MARC'S MASTERS DEGREE
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My Masters of Education Degree subject was based on my 10 years experience teaching digital technology at Alfriston College, including integrating Minecraft into the digital curriculum for students to develop their Computational Thinking, Computer Science and Computer Programming skills in a fun, engaging and meaningful way.
My research dissertation asked the question; What is Computational Thinking, Computer Science and Computer Programming in a New Zealand education context with a lens on Māori and Pasifika students?
A critical analysis of Computational Thinking (Whakaaro Hangarau), Computer Science (Mātai Rorohiko) and Computer Programming (Papatonotanga) Digital Technology (Hangarau Matihiko) in New Zealand schools.
Written by Marc Williams.
A dissertation submitted in fulfilment of the requirements for the degree of Master of Education.
The University of Auckland 2022
Abstract
This study looks at the intricate relationship between Computational Thinking (Whakaaro Hangarau), Computer Science (Mātai Rorohiko) and Computer Programming (Papatonotanga) in a school context with a lens on Māori and Pasifika students in New Zealand. Māori and Pasifika Year 11 to 13 students Computer Science and Programming academic results are very low as is their representation in the digital technology workforce.
The New Zealand Government ‘digital skills for a digital nation’ vision is for all New Zealanders to thrive in a digital age driven by a high-wage economy; “A workforce trained in computational problem solving spells efficiency, economic benefit, and even further advances to technology, research and innovation to drive productivity and increase wages”.
The New Zealand Ministry of Education’s 2016 directive that by 2020 all schools from Years 1 to 13 adopt the new compulsory Digital Technologies | Hangarau Matihiko curriculum is a significant shift from the previous policy of the Digital Technology curriculum being optional for Years 11 to 13 students. All students will experience the recently defined Learning Outcomes for the new Data Representation, Algorithm and Programming curriculum subjects. This new initiative and the new Computational Thinking pedagogy integration within the existing Digital Technology Computer Science and Programming curriculum is an opportunity to analyse this nascent iteration of Digital Technology education for this dissertation review.
Computational Thinking is a key focus of this dissertation. Internationally acknowledged as an essential skill set for the 21st Century, Computational Thinking is broadly categorised into the fields of; Abstraction, Algorithmic Thinking, Automation, Decomposition, Debugging, Iteration, and Generalisation. Definitions of Computational Thinking from academic literature highlight the variety of interpretations of what Computational Thinking is.
This study concludes that understanding the complexity of Computer Science and Computer Programming and the emerging status of Computational Thinking requires greater exposure at all levels of education to realise the equitable digital nation ambitions of New Zealand.
Table of Contents
Abstract
Table of contents
Chapter 1 - Introduction
1.1 General Introduction
1.2 Definitions of Computational Thinking, Computer Science and Programming
1.3 Rationale for this study
1.4 Gaps in the literature
1.5 Purpose and significance of this study
1.6 Research Question
1.7 Dissertation outline
Chapter 2 - Computational Thinking, Computer Science, Programming
2.1 Computational Thinking
2.2 Computer Science
2.3 Computer Programming and Coding
Chapter 3 - Computational Thinking in Education
3.1 Computational Thinking in international education
3.2 Computational Thinking in New Zealand education
Chapter 4 - Digital Technology in New Zealand Education
4.1 A history of computing in New Zealand education
4.2 The Digital Technologies | Hangarau Matihiko curriculum
4.3 Māori and Pasifika students
Chapter 5 - Teaching Computational Thinking and Computer Science
5.1 Challenges and Opportunities
5.2 Strategies
5.3 Teachers Professional Development
Chapter 6 - Methodology
6.1 Rationale for analysis
6.2 Search process
6.3 Inclusion and exclusion criteria
6.4 Scope and limitations of this study
Chapter 7 - Conclusions and Future Research
7.1 Conclusions
7.2 Recommendations for future research
Bibliography
Dissertation references
Chapter 1 - Introduction
1.1 General Introduction
Computational Thinking is described as an essential skill set for the 21st Century (Harris, 2018; Tang et al., 2020). The New Zealand Ministry of Education 2016 directive (Duncan et al., 2017) that by 2020 all schools from Years 1 to 13 integrate the new Digital Technologies | Hangarau Matihiko curriculum which includes Computational Thinking pedagogy into their curriculum for years 1 to 13, a significant policy shift from the existing Digital Technology programming curriculum being optional for years 11 to 13. “The learning progressions for digital technologies are structured to ensure that once students complete year 10 they will be ready, with good teaching, to be successful in all of the NCEA achievement standards in digital technologies. In the Computational Thinking progression, this means students will have developed a base of skills and knowledge in three key areas: data representation, algorithms, and programming (Ministry of Education, 2017). “The digital curriculum is about teaching children how to design their own digital solutions and become creators of, not just users of, digital technologies, to prepare them for the modern workforce." Chris Hipkins (2017) (New Zealand Government, 2017).
Computational Thinking education in New Zealand schools is at a nascent stage so it is an opportunity for this dissertation to study it’s integration with the existing Computer Science and Computer Programming curriculum. For this reason, it is important to understand the implications and impact especially for Māori and Pacific peoples as there is a decreasing participation in technology in education and a less diverse workforce based on recent research studies (New Zealand Digital Skills Forum, 2021).
