Abstract
Abstract
The literature focusing on highly able programmers and in particular effective strategies to support them is limited. In the wider educational context beyond Computing, the conception of ‘gifted’ learners has been undergoing a shift with research suggesting that existing strategies have been reproducing inequalities, particularly for pupils from disadvantaged backgrounds. It has been suggested that individual interventions are not effective in supporting highly able learners. Instead, a suite of different types of interventions (academic extension; cultural enrichment; personal development and removal of financial barriers) is required.
In this study we sought to develop an understanding of the strategies currently being employed to support highly able programmers in secondary schools in England. We conducted a national survey of secondary computing teachers and received 108 complete responses from across the country. Participants were asked to describe the strategies they employ to support their most able programmers. These responses were coded and assigned to one (or more) of the four types of intervention. The majority (85%) of teachers described interventions which involved academic extension, with 24% describing interventions which involved personal development. Only 1 teacher mentioned cultural enrichment activities and none discussed removal of financial barriers.
The participants were also asked to rate the training they received in supporting highly able programmers. Half of the participants reported receiving no training during their pre-service teacher education and 30% reported receiving no in-service training in this area.
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1 Background
1.1 Highly able programmers
The literature focusing on supporting highly able programmers is limited. In terms of recommended pedagogical approaches for highly able learners in programming education, project-based learning [19] and self-directed activities [6] are advocated.
Research has explored the factors which can predict success in undergraduate programming courses [3, 4]. These studies have suggested that spatial visualisation skills [4] and spatial training [3] are correlated with success. Interestingly, spatial skills are also identified as an important factor in identifying and supporting highly able learners across STEM disciplines [18]. However, while there is research which explores highly able learners in STEM disciplines, it is still an under-researched area and much of the literature that does exist places emphasis on Science and Mathematics [18].
Related research has explored the potential benefits of engaging in programming education for learners that are considered to be generally highly able across disciplines [8] or in related disciplines such as mathematics [19]. Engaging in programming education for highly able learners has been shown to have the potential to improve their problem-solving [19] and analytical thinking skills [8]. Additionally, it is suggested that coding is beneficial for highly able learners as it engages higher order thinking skills [14].
Due to the limited research that is directly relevant to supporting highly able programmers, it is necessary to draw upon wider literature relating to highly able learners and how they can be effectively supported.
1.2 Changing conceptions of highly able learners
In England there is no nationally agreed definition for highly able pupils [9]. However, the Young Gifted and Talented Programme (YGTP) was in place until 2010, which placed an expectation on schools to identify and support their gifted and talented learners. These learners were characterised as “children and young people with one or more abilities developed to a level significantly ahead of their year group (or with the potential to develop those abilities)” [2, p. 1].
The concept of gifted learners has been undergoing a shift for some time [17]. Historically, giftedness has been considered as closely linked to genetic factors, being applicable across domains and remaining stable throughout our lives. Research has demonstrated that, rather than being innate, giftedness develops from a complex interaction of genetic and environmental factors [10]. For example, while there is evidence to support the role of genetic factors, there is also clear evidence which demonstrates the role of neuroplasticity in the development of gifted learners [7]. Additionally, while a high level of general intelligence does confer a learning advantage in many tasks, it is not a predictor of domain specific expertise [10].
Changing conceptions of what it means to be a ‘gifted’ learner have led to the term itself being critiqued, as it implies that giftedness is innate and the label can carry elitist connotations [11]. This shift in conception has seemingly led to the term ‘gifted’ falling out of usage in English schools, being replaced by terms such as ‘more able’, ‘most able’ and ‘highly able’ [16]. Additionally, a separate term ‘talented’ was previously used in England to refer to learners that are highly able in non-academic or vocational subjects. Today, little distinction is made between ‘talented’ and other ‘highly able’ learners [16]. For the purposes of this research, we have chosen to employ the term ‘highly able’.
