less so than we have previously observed for schools.
  • About half of the adverts were for teaching postions and most of the rest were for support positions. The most common subject areas for teaching positions were in construction, engineering and health. The most common specific subject was maths.
  • The vast majority of postions were permanent and most of them were full time. Among adverts for part-time vacancies, teaching postions were most likely to specify 0.5, 0.6 or 0.8 FTE, while support positions were more likely to specify higher time commitments. Annual leave entitlements varied widely, but were most commonly in the range 30-40 days.
  • Annual FTE salaries for support positions were most commonly in the £20-25k range, those for teaching positions peaked in the £30-35k range and those for leadership positions in the £40-45k range (but with wider variation).
  • An analysis of putative repeat advertising rates, which made use of the ability of LLMs to analyse the semantic similarity of adverts, suggested that around 40% of adverts may be followed by repeats for the same vacancy.

Figure 1: Numbers of FE college recruitment adverts per month
Sources: AoC Jobs; SchoolDash analysis.
Figure 2: Numbers of FE college recruitment adverts by role (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 3: Numbers of FE college teacher recruitment adverts by subject group (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Note: Any given advert might fall into more than one group.
Figure 4: Numbers of FE college teacher recruitment adverts by subject (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Note: Any given advert might specify more than one subject.
Figure 5: Numbers of FE college teacher recruitment adverts by duration of position (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 6: Numbers of FE college teacher recruitment adverts by time commitment of position (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 7: Proportion of recruitment adverts by stated FTE time commitment (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Note: The '0.1-0.2' category includes any values above 0.1 up to and including 0.2, and so on for other categories.
Figure 8: Proportion of FE college teacher recruitment adverts by number of days leave (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Note: The '10-20 days' category includes any values above 10 up to and including 20, and so on for other categories.
Figure 9: Proportion of FE college teacher recruitment adverts mentioning flexible working (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 10: Proportion of recruitment adverts by stated salary (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Notes: Where a salary range was given, this figures uses the mid point. For example, '£20,000 to £25,000' would appear here as £22,500. The '£10-15k' category includes any values above £10k up to and including £15k, and so on for other categories.
Figure 11: Number of FE college teacher recruitment adverts by qualifications required (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Note: Any given advert might mention more than one qualification.
Figure 12: Similarity scores of advert pairs from the same FE college employer (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 13: Numbers of putative repeat adverts by days since the preceding similar advert (May 2022 - August 2024)
Sources: AoC Jobs; SchoolDash analysis.
Figure 14: Putative repeat advertising rate
Sources: AoC Jobs; SchoolDash analysis.
  1. The taxonomy for subjects was similar to the one used in our previous analysis of college website adverts. 583 terms were organised into a hierarchical structure that encompassed 104 subjects organised into 58 subject areas and ultimately aggregated into 30 subject groups: Agriculture, environmental and animal care; Business and administration; Care services; Catering and hospitality; Computing and ICT; Construction and the built environment; Crafts, creative arts and design; Creative and design; Digital; Education and childcare; Engineering and manufacturing; Foundation learning; Geographical and environmental studies; Hair and beauty; Health and science; Historical, philosophical and religious studies; Languages, literature and culture of British Isles; Learning support; Legal, finance and accounting; Mathematics; Media and communication; Other languages, literature and culture; Performing arts; Protective services; Psychology; Sales, marketing and procurement; Sciences; Social sciences; Sport, leisure and recreation; Transport and logistics. This taxonomy was developed in collaboration with The Gatsby Foundation.
 

Grouping schools to tackle disadvantage

  • Cluster 1, Suburban: This represents 'middle England' outside the major cities. Socioeconomic and educational indicators are mostly unexceptional.
  • Cluster 2, Affluent Suburban: Richer suburban and rural neighbourhoods. The incidence of income deprivation is very low, but educational outcomes are not as good as you might expect.
  • Cluster 3, Affluent Urban: Richer city areas, especially in London. Much greater levels of income deprivation than Cluster 2, but also higher levels of educational engagement and better outcomes.
  • Cluster 4, Poor Urban: Especially in the North and the Midlands, but also to the east of London and elsewhere. Lots of adverse socioeconomic indicators, coupled with relatively weak educational outcomes – though not as bad as you might think given the levels of poverty.
  • Cluster 5, Poor Suburban: Again, mainly in the North and Midlands. IMD indicators are mixed, but income deprivation is high and educational outcomes are poor. These are the areas that have fallen furthest behind.
  • Cluster 6, Urban: Middling city areas in London, Birmingham and Manchester, among other places. Moderately high levels of income deprivation, but relatively good educational outcomes.

Figure 1: K-means inertia measure against number of clusters
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students; SchoolDash analysis.
Figure 2: Two school clusters shown by their two principal statistical components
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students;
SchoolDash Insights; SchoolDash analysis.
Figure 3: Locations of schools in Clusters 1 and 2
 
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students;
SchoolDash Insights; SchoolDash analysis.
Figure 4: Four school clusters shown by their two principal statistical components
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students; SchoolDash analysis.
Figure 5: Locations of schools in Clusters 1 to 4
 
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students;
SchoolDash Insights; SchoolDash analysis.
Figure 6: Six school clusters shown by their two principal statistical components
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students; SchoolDash analysis.
Figure 7: Six school clusters shown by their three principal statistical components
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students; SchoolDash analysis.
Figure 8: Locations of schools in Clusters 1 to 6
 
Sources: Department for Education; Department for Levelling Up, Housing and Communities; Office for Students;
SchoolDash Insights; SchoolDash analysis.

  1. For Income, we actually used the IDACI subcomponent, which specifically covers income deprivation among families with children.
 

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