This dissertation features a selection of referenced quotations from academic research articles to give context and a sense of perspective to the complexity and technical jargon of Computational Thinking, Computer Science and Computer Programming.
1.2 Definitions of Computational Thinking, Computer Science and Programming
Computational Thinking (University of Canterbury, 2022a): Computational Thinking encourages the use of critical thinking using concepts that relate to Computer Science. Computational Thinking skills are categorised into the fields of; Abstraction, AlgorithmicThinking, Automation, Decomposition, Debugging, Iteration, and Generalisation (Tang et al., 2020). Academic research literature shows that interpretations of Computational Thinking and technological definitions of the fields of Computational Thinking vary and are explained in Chapter 2.1, examples include; “Computational Thinking is understanding the principles of Computer Science that underlie all digital technologies” (Ministry of Education, 2022a) and “Computational Thinking is a thought processes involved in formulating problems and their solutions, so that the solutions are represented in a form that can be effectively carried out by an information processing agent” (Wing, 2006).
Computer Science (University of Canterbury, 2022e): Computer Science is the studyof computers and computer concepts, their systems, design, development and use(University of Auckland, 2022). Computer Science is characterised into the fields of; HumanComputer Interaction, Computer Graphics, Coding Introduction, Compression Coding,Encryption Coding, Error Control Coding, Software Engineering, Algorithms, Artificial Intelligence, Programming Languages, Formal Languages, Computer Vision, Data Representation, Network Protocols, Complexity and Tractability.
Computer Programming Alan Turing (The Alan Turing Institute, n.d.), recognised asthe Father of Computer Science and Artificial Intelligence, defined Programming in 1950 as; “Constructing instruction tables is usually described as ‘programming’. To ‘programme a machine to carry out the operation A’ means to put the appropriate instruction table into the machine so that it will do A” (Turing, 1950). Programming is the process of taking a solution to a problem (i.e. an Algorithm that solves a particular problem) & putting it into an unambiguous form (lines of code), so it can be executed by a computer (digital outcome). There is a general misconception that Computer Programming is Coding and vica-versa. “Coding and programming are often used interchangeably to indicate the process of ‘writing’ instructions for a computer to execute. However, programming refers to the broader activity of analysing a problem, designing a solution and implementing it. Coding is the stage of implementing solutions in a particular programming language. Implementation skills go beyond coding since they include debugging and testing” (Bocconi et al., 2016; Duncan et al., 2014).
These images demonstrate the relationship between Computational Thinking, Computer Science and Computer Programming (Coding). Fig 1 is the conceptual framework of CS Unplugged learning Computer Science, (Tim Bell). Fig 2 is relation between coding and Computational Thinking, Google for Education.
1.3 Rationale for this study
Computational Thinking education in New Zealand schools has the potential to “provide students with a broader view of the kinds of advanced topics they might study beyond High School” (Bell et al., 2010). There are very few research papers that focus on the opportunities and impacts of Computational Thinking, Computer Science and Computer Programming in a New Zealand school context, especially the academic achievement of senior students. Accordingly, comparative research shows that the New Zealand Computational Digital Technologies | Hangarau Matihiko curriculum is aligned with International studies that suggest Computational Thinking in education has widespread adoption and substantial industry support (Bell et al., 2009), emerging trends include collaboration and partnerships across sectors and national boundaries redefining digital competence and an emphasis on broadening access and interest (Y.-C. Hsu et al., 2019).
The rationale of this dissertation is to advance academic research by studying the intricate relationship between Computational Thinking, Computer Science, and Computer Programming in a school context with a lens on Māori and Pasifika students in New Zealand. Computational Thinking in education faces unresolved issues and challenges (Bocconi et al., 2016) which are discussed in greater detail in Chapter 5, Teaching Computational Thinking and Computer Science.
The rationale of this dissertation is to link academic research to the current situation of digital technology education in New Zealand schools to set a benchmark for future research.
The New Zealand Digital Skills Forum ‘Digital Skills Effort Survey 2021’ (New Zealand Tech Alliance, 2021) is referenced in this study as it addresses and raises significant issues and opportunities. “For the first time ever, data from the survey has been aggregated across the entire digital skills pipeline, from school to tertiary education, from education to employment, from within the market and from immigration. We find decreasing participation in technology in education and a less diverse workforce. System wide challenges require urgent national attention. Industries report dramatic skills challenges driving a heavy reliance on immigration, while under investing in the development of its own workforce” (New Zealand Digital Skills Forum, 2021).
1.4 Gaps in the literature
Computational Thinking has been integrated into the New Zealand Curriculum Technology learning area (Ministry of Education, 2022a) to strengthen the existing Computer Science and Programming curriculum and new learning Progress Outcomes have been integrated into the New Zealand Curriculum from years 1 to 13. Other than some research by Bell et al (Bell et al., 2010), there is minimal research concerning the acceptance, readiness and impact of the new New Zealand Curriculum Learning Progress outcomes 1 to 8 whichdescribe the significant learning steps that students take as they develop their expertise in Computational Thinking in the Digital Technology curriculum.
Some New Zealand academic articles (Arora, 2019; Bell et al., 2009; Bell & Vahrenhold, 2018; Delal & Oner, 2020), relate to pilot schemes using CS Unplugged resources with a focus on primary school children and professional development of their teachers but there is minimal academic research into how Computational Thinking is being integrated into intermediate and secondary schools or how teachers at these schools are participating in their professional development of improving their Computational Thinking, Computer Science and Computer Programming skills.