1.3 Criticisms of gifted education initiatives
Critics of gifted educational initiatives argue that despite efforts to ensure learners from all backgrounds benefit, in reality they have often reproduced and reinforced existing inequalities in society [10]. This view is supported by research in England which highlights the attainment gap between highly able pupils from disadvantaged backgrounds and other highly able pupils. Research by the Sutton Trust found that disadvantaged pupils that were high performing in primary school have a much higher chance of falling behind in secondary school when compared to other high performing students [12]. Additionally, concerns have been raised regarding the poor attainment of highly able pupils in selective secondary schools compared to their peers in non-selective schools [13]. Concerns regarding these and other inequalities led to the introduction pupil premium funding in 2011 to support schools in raising the attainment of pupils from disadvantaged backgrounds [9].
Such concerns regarding the relative under attainment of highly able pupils from disadvantaged backgrounds led the UK government to commission research to identify effective approaches that schools can use to support highly able pupils from disadvantaged backgrounds [1]. This research led to the development of a model which encapsulated the key components of a school strategy that effectively supports highly able pupils from disadvantaged backgrounds. These key components are:
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Leadership and infrastructure (e.g. identification of highly able disadvantaged pupils, staff training),
•
Four activity strands (Academic extension, cultural enrichment, personal development and removal of financial barriers to achievement),
•
Partnerships with parents, universities and other external organisations (underpin the four activity strands),
•
Monitoring, review and evaluation (monitoring and reviewing impact of activities).1
Cullen et al. [1] suggest that individual interventions alone from the four activity strands are not effective in supporting highly able disadvantaged students and the most impactful approach involved a suite of activities across these four areas. While all activity strands are important, the academic extension activity strand is the most significant, with the need for the other strands varying in intensity depending on the school context [1].
2 Methodology
This study seeks to begin to address the gap in the literature regarding the strategies that secondary computing teachers in England employ to support highly able programmers. The model of successful support for highly able disadvantaged pupils developed by Cullen et al. [1] is employed as a framework for the analysis of the data. Although this study investigates the strategies employed to support all highly able programmers and not only those from disadvantaged backgrounds, we argue that this model is still relevant as it is designed to address concerns regarding highly able pupil programmes reinforcing inequalities [10].
2.1 Research questions
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What strategies do secondary computing teachers in England employ to support highly able programmers?
•
How do these strategies align with the Cullen et al. [1] model for effective support of highly able learners from disadvantaged backgrounds?
•
What training have secondary computing teachers in England received in effectively supporting highly able programmers?
2.2 Data collection
We developed an online survey as part of a larger research project designed to investigate the current state of programming education in secondary schools in England. The survey included questions covering the following areas:
•
Demographic information, programming background, school type and location,
•
Clubs, extra-curricular activities and competitions,
•
Programming languages taught at KS3/4,
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Programming pedagogies employed,
•
Strategies for supporting highly able programmers,
•
Training received in supporting highly able programmers.
The research reported in this paper focuses on the analysis of the data gathered relating to the last two areas, with the data relating to the first area being used to provide contextual information. The analysis of the data relating to the other areas is examined in a separate technical report [5].
The survey was designed to be anonymous, however participants were asked to either provide information regarding the characteristics of their school/college or its Postcode/URN (unique reference number) so this information could be obtained.
Ethical clearance for the study was sought and obtained from the King’s College London Ethics Committee (Ethical Clearance Reference Number: LRS/DP-23/24-37000).
2.2.1 Strategies for supporting highly able programmers.
In order to address the first research question, we included the following open question in the survey: ’How do you push / support your best programmers?’. The question was intentionally left broad and was not broken down according to the areas outlined in the Cullen et al. [1] model to avoid leading responses. The subsequent analysis of the responses employed the model as a structure in order to address the second research question.
2.2.2 Training received in supporting highly able programmers.
To address the third research question, we included a question in the survey which explored the training that teachers have received in the teaching of highly able programmers. We asked the participants to respond to the question ’How do you rate the training you have received in supporting highly able programmers?’ using a five-point Likert scale with a ’none received’ option. They were asked to rate both the pre-service and in-service training they had received separately.
