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Language Choice in Introductory Programming Courses at Australasian and UK Universities Simon, Raina Mason, Tom Crick, James H. Davenport and Ellen Murphy University of Newcastle, Southern Cross University Swansea University, University of Bath

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Language Choice in Introductory Programming Courses at Australasian and UK Universities

Simon, Raina Mason, Tom Crick, James H. Davenport and Ellen Murphy

University of Newcastle, Southern Cross University Swansea University, University of Bath ×2

24 February 2018

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 1 / 24

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Introduction

Background

2001 onwards Longitudinal and Similar Surveys conducted in Australia and New Zealand Structurally Several independent states with a common educational heritage, targeted degrees but many common modules UK Four education administrations (but England is 90%) England&Wales specialist degrees, few common modules, Scotland “choose a major” 2014–16 UK-wide Shadbolt review – accreditation and graduate employability in computer science Therefore we thought UK needed such a survey

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 2 / 24

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Introduction

Methodologies

Both were online surveys. UK Mailing list of professors/heads Aus Email invitations were sent to past participants, a relevant mailing list, and academics identified from their University’s website. Not all institutions teach CS, but UK 70 institutions (47%) Aus 35 institutions (57%) Some institutions have parallel courses (so 80/48 courses) Health warning on sampling: [MS17, end of §3.1]

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 3 / 24

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Australasia

Demographics of instructors

Years of Experience Aus: 48 courses UK 80 courses Not much “give it to the newbie”; effect, at least in UK

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 4 / 24

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Australasia

Australasia 2013 survey [MC14]

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 5 / 24

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Australasia

Australasia Trends (weighted by student numbers)

2001 2003 2010 2013 change Python 0% 0% 20% 34% 14% Java 44% 44% 39% 27%

  • 12%

Javascript 0% 0% 1% 10% 9% C 6% 11% 12% 9%

  • 3%

C# 0% 0% 8% 5%

  • 3%

C++ 15% 19% 5% 3%

  • 2%

Matlab 0% 1% 1% 2% 1% Haskell 9% 6% 0% 2% 2% Ada 2% 0% 0% 2% 2% VB/VB.NET 19% 16% 5% 1%

  • 4%

Alice 0% 0% 1% 1%

  • 0%

Processing 0% 0% 5% 0%

  • 5%

Fortran 0% 1% 4% 0%

  • 4%

2016 figures [MS17] show no significant changes from 2013.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 6 / 24

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Australasia

Australasia 2013 reasons [MC14]

Python: All of the Python-using participants gave the following reasons for their choice (varying importance):

  • Availability/Cost to students
  • Easy to find texts
  • Extensions/Libraries available
  • Platform independence

Java: In contrast, all of the Java-using participants gave the following reasons for their choice (varying importance):

  • Object-Oriented Language
  • Online community/Help available
  • Relevant to industry

Note the absence of overlap, even when clearly present

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 7 / 24

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UK

UK Context and Shadbolt Review [S16]

Prediction that by 2022 some 518,000 additional workers will be needed to fill the roles available for the three highest skilled

  • ccupational groups in the digital arena. This is three times the

number of Computer Sciences graduates produced in the past 10 years In this context, apparently high rates of unemployment amongst graduates of Computer Sciences demanded an explanation. Unemployment among Computer Sciences graduates is currently running at a little over 10%. Although more likely to be unemployed, compared to other STEM graduates, Computer Sciences graduates who are in employment are more likely to be in graduate level work and well paid.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 8 / 24

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UK

What the UK team did

Surveyed 80 instructors from at least 70 institutions across England, Wales, Scotland and Northern Ireland (attempted to weed out duplicates) This represents 13,462 students (excluding the Open University’s 3200 students), compared with a total of around 19,000 Questions aligned to those used in the Australian and New Zealand Surveys Asked questions on the: programming language(s) used in introductory programming courses use of development tools and IDEs main aims when teaching introductory programming

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 9 / 24

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Results

Course aims

A number of themes were clearly dominant across both surveys: Fundamentals of programming, programming concepts Problem solving Algorithmic/computational thinking Programming language syntax and basic code Student enjoyment/motivation The specifics of particular programming languages were seldom rated as highly as more generic concepts such as problem solving, algorithmic thinking, and programming concepts.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 10 / 24

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Results

UK Results: Language popularity

10 20 30 40 50 Java C Family Python C++ C Javascript Haskell C\# Processing Matlab PHP Alice Objective Perl Language instances Students

Total of 106 language instances (in introductory prog.) 59 courses using just one language 17 courses using two languages 4 courses using three or more languages

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 11 / 24

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Results

Contrast: Languages

Aus early 2000s Definitely Java (44%) Aus 2013 [MC14] Python/Java equal on courses, Python winning on students Aus 2016 [MS17] unchanged UK 2016 [MCD17] Java (46%, used in 61% of courses), Python distant second, beaten by “C family”