There is no New Zealand academic research on how schools are planning to or are adapting to integrate Computational Thinking into their curriculums.
New Zealand does not feature in the top 10 countries for academic research (Tang et al., 2020) into Computational Thinking or Computer Science despite being a leading country who has a comprehensive education system with a progressive strategy for integrating these learning areas in schools and the new Computational Thinking compulsory curriculum.
There are approximately 200,000 Māori and approximately 80,000 Pasifika students enrolled in primary and secondary schools in New Zealand (Ministry of Education, 2021c, 2021d). Research shows only 30 percent student participation in technology curriculum education in secondary schools, 14 percent Māori and 9 percent Pacific peoples. Academic results in Computer Science assessment show a very low rate of achievement, especially in decile 1-3 schools. In the technology workforce 27 percent of employees are women, 4 percent Māori and 2.8 percent Pacific peoples (New Zealand Digital Skills Forum, 2021). Research into Computational Thinking education for Māori and Pasifika students is minimal and there is a lack of research on how teachers can undertake professional development to focus on the specific cultural needs of these students. This dissertation seeks to advance the discourse research of Māori and Pasifika students' digital technology education.
This dissertation aims to highlight some of the issues and opportunities for future research of Computational Thinking in education.
1.5 Purpose and significance of this study
This study recognises that Computational Thinking in schools is at a nascent stage which is evolving with best practice pedagogy, collaboration with private sector industries and ongoing support from the Ministry of Education. This study aims to provide a more comprehensive analysis of Computational Thinking in education and to identify what questions remain unanswered by research into Computational Thinking in a New Zealand context especially relating to Māori and Pasifika. This study examines current findings concerning the integration of Computational Thinking into the existing Digital Technologies | Hangarau Matihiko curriculum, international observations, challenges and strategies in teaching Computational Thinking and Computer Science and teachers professional development.
1.6 Research Question
The mandate requiring all schools from Years 1 to 13 adopt the new compulsory Digital Technologies | Hangarau Matihiko curriculum so that all students will get to experience Computational Thinking pedagogy, Data Representation, Algorithm and Programming curriculum subjects is significant. This dissertation review of existing academic literature is guided by the opportunity to analyse this current iteration of Digital Technology education in New Zealand by asking;
● What is Computational Thinking, Computer Science and Computer Programming in a New Zealand education context with a lens on Māori and Pasifika students?
1.7 Dissertation outline
This dissertation is presented in seven chapters. Chapter 1 introduces and identifies the proposed scope of the study. Chapter 2 looks at Computational Thinking, Computer Science and Computer Programming. Chapter 3 looks at Computational Thinking in education from an international and New Zealand perspective. Chapter 4 is an overview of Digital Technology in New Zealand education, the Digital Technologies | Hangarau Matihiko curriculum and Māori and Pasifika students. Chapter 5 reviews the challenges, strategies and teachers professional development. Chapter 6 states the methodology of this dissertation. Chapter 7 discusses these findings by presenting conclusions and recommendations for future research.
Chapter 2 - Computational Thinking, Computer Science, Programming
This chapter looks at the historical use of computing in education and the existing Computer Science and Computer Programming (coding) curriculum to give context to how the new Computational Thinking curriculum integrates, strengthens and enriches the existing pedagogy of computing in New Zealand schools.
2.1 Computational Thinking
Computational Thinking encourages the use of critical thinking using concepts that relate to Computer Science. Computational Thinking skills are categorised into the fields of; Abstraction, Algorithmic Thinking, Automation, Decomposition, Debugging, Iteration, and Generalisation (Tang et al., 2020).
The idea of Computational Thinking dates back to the 1950s and has been in debate since then (Tedre & Denning, 2016). International research shows that descriptions of Computational Thinking differ, for example Finland and Norway call Computational Thinking ‘Algorithmic Thinking’, Estonia calls it ‘Technological Literacy’, and Poland calls it ‘Informatics Education’ although all these names relate to Computational Thinking and Computer Science education (Bocconi et al., 2018). Jeanette Wing’s influential 2006 article on Computational Thinking has become the most highly cited academic article on Computational Thinking (Tang et al., 2020; Wing, 2006). Wing described Computational Thinking as “the thought processes involved in formulating lems and their solutions, sothat the solutions are represented in a form that can be effectively carried out by an information-processing agent” (Wing, 2006).
However, the European Commission’s Joint Research Centre policy report ‘Developing Computational Thinking in Compulsory Education’ (Bocconi et al., 2016) concluded that “There is a lack of consensus on the definition of Wing’s landmark definition as a reference point for discussion in the field, providing two valuable perspectives: (i) Computational Thinking is a thought process, thus independent of technology; (ii) Computational Thinking is a specific type of problem solving that entails distinct abilities, e.g. being able to design solutions that can be executed by a computer, human, or a combination of both”.
One of the objectives of this dissertation is to investigate the emerging status of Computational Thinking in schools and give greater exposure to the intricacies of the subject. Research shows that technological definitions of the fields of Computational Thinking vary between academics.