2.3 Distribution
The survey was distributed through organisations involved in computing education in England such as CAS (Computing at School) and examination boards. Additionally, it was shared through social networks such as X, LinkedIn and closed Facebook groups for Computing teachers. There were 166 responses to the survey, however, only 108 of these were complete responses and we removed incomplete submissions. The survey was piloted prior to its general release, and the pilot raised no concerns about the survey design.
2.4 Analysis
2.4.1 Qualitative Analysis.
As a starting point for the analysis of the open text responses to the question ’How do you push / support your best programmers?’, we defined initial themes based upon the areas of the Cullen et al. [1] model which relate to how teachers may directly support their highly able students. Therefore, we chose to base the initial themes for coding on the four activity strands (academic extension, cultural enrichment, personal development, removal of financial barriers to achievement) along with the partnership component as it underpins the four activity strands.
The open text responses were initially thematically coded independently by a member of the research team, this process involved the expansion of the initial themes through the creation of new codes that fell under each theme. These thematic codes were validated by another member of the team who independently applied the codes to a sample of open text responses. Any discrepancies were reviewed by the research team and alterations to the codes were agreed. Finally, the coding of all responses was reviewed once more to ensure that the thematic codes had been applied consistently across the data set.
2.4.2 Quantitative Analysis.
We used the R statistical programming language to create descriptive tables of the demographic details of respondents, helping us gauge the representativeness of the responses against the national picture. Taking the results from the qualitative analysis described above, we created tables showing the number of participants and overall percentage of participants who provided a response that matched each of the four activity strands, plus the partnership intervention, outlined in Cullen et al. [1]. We then created tables on the subcategories we found in academic extensions, personal development interventions, and cultural enrichment interventions. Finally, we explored the ’staff training’ element of the ’leadership and infrastructure’ component described in Cullen et al.’s [1] model, graphing the Likert scale responses to questions on in-service and pre-service training.
2.5 Contextual information
Although we received responses from across England, Table 1 demonstrates that over half of the responses (60%) originated from London and the South of England. This is likely to be related to the research team and much of their professional network being based in London. Table 2 shows that nearly three quarters (72%) of responses came from state-funded non-selective schools, compared to 88% nationally for all schools of this type.
Table 1:
| Region | n | % |
|---|---|---|
| East Midlands | 4 | 3.7 |
| East of England | 7 | 6.5 |
| London | 34 | 31.5 |
| North East | 5 | 4.6 |
| North West | 6 | 5.6 |
| South East | 19 | 17.6 |
| South West | 12 | 11.1 |
| West Midlands | 14 | 13.0 |
| Yorkshire and the Humber | 4 | 3.7 |
| missing / unmatched | 3 | 2.8 |
School/College Location
Table 2:
| Type | n | % |
|---|---|---|
| State Funded Non-Selective | 78 | 72.2 |
| State Funded Selective | 9 | 8.3 |
| Privately Funded | 18 | 16.7 |
| missing / unmatched | 3 | 2.8 |
School/College Type
3 Results
3.1 Approaches for supporting highly able programmers
As shown in Table 3, the majority of teachers described approaches to supporting their highly able programmers which align with the one or more of the four activity strands outlined by Cullen et al. [1], with academic extension being by far the most popular strand. In addition to strategies that align with the four activity strands, a minority of the strategies described (5%) aligned with the partnership component of the model. The removal of financial barriers strand did not feature in any responses, this is likely due to the fact that the participants were focusing on all highly able programmers and not specifically on those from disadvantaged backgrounds.