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 12 / 24

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Results

UK Results: Reasons for choosing a language

OS/Machine limitations of department Don't know / other Interpreted language Department politics Ease of installation GUI interface available Online community and help available Structure of degree Platform independence Extensions Libraries available Marketable to students Easy to find appropriate texts Pedagogical benefits Availability / Cost to students Object oriented language Relevant to industry 25 50 75 All Java Python

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 13 / 24

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Results

UK Results: Difficulty vs Utility (of teaching fundamentals)

2 4 6 C C\# C++ Haskell Java Javascript Matlab Processing Python Median Difficulty Usefulness

Difficulty: 1 Extremely easy – 7 Extremely difficult Utility: 1 Extremely useless – 7 Extremely useful

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 14 / 24

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Results

UK Results: Reasons for choosing a language

Top reasons for a language: Relevance to industry (55%; 60% Java; 37% Python) Object-oriented language (55%; 88% Java; 18% Python) Availability and cost to students (55%; 56% Java; 64% Python) Pedagogical benefits (48%; 39% Java; 73% Python) Why Java? Relevance to industry Object-oriented language Why Python? Pedagogical benefits

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 15 / 24

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Contrast

Contrast: Language Difficulty

Figure: Median perceived difficulty of the language for novices; 1 = Least Difficult

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 16 / 24

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Contrast

Contrast: Utility for Teaching Fundamentals

Figure: Median perceived usefulness of the language for teaching programming fundamentals; 1= Least Useful

Marked differences

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 17 / 24

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Contrast

Contrast: Reasons for choosing a language

Reason Aus 2013 UK 2016 Pedagogical benefits 1 4 Platform independence 2 8 (curious) Relevant to industry 3 =1 Availability / Cost to students 4 =1 Object oriented language 5 =1 Easy to find appropriate texts P6 J5 Marketable to students 7 6 GUI interface available 8 11 Structure of degree 9 9 Ease of installation =10 12 Online community and help J=10 P10 Extensions/Libraries available P12 (both) 7

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 18 / 24

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Contrast

Questions

1 Why the difference in “Utility for Teaching Fundamentals”? 2 Why does the UK teach Java even though Python is perceived as

easier? Is it the “Relevant to industry” argument?

3 If Scotland is closer to Australasia, why don’t we see more Python in

Scotland? [MCD17]

4 Will the growth of Python in “Data Science” change the “Relevant to

industry” argument?

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 19 / 24

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Institute of Coding

Timeline

17/11/2015 Announced by George Osborne at GCHQ. https://www.gov.uk/government/speeches/ chancellors-speech-to-gchq-on-cyber-security. 27/3/2017 Competition launched by HEFCE (England!). http://www.hefce.ac.uk/pubs/Year/2017/CL,082017/ £20 million in HEFCE funding is available from 1 April 2017 to 31 March 2019, needs matching. 25/1/2018 Announced by Theresa May at Davos. https://www.gov.uk/government/speeches/ pms-speech-at-davos-2018-25-january And we are establishing an Institute of Coding — a consortium

  • f more than 60 universities, businesses and industry experts to

support training and retraining in digital skills.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 20 / 24

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Institute of Coding

HEFCE goals

  • a. To increase the quality and support the growth of digital skills

provision in England at Levels 6 and 7 (Bachelor/Master).

  • b. To create and promote innovative learning and teaching models.
  • c. To make a tangible, lasting and measurable impact on digital skills

provision at national level. But, even though it’s nothing to do with cryptography, and everything to do with digital skills, it has to be called the Institute of Coding not the Institute of Digial Skills, because that’s what a minister called it.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 21 / 24

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Contrast

Consortium

Lead University of Bath, Director Rachid Hourizi Theme Leads Open University, Aston, Coventry, QMUL, Bath 13 more Universities (currently: enlargement possible, but not immediately) 50+ Industries of various sizes: both IT and non-IT. Shadbolt “there is a current lack of a coherent employer voice on what makes an employable Computer Sciences graduate”.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 22 / 24

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Bibliography

Bibliography I

  • R. Mason and G. Cooper.

Introductory programming courses in Australia and New Zealand in 2013 — trends and reasons. ACE ’14 Proceedings of the Sixteenth Australasian Computing Education Conference, 148:139–147, 2014.

  • E. Murphy, T. Crick, and J.H. Davenport.

An Analysis of Introductory Programming Courses at UK Universities. The Art Science and Engineering of Programming, 1(2):18-1–18-23, 2017.

  • R. Mason and Simon.

Introductory Programming Courses in Australasia in 2016. ACE ’17 Proceedings of the Nineteenth Australasian Computing Education Conference, ACM, New York, 2017, pp. 81–89.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 23 / 24

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Bibliography

Bibliography II

Sir Nigel Shadbolt. Shadbolt Review of Computer Sciences Degree Accreditation and Graduate Employability. https://www.gov.uk/government/uploads/system/uploads/ attachment_data/file/518575/ ind-16-5-shadbolt-review-computer-science-graduate-employability. pdf, 2016.

Simon et al. (J.H.Davenport@bath.ac.uk) 24 February 2018 24 / 24