The European Commission’s report (Bocconi et al., 2016) seeks to give clarity of Computational Thinking terminology by defining each of the fields as;
Abstraction: “The most important and high-level thought process in one of the most high-level thought processes of Computational Thinking is the Abstraction process. The process of making an artefact more understandable through reducing the unnecessary detail. The skill in abstraction is in choosing the right detail to hide so that the problem becomes easier, without losing anything that is important. A key part of it is in choosing a good representation of a system. Different representations make different things easy to do. An algorithm is an abstraction of a process that takes inputs, executes a sequence of steps, and produces outputs to satisfy a desired goal. Computing is the automation of our abstractions. Computational Thinking is using abstraction and decomposition when attacking a large complex task or designing a large complex system. Abstraction is used in defining patterns, generalising from instances, and parameterisation which is Generalisation”.
Algorithmic Thinking: “A way of getting to a solution through a clear definition of the steps”.
Automation: “A labour saving process in which a computer is instructed to execute a set of repetitive tasks quickly and efficiently compared to the processing power of a human. In this light, computer programs are automations of abstractions”.
Decomposition: “A way of thinking about artefacts in terms of their component parts. The parts can then be understood, solved, developed and evaluated separately. This makes complex problems easier to solve, novel situations better understood and large systems easier to design”.
Debugging: “The systematic application of analysis and evaluation using skills such as testing, tracing, and logical thinking to predict and verify outcomes”.
Generalisation: “Associated with identifying patterns, similarities and connections, and exploiting those features, it is a way of quickly solving new problems based on previous solutions to problems, and building on prior experience. Asking questions such as ‘Is this similar to a problem I’ve already solved?’ and ‘How is it different?’ are important here, as is the process of recognising patterns both in the data being used and the processes/strategies being used. Algorithms that solve some specific problems can be adapted to solve a whole class of similar problems”.
Conversely to the individual definitions of the European Union, Professor Tim Bell (University of Canterbury, 2022d) who is one of New Zealand’s most accomplished Computer Scientists and co-creator of CS Unplugged describes the relationship between Computational Thinking and Computer Science and Computer Programming in a more abstract perception;
“A strange connection where Computational Thinking (and the closely related field of Computer Science) are not particularly about programming, yet programming can be a key focus for Computational Thinking. While Computational Thinking isn’t directly about programming, when you write a program it provides a thorough test of your Computational Thinking, the computer is completely unforgiving and will follow your set of instructions exactly, so students receive instant feedback if their Computational Thinking is sound. Computational Thinking isn’t about thinking like a computer; it’s about getting control over digital devices by understanding them. This requires a higher order of thinking and reasoning than a computer can do, and a different kind of reasoning to what we are used to in the physical world. Computational Thinking gets students to look behind the screen at what is really happening, and empowers them to know that they can influence it, and even create things behind the screen for themselves. In the same way that students need to understand some science to form a view on climate change, or they need to understand social and cultural issues to form a view on politics and conflicts, they need to know some basics of the concepts underlying digital technologies to make reasonable decisions about the digital systems that they interact with” (Ministry of Education, 2017).
Historically, schools that offered Programming meant students would learn to code without learning the overarching concepts of Computational Thinking or Computer Science.
“Text-based programming is a traditional way to type various characters from a syntax, however it is rather passive and inaccessible to general students. Approaches to learning that predominantly rely on computer devices fail to promote advanced computer concepts necessary for programming” (Threekunprapa & Yasri, 2020). Some research suggests negative attitudes towards computer education (Delal & Oner, 2020). The term Computational Thinking was first used by Seymor Papert in 1980’s (Papert, 1980). Jeannette Wing put this term in front of the computer science community, thereby giving everyone a glimpse of the importance of computational thinking and its role as an integral part of education. Wing further added that computational thinking is a universal skill set for everyone, not necessarily only for computer scientists (Arora, 2019).
Contemporary educational pedagogy of the New Zealand Digital Technologies | Hangarau Matihiko curriculum now integrates the overarching principles of Computational Thinking and Computer Science into learning to strengthen the practice of Computer Programming.
2.2 Computer Science
“Many of the key ideas in Computer Science existed before computers did; for example, the main logic that is the basis of all digital computers is Boolean algebra, developed by George Boole (1815-1864). The word Algorithm is derived from the name of a Persian mathematician, Muḥammad ibn Mūsā alKhwārizmī (780-850AD)”. (Ministry of Education, 2017). The first published definition of Computer Science was in 1967 “The study of computers and all the phenomena surrounding them” (Perlis/Simon/Newell) (Guzdial, 2021).
Computer Science is the study of computers and computer concepts, their systems, design, development and use (University of Auckland, 2022). Computer Science is characterised into the fields of; Human Computer Interaction, Computer Graphics, Coding Introduction,Compression Coding, Encryption Coding, Error Control Coding, Software Engineering, Algorithms, Artificial Intelligence, Programming Languages, Formal Languages, Computer Vision, Data Representation, Network Protocols, Complexity and Tractability. Computer Science careers include; Computer Scientist, Software Developer, Website and Mobile App Developer, IT Solution and Infrastructure Architect, Systems Administrator, Network Engineer, Hardware Engineer, Database Administrator, Security Engineer, 2D/3D Modeller and Animator, Visual Effects and Graphic Designer, Multi-Media Artist, Games Programmer and Hacker.