Table 3:
| Types of intervention | Description | n | %* |
|---|---|---|---|
| Academic extension | Activities which ’stretch and challenge’ | 92 | 85 |
| Personal development | Opportunities designed to raise confidence | 26 | 24 |
| Cultural enrichment | Opportunities designed to broaden horizons | 1 | 1 |
| Removal of financial barriers | Interventions to address material poverty | 0 | – |
| Partnerships | External partnerships with parents, industry and universities | 5 | 5 |
| *Percentage of participants | |||
Types of intervention by category
Table 4:
| Extension types | Description | n | %* |
|---|---|---|---|
| Programming challenges | Bank of short programming challenges | 68 | 63 |
| Extended projects | Extended independent programming projects | 13 | 12 |
| Freedom to choose tasks | Freedom to choose tasks related to their interests | 10 | 9 |
| Extra-curricular clubs | Participating in extra-curricular activities such as clubs | 9 | 8 |
| Practice at home | Encouraging and support programming practice at home | 7 | 6 |
| Beyond current level | Setting tasks beyond current level of study | 6 | 6 |
| Learning other languages | Learning other programming languages and frameworks | 6 | 6 |
| Alternative approaches | Applying alternative approaches to solve the same problem | 4 | 4 |
| Online courses | Online courses in advanced programming techniques | 3 | 3 |
| Exam practice | Exam practice using past exam questions | 3 | 3 |
Academic extension interventions
Table 5:
| Development types | Description | n | %* |
|---|---|---|---|
| Competitions | Supporting entry to competitions such as Informatics Olympiad | 17 | 16 |
| Mentorship and talks | Careers talks and mentors from industry | 5 | 5 |
| Mentoring others | Students mentoring others, for example by running a club | 3 | 3 |
Personal development interventions
Table 6:
| Enrichment types | Description | n | %* |
|---|---|---|---|
| Computing trips | Running computing related school trips | 1 | 1 |
Cultural enrichment interventions
Table 7:
| Partnership type | Description | n | %* |
|---|---|---|---|
| Working with parents | Build relationships with parents, for example through parents’ evenings | 1 | 1 |
| Working with universities | Develop partnerships with local universities | 1 | 1 |
| Working with industry | Develop links with industry, for example through STEM ambassadors | 4 | 4 |
| *Percentage of participants | |||
External partnerships
3.1.1 Academic extension.
The different interventions which fall under the academic extension activity strand are outlined in Table 4. The setting of in class programming challenges was the most popular type of academic extension (63%), followed by extended projects (12%). The use of extended projects aligns with the findings of [19] which suggest that project-based learning is an effective pedagogical approach for supporting highly able learners. 9% of teachers reported giving their highly able programmers freedom to choose tasks that relate to their interests.
3.1.2 Personal development.
Personal development includes activities which build confidence and address emotional or social issues. Supporting pupils entering and completing competitions is by far the most popular type of personal development activity, as shown in Table 5. Although it could also be argued that competitions also constitute a form of academic extension, Cullen et al. [1] suggest that they primarily serve as a form of personal development. Some teachers mentioned offering mentoring to their highly able pupils and arranging for external speakers to give talks. Additionally, some highly able programmers are given the opportunity to develop their confidence through mentoring others, for example by running clubs for younger pupils.
3.1.3 Cultural enrichment.
As shown in Table 6, there was only one mention of a cultural enrichment activity which was a computing trip. This is one potential area in which the removal of financial barriers could come into play as cost may be a barrier to participation for some pupils.
3.1.4 Partnerships.
Few teachers described external partnerships when discussing the approaches they employ to support their highly able programmers. Table 7 shows that the most common form of external partnership was with industry, whereas working with parents and universities only had one mention each. Cullen et al. [1] highlight the importance of working with parents to develop effective support strategies for highly able learners, particularly from disadvantaged backgrounds.
3.2 Training on teaching highly able programmers
Participants were also asked about any training they had received on the teaching of highly able programmers. Figure 1 shows that half of the teachers received no training in the teaching of these programmers during pre-service teacher education.
Figure 1:
A bar chart.
Teacher rating of their pre-service training on teaching highly able programmers
Figure 2 shows that when asked about in-service training the participants had received in the teaching of highly able programmers, 30% reported having received none. Only 47% of the teachers had received training which they felt was at least adequate and 22% received training which was either poor or very poor.
Figure 2:
A bar chart.