In a school context, Computer Science education promotes logical thinking, problem solving and abstract thought within the frameworks of; Digital Information (digital tools and systems for managing information), Digital Infrastructure (hardware and networks, including installing software), Digital Media (video, audio, layout/design, web, graphics, animation, games, web), Electronics (electronic and embedded systems), and Programming and Computer Science (concepts from Computer Science and Software Engineering, designing and implementing programs). Historically New Zealand students at years 11, 12 and 13 had the option to create digital technology outcomes to demonstrate their understanding of Computer Science for a variety of New Zealand Qualifications Authority or Cambridge International Education assessments. However, the New Zealand Ministry of Education’s mandate that by 2020 all schools from Years 1 to 13 adopt the new Digital Technologies | Hangarau Matihiko curriculum, which includes a new Computational Thinking pedagogy, is a significant shift from the existing Digital Technology curriculum being optional for Years 11 to 13. “Computing in school curricula is often diluted because it has to cover three quite different directions: (1) using computers as a tool for teaching (e.g. e-learning), (2) using computers as a tool for general purpose applications (sometimes called ICT), and (3) computing as a discipline in itsown right (including programming and CS). Sometimes administrators and leaders confuse these roles, and this can make it difficult for Computer Science to be visible as a discipline in its own right. Many of the difficulties implementing effective computing curricula are common to a number of countries, and the New Zealand experience has reflected the experience of others" (Bell et al., 2010).
2.3 Computer Programming and Coding
Computer Programming is often confused with Coding, although they have similar meanings they are not the same thing, Coding is a subset of Programming. Computer Programming “is a method of designing an end to end software or product that adheres to particular guidelines and accomplishes a certain purpose” (Scaler Academy, 2021). Coding is writing a set of instructions in a programming language like Python or Java and inputting it into a computer so it can perform a task. The New Zealand Qualifications Authority Programming assessments require students to learn programming languages to write lines of code to create a digital technology outcome. “Coding is explicitly regarded as a key 21st century skill: "Coding is the literacy of today and it helps practice 21st century skills such as problem-solving, teamwork and analytical thinking" (Bocconi et al., 2016). This image is an example of code written in Python that will convert any PDF into an Audiobook (Nyakundi, 2021).
There are two categories of Programming languages; High Level and Low level. High level languages like Python, C, C++, C# Java, JavaScript, Visual Basic, PHP, Perl, Kotlin, Julia Ruby, Swift, Dart and Scarla are programmer friendly with easy to understand syntax (specific sets of written information defined by the structure of the language that instructs computing devices to perform tasks), are simple to debug, can run on any platform and are widely used in the industry. Low level languages are machine dependent, tough to understand, complex to debug, complex to maintain, need an assembler for translation and are not commonly used but have advantages like being able to run programmes quickly with a low computer memory footprint. All programming languages have different syntax coding attributes for developing various digital technology outcomes like websites, mobile apps, 3D virtual and augmented reality, databases and cloud platform services.
There are approximately fifty (TIOBE, n.d.) popular high level programming languages and an estimated 8945 programming languages (HOPL, n.d.). Examples of the programming languages that multinational technology companies use are; Apple (Swift), Google Android (Java), Google Search (Python), Linux (C), Microsoft Windows (C and Visual Basic), Unity (C#) and Unreal Engine (C++). Python is one of the most popular programming languages for beginner coders due to its short learning curve and straightforward object-oriented syntax which can run on multiple operating systems like iOS, Windows and Linux. Research shows it's better for two students to peer-assist learn together when learning to code (Altintas et al., 2016). A benefit of Python’s open-source platform is that it features an extensive collection of built-in libraries of code packages so you don't have to write individual code, you can index library extensions. It’s estimated that Google runs on two billions lines of code (Metz, 2015), whereas an iPhone or Android app is about 500 lines of code.
Young children ages 5-7 can learn the basics of coding using ScratchJr (MIT Media Lab et al., n.d.) which is based on a Visual Programming language, a drag-&-drop block-based coding application. “Data reveals that countries with robust Computer Science initiatives such as the UK and the Nordic countries have high usage of ScratchJr” (Bers, 2018). Scratch (MIT Media Lab, n.d.) designed for 8-16 year olds is the world's largest free block-based coding community. Alice (Carnegie Mellon University, n.d.) is also a popular block-based coding platform. International Computational Thinking academic literature research (Tang et al., 2020) from 2006-2018 has found that the third most popular citation based academic research article is ‘Scratch: programming for all’, Resnick et al (2009) (Resnick et al., 2009) which introduces a game based programming tool that facilitates individuals to access coding activities. Game construction involving both design and programming activities can enhance students’ learning of Computer Science concepts, Denner et al. (2012) (Denner et al., 2012). Game design can facilitate Computational Thinking cultivation, enabling students to solve problems in real life, and empower them to apply educational knowledge into the practical world, (Denner et al., 2012). “Through programming practices, students learn to generalise abstractions, process information and detect errors systematically, compose and decompose problems structurally, and think in iterative, recursive, and parallel ways” (Grover & Pea,2013; Tang et al., 2020).
This image is a screenshot of block-based programming in Scratch (MIT Media Lab, n.d.).
Minecraft also features block-based programming that auto creates lines of JavaScript text code so you can switch between and compare code instructions of the visual based blocks and JavaScript text editor programming using Microsoft’s MakeCode platform (Microsoft, n.d.). In 2014, Alfriston College pioneered the use of Minecraft for the New Zealand Qualifications Authority Computer Programming assessments using MakeCode, JavaScript and Code Connection (Alfriston College, 2021). This image is a screenshot of a block-based programming project showing the block and JavaScript code in Minecraft (Williams, 2019).
Generally, the first lines of code you learn to write in any language is ‘Hello World!’ This image below shows the variety and complexity of coding languages and their syntax to produce the same result of writing Hello World!
Chapter 3 - Computational Thinking in Education
3.1 Computational Thinking in International Education
To gain an international perspective of Computational Thinking in Education, Tang etal. used a systematic keyword search including “computational thinking, computer education, computer science education, computer literacy, abstraction, decomposition, programming, computer program, coding thinking, algorithm thinking, computing intelligence”, and a citation-based relevancy test to secure research data to analyse academic literature content research trends from 2006-2018 (Tang et al., 2020). The ten most productive countries of Computational Thinking publications were USA (255), Taiwan (55), Spain (49), Turkey (43), UK (33), China (29), Greece (23), Australia (19), Netherlands (15), Canada (14). Analysis of results reveals that “there is a lack of teacher training for Computational Thinking, this indicates that fostering Computational Thinking is still a challenge due to only a few teachers being trained with the knowledge and skills to integrate Computational Thinking into course curricula” (Tang et al., 2020). Conclusions highlight that “pedagogy using games, peer/collaboration approaches, and task-based learning environments were found. This higher-order thinking cultivation environment can foster students’ computational practices with information processing, scaffolding, and reflection activities. Moreover, Scratch, Lego, Alice, and Python were found to be emerging programming language/ tools, highlighting the use of game-programming tools as a means of helping learners develop the skills to face real-life problems” (Tang et al., 2020).
“Since 2006, there has been an ever-increasing momentum in Computational Thinking educational policy initiatives across the globe” (Y.-C. Hsu et al., 2019). Computational Thinking educational policy initiatives were viewed through the lens of international perspectives and cultural contexts were examined, the trends that emerged were; “collaboration and partnerships across sectors and national boundaries, rationales that take a broad perspective and refer to common themes, a redefinition of digital competence, and an emphasis on broadening access and interest” (Y.-C. Hsu et al., 2019).
Most countries have or are reforming their curriculums to include Computational Thinking (Y.-C. Hsu et al., 2019). For example; India’s ‘CSpathshala’ (Association for Computer Machinery, n.d.) and Canada’s ‘CanCode’ (Innovation, Science, and Economic Development Canada, 2019). In 2014 Switzerland introduced Lehrplan 21 (German-Swiss Educational Directors, n.d.) Computer Science education and Japan’s new Computational Thinking and Programming curriculum which was integrated into primary school Maths and Science curriculums in 2016 and comes into effect in secondary schools in 2022 (Bocconi et al., 2018). In the United States of America, there is no federal organisation that guides the curriculum of Computer Science education in schools (Bell et al., 2010). In Great Britain, new computing programs of study were introduced into the National Curriculum in 2014. “Learning the skills of computer programming has grown from a minority concern among computing educators, grassroots computing organisations and computer scientists into a major curriculum reform discourse in England. Learning to code has become part of a major reform agenda in education policy in England. It also examines how the pedagogies of learning to code are intended to inculcate young people into the material practices and systems of thought associated with computer coding, and to contribute to new forms of digital governance". (Williamson, 2016).
Research from European countries conclude that “In accordance with the international growing trend, the teaching of coding is becoming an increasingly important focus in European education. European usage trends are in alignment with the rest of the world in terms of coding patterns” (Bers, 2018). The European Commission’s CompuThink 2015 study overview of research and policy initiatives “discusses the most significant Computational Thinking developments for compulsory education in Europe and provides a comprehensive synthesis of evidence, including implications for policy and practice”. It concludes that “despite this widespread interest, successful Computational Thinking integration in compulsory education still faces unresolved issues and challenges that still needs to be addressed for the effective integration of Computational Thinking in compulsory education" (EU Science Hub, 2016).
This significant international research concludes that there are four important areas for policy makers and stakeholders to focus on: “consolidated Computational Thinking understanding; comprehensive integration; systemic rollout; and policy support” (Bocconi et al., 2016). These key issues are congruent with the Computational Thinking initiatives that the New Zealand Ministry of Education have implemented in the new Digital Technologies | Hangarau Matihiko curriculum.
3.2 Computational Thinking in New Zealand Education
The New Zealand Government's ‘digital skills for a digital nation’ vision is for all New Zealanders to thrive in a digital age. Their underlying rationale is that “A workforce trained in computational problem solving spells efficiency, economic benefit, and even further advances to technology. Students can demonstrate they are genuine Computational thinkers by planning and constructing programs” (Ministry of Education, 2017). This contributes to further advances in technology, research and innovation to drive productivity and increase wages.
The ongoing work to revise the technology learning area to strengthen digital technologies in the New Zealand Curriculum culminated in the Ministry of Education’s 2016 directive that by 2020 all schools from Years 1 to 13 adopt the new compulsory Digital Technologies | Hangarau Matihiko curriculum, which is a significant shift from the previous policy of the Digital Technology curriculum being optional for Years 11 to 13 students. The intent is that all school students will experience the recently defined Learning Outcomes for the new Data Representation, Algorithm and Programming curriculum subjects. ‘Computational Thinking for digital technologies’ and ‘Designing and developing digital outcomes’ are the key conceptual areas that were introduced into the Digital Technologies | Hangarau Matihiko curriculum and were designed to integrate with the existing technology learning area achievement objectives of Computer Science and Programming. “The two proposed learning progressions for digital technologies are structured to ensure that once students complete year 10 they will be ready, with good teaching, to be successful in all of the NCEA achievement standards in digital technologies. In the Computational Thinking progression, this means students will have developed a base of skills and knowledge in three key areas: Data Representation, Algorithms, and Programming” (Ministry of Education, 2017).