Teacher rating of their in-service on teaching highly able programmers
4 Discussion
The participants identified a range of approaches for supporting highly able programmers and these align with many areas of the model for effective support of highly able pupils from disadvantaged backgrounds proposed by Cullen et al. [1]. Central to the model are the four types of activity which interventions can fall into, these are academic extension, personal development, cultural enrichment and removal of financial barriers. The majority of the approaches described by the participants fall under the category of academic extension, with programming challenges being the most popular type of extension. Academic extension being the most popular activity type aligns with Cullen et al. [1] who describe it as being the most important. Although they stress the importance of covering more than one area of activity, they also highlight that the necessary intensity of the other areas will vary depending on the context of each school.
The second most popular of the four strands of activity is personal development, with supporting students entering competitions being the most popular activity within this strand. Even though it was the most popular, it still only accounts for 16% of teachers. This is interesting considering a recent report highlighted that nearly three quarters of secondary schools in England support students in entering external computing related competitions [5], however, many of these competitions are not solely focused on programming. It is possible that many teachers believe that all students should have the opportunity to enter competitions, and they do not view them as something targeted at highly able programmers specifically.
In the Cullen et al. [1] model the four types of activity are underpinned by three other components: leadership and infrastructure; partnerships; and monitoring, review and evaluation. One aspect of leadership and infrastructure is the provision of staff training for teachers to effectively support highly able learners [1]. Research in broader STEM education also stresses the importance of teachers receiving training to support highly able pupils [15, 18]. When asked about in-service development opportunities relating to supporting highly able learners, 30% of teachers reported receiving no training and a further 22% reported receiving training that was poor or very poor. Additionally, almost half of teachers reported receiving no training in this topic in their pre-service education. This might not be surprising due to the lack of specialist research into supporting highly able programmers. This highlights an urgent need to improve the provision of training in effectively supporting highly able programmers.
Relatively few teachers mentioned external partnerships and most of those mentioned involved working with industry. However, Cullen et al. [1] suggest the most important partnership is developing effective relationships with parents to support highly able pupils and only one teacher mentioned this. It is of course possible that many teachers do this as a matter of course and do not consider it to be a strategy worth mentioning for supporting this group of learners in particular.
5 Limitations
It is important to note that the survey sample was self-selecting and therefore the participants are more likely to be actively involved in the wider Computing education community in England. This may have influenced the results. However, we sought to mitigate this factor by ensuring the survey was distributed through a range of channels. Additionally, more responses were received from teachers based in London and the south of England. This is likely to be due to the research team and a portion of their professional network being based in London. Nevertheless, responses were received from across the country and therefore believe the results will give valuable insights which have implications for computing teachers nationally.
Although this research employed aspects of the Cullen et al. [1] model as framework for analysis, some components of the model were not fully explored. For instance, only the staff training aspect of leadership and infrastructure was examined. Additionally, the monitoring, review and evaluation component was not investigated. This is due to the focus of this research being on classroom practitioners and interventions they are likely to have some level of influence over. Further research is needed to explore the role that members of senior leadership in schools play in supporting highly able programmers.
6 Conclusion
In this study we identified a range of approaches that are currently being employed by secondary computing teachers in England to support their highly able programmers. The majority of strategies fall under the category of academic extension, followed by personal development. Other categories such as cultural enrichment and removal of financial barriers to achievement were substantially under-represented. This is important considering that Cullen et al. [1] suggest that a suite of activities across the categories is required in order to effectively support highly able programmers, particularly those from disadvantaged backgrounds.
Another important aspect that underpins the Cullen et al. [1] model is leadership and infrastructure and this includes providing opportunities for staff to receive in-service training in effectively supporting highly able pupils, however 30% of teachers reported receiving no training in this area. Furthermore, almost half of teachers received no such training during their pre-service teacher education. This demonstrates an urgent need to improve the provision of teacher training in supporting highly able programmers and the research that will inform this.
This study makes an important contribution to this under-researched area by reporting the strategies that teachers employ to support their highly able programmers. However, further research is required in order to investigate the effectiveness of different approaches.