In order to appraise students understanding of Computational Thinking and Computer Science based on the Digital Technologies | Hangarau Matihiko curriculum, the New Zealand Qualifications Authority administers assessments which include Data Representation and Algorithm areas of Computational Thinking. These map to the Achievement Standards listed below that develop students' knowledge of Computer Science which are academically assessed in Years 11 to 13, Levels 1, 2 and 3.
● Demonstrate understanding of basic concepts from Computer Science (L1, AS91074)
● Demonstrate understanding of advanced concepts from Computer Science (L2, AS91371)
● Demonstrate understanding of areas of Computer Science (L3, AS91636)
The Computer Programming area of the Digital Technologies | Hangarau Matihiko curriculum maps to the Achievement Standards listed below that develop students’ ability to plan and construct computer programs:
● Construct a plan for a basic computer program for a specified task (L1, AS91075)
● Construct a basic computer program for a specified task (L1, AS91076)
● Construct a plan for an advanced computer program for a specified task (L2, AS91372)
● Construct an advanced computer program for a specified task (L2, AS91373)
● Develop a complex computer program for a specified task (L3, AS91637)
Data from the 2018-2020 Digital Technologies | Hangarau Matihiko Computer Programming assessments show a low rate of achievement for Māori and Pasifika students, especially at decile 1-3 schools compared to national statistics (Ministry of Education, 2021). For example, AS91883 is a Level 1 assessment to ‘Develop a computer program’ (NZQA, 2020). The telling statistic is the total number of students who did this assessment at decile 1-3 and their comparative statistical differential of Not Achieved, Merit and Excellence results.
This image is the 2018-2020 national results for all male and female students of all nationalities, deciles 1-10, who did this assessment.
This image is the 2018-2020 national results for all Māori nationality male and female students, deciles 1-3, who did this assessment.
These are the 2018-2020 national results for all Pasifika nationalities male and female students, decile 1-3, who did this assessment.
Chapter 4 - Digital Technology in New Zealand Education
4.1 A history of computing in New Zealand education
Human capacity for computation using devices has a long history, from the Abacus counting tool (500 BC) to Archimeds’ Antikythera mechanism (250 BC) to Charles Babbage’s Analytical Engine (1822) and Ada Lovelace’s first algorithmic computer program (1842) to Alan Turing's Universal Machine (1936) to large mainframe computers (1960s) through to personal computing (1980s). In the past decade devices like mobile phones, iPads and similar tablets, Chromebooks and the Internet changed the nature of education by enabling mass adoption of eLearning and the sharing of knowledge as a ‘global classroom’ (Campbell, 2004) which are the catalysts for an enriched technologies curriculum in New Zealand that aligns with global trends in digital technology education.
The historical discipline of computing taught in some New Zealand schools started in the mid 1970’s as ‘Applied Maths’ (Bell et al., 2010) and was based on the theory of computing with hand written binary algorithm coding and punched-card coding sheet technology. On 1 April 1984 the Department of Education established the Computers in Education Development Unit (CEDU) to provide training and direction for educational computing in schools (Campbell, 2004). In the mid 1980’s some schools had access to personal computing devices like Commodore, Sinclair Spectrum and BBC Micro computers which were used as writing tools (electronic typewriters) and for programming (coding) using the BASIC programming language.
The advancement of computer technology in the late 1980’s ment some early adapting schools and students were using email (Starnet electronic mail) and by the early 1990’s the development of the Internet started to provide access to the ‘global classroom’ and ‘eLearning’ (Campbell, 2004). “Perhaps computer mediated communication is at last coming of age as educators themselves learn that it has a significant role to play in student learning, that it is not an end in itself but a means to an end. At last the technology has respectability” (Chapple, 1992).
In 1993 the Ministry of Education released the Technology in the New Zealand Curriculum paper for consultation, in 1999 the New Zealand Curriculum was officially released that included the term ‘Information and Communication Technology (ICT) which signified the importance of digital technology tools to promote and enhance eLearning and formally integrated ICT into the national curriculum. “There was no centralised scheme to facilitate widespread dumping of large numbers of computers into classrooms and suites, leaving teachers numb with anticipation. There was no clear political or economic expediency to drive the change toward the implementation of distance modes of teaching and eLearning. The result was a situation where there were steadily increasing numbers of students and teachers in New Zealand classrooms becoming excited about what eLearning could offer them” (Campbell, 2004).
In 2011, Computer Programming was introduced as a formal assessment optional subject for senior students. “Programming which is a traditional way to type various characters from a syntax. However, it is rather passive and inaccessible to general students” (Threekunprapa & Yasri, 2020).
In 2017 Ministry of Education incorporated the new Digital Technologies | Hangarau Matihiko (Ministry of Education, 2021a) strand into the New Zealand Curriculum (Ministry of Education, 2022c) and added new Computational Thinking pedagogy into the existing Digital Technologies curriculum which already covered Computer Science and Computer Programming (coding). “In December 2017, the technology learning area was revised to strengthen digital technologies in The New Zealand Curriculum. The goal of this is to ensure all students have the opportunity to become digitally capable individuals” (Ministry of Education, 2022d).
By 2020 all schools were mandated to incorporate Digital Technologies | Hangarau Matihiko and Computational Thinking into their curriculum for years 1 to 13, a shift from the Digital Technology programming curriculum being optional for years 11 to 13. Anecdotal evidence suggests this has yet to eventuate.
The new New Zealand Curriculum Technology learning strands include (1) Computational Thinking for digital technologies and (2) Designing and developing digital outcomes by learning how to design quality, fit-for-purpose digital solutions (Ministry of Education, 2022a). Six core learning areas were defined as: Algorithms, Programming, Data Representation, Digital Devices and Infrastructure, Digital Applications, and Humans and Computers.
4.2 The Digital Technologies | Hangarau Matihiko curriculum
Computational Thinking was integrated into the New Zealand Curriculum Technology learning area to strengthen the existing Computer Science and Computer Programming curriculum (Ministry of Education, 2022a). Learning Progress Outcomes were added to the curriculum which describe the significant learning steps that students take as they develop their expertise in Computational Thinking for digital technologies from year 1 to 13. One of the New Zealand Curriculum key competencies is for students to be competent users of technologies in a range of contexts. In the Technology learning area, the intent is that students learn to be innovative developers of products and systems, effective pedagogy should stimulate the curiosity of students to apply what they discover in new contexts or in new ways. The teaching as enquiry eLearning pedagogy is to enhance opportunities to learn by offering students virtual experiences and ICT that can supplement traditional ways of teaching and open up new and different ways of learning. Students should make enterprising use of knowledge, skills and practices for exploration and communication, which includes computer programming and software development. New compulsory learning Progress Outcomes have been integrated into the New Zealand Curriculum from years 1 to 13.
Y1-9 Progress Outcomes: Students use their decomposition skills to break down simple non-computerised tasks into precise, unambiguous, step-by-step instructions (algorithmic thinking), identify any errors and correct them (simple debugging).
Y10 Progress Outcome: By the end of year 10, students can independently decompose a Computational problem into an algorithm that they use to create a program incorporating inputs, outputs, sequence, selection and iteration.
Y11-13 Progress Outcome: By the end of year 13, students who have specialised in digital technologies can understand how areas of Computer Science such as network communication protocols and artificial intelligence are underpinned by algorithms, data representation and programming, and they analyse how these are synthesised in real world applications. They use accepted software engineering methodologies to design, develop, document and test complex computer programs.
For Year 11-13 students, the New Zealand Digital Skills Forum survey evidence shows low rates (30%) of technology curriculum education in secondary schools so there are considerable opportunities for enhancing education with digital technology. The Progress Outcomes are designed to improve students' Computational Thinking skills and that students in Years 11-13 can effectively complete the New Zealand Qualifications Authority (NZQA) assessments to demonstrate their knowledge of Computer Science.
These are the skills required to complete the NZQA Computer System Assessment (L1) AS91882; Configuring hardware, software and peripherals, testing procedures, diagnosing and troubleshooting installation and configuration faults, investigating hardware and software of computer systems, improve the quality of the computer systems, explaining purpose and function of hardware and software, installing and configuring hardware and software.
These are the skills required to complete the NZQA Computer Programme assessments (L1) AS91883, (L2) AS91896, (L3) AS91906; Writing code for a program that performs a specified task, use complex techniques in a suitable programming language, code clearly and code comment, use variables and comments that describe code function and behaviour, follow common coding conventions, testing and debugging the program effectively to ensure that it works on a sample of both expected cases and relevant boundary cases, programs are well-structured, logical response to the task, making the program flexible and robust, comprehensively testing and debugging.
These are the skills required to complete the NZQA Computer Science assessments (L2) AS91898, (L3) AS91908; Identifying and explaining Computer Science, how the concept is used or occurs and applied to address an opportunity, relevant algorithms and mechanisms that shape the concept, impact of the concept, key problems or issues related to the concept, technical capabilities and limitations of the area relate to humans, comparing and contrasting different perspectives on the area.
In 2014, the Ministry of Education first signalled that Computational Thinking and Learning Progress Outcomes would be integrated into the New Zealand Curriculum and facilitated a national series of workshops, professional development and other supportive learning opportunities to help schools and teachers integrate these topics into their curriculums of learning by 2020.
In the past two years the Covid19 pandemic has been a factor in the anecdotal evidence that schools have not had the full opportunity to implement Computational Thinking and Learning Progress Outcomes into their programmes of learning. Currently, there is no published academic research that relates to the experiences and student achievement of these planned Computational Thinking curriculum and Learning Progress Outcomes.
4.3 Maori and Pasifika Students
There are approximately 200,000 Māori students and approximately 80,000 Pasifika students of Samoan, Tongan, Cook Island, Fijian, Niuean, Tokelauan and other Pacific Island cultures enrolled in primary and secondary schools in New Zealand (Ministry of Education, 2021d, 2021e).
There is minimal academic research about Māori or Pasifika students learning Computational Thinking or Computer Science in schools. Margaret Wilkie’s book ‘Te Timata - The first step to Māori succeeding in higher education’ describes a “silence of the archives’, the lack of information about Māori succeeding in higher education, particularly from a Māori world