Footnote
1
Adapted from Cullen et al. [2018]
References
[1]
Stephen M Cullen, Mairi-Ann Cullen, Siobhan Dytham, and Nikita Hayden. 2018. Research to understand successful approaches to supporting the most academically able disadvantaged pupils. Technical Report. Department for Education.
[3]
Madeline Endres, Madison Fansher, Priti Shah, and Westley Weimer. 2021. To read or to rotate? comparing the effects of technical reading training and spatial skills training on novice programming ability. In ESEC/FSE 2021 - Proceedings of the 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece), Vol. 21. Association for Computing Machinery, Inc, 754–766.
[4]
Sally Fincher, kentacuk Anthony Robins, Bob Baker, Quintin Cutts, Patricia Haden, Margaret Hamilton, Marian Petre, Denise Tolhurst, Ilona Box, Michael de Raadt, John Hamer, Raymond Lister, Ken Sutton, and Jodi Tutty. 2006. Predictors of Success in a First Programming Course. In ACE ’06: Proceedings of the 8th Australasian Conference on Computing Education - Volume 52 (Hobart, Australia), Vol. 52. Australian Computer Society, 189–196.
[5]
Alex Hadwen-Bennett and Peter EJ Kemp. 2024. Programming in Secondary Education in England: Technical Report. Technical Report. King’s College London.
[6]
Julia Hagge. 2017. Scratching Beyond the Surface of Literacy: Programming for Early Adolescent Gifted Students. Gifted Child Today 40, 3 (2017), 154–162. arXiv:https://doi.org/10.1177/1076217517707233
[7]
Claire M.A. Haworth and Robert Plomin. 2010. Quantitative genetics in the era of molecular genetics: Learning abilities and disabilities as an example. Journal of the American Academy of Child and Adolescent Psychiatry 49 (2010), 783. Issue 8.
[8]
Berrak Kocaman. 2023. The Effect of Coding Education on Analytical Thinking of Gifted Students. International Journal of Educational Methodology volume-9-2023, volume-9-issue-1-february-2023 (2023), 95–106. arXiv:https://doi.org/10.12973/ijem.9.1.95
[10]
Dona J. Matthews and David Yun Dai. 2014. Gifted education: changing conceptions, emphases and practice. International Studies in Sociology of Education 24 (10 2014), 335–353. Issue 4.
[11]
Michael S. Matthews and Jennifer L. Jolly. 2022. Why Hasn’t the Gifted Label Caught up with Science? Journal of Intelligence 10 (12 2022). Issue 4.
[12]
Rebecca Montacute. 2018. Potential for Success: Fulfilling the promise of highly able students in secondary schools. Technical Report. Sutton Trust.
[13]
Ofsted. 2013. The most able students: Are they doing as well as they should in our non-selective secondary schools? Technical Report. Ofsted. www.nationalarchives.gov.uk/doc/open-government-licence/,
[15]
Bruria Shayshon, Hagar Gal, Bertha Tesler, and Eun Sung Ko. 2014. Teaching mathematically talented students: a cross-cultural study about their teachers’ views. Educational Studies in Mathematics 87 (9 2014), 409–438. Issue 3.
[17]
Rena F. Subotnik, Paula Olszewski-Kubilius, and Frank C. Worrell. 2011. Rethinking Giftedness and Gifted Education. Psychological Science in the Public Interest 12 (1 2011), 3–54. Issue 1.
[18]
Bestami Bugra Ulger and Salih Çepni. 2020. Gifted Education and STEM: A Thematic Review. Journal of Turkish Science Education 17 (9 2020), 443–466. Issue 3.
[19]
Hsiu Ying Wang, Iwen Huang, and Gwo Jen Hwang. 2014. Effects of an Integrated Scratch and Project-Based Learning Approach on the Learning Achievements of Gifted Students in Computer Courses. In 2014 IIAI 3rd International Conference on Advanced Applied Informatics. 382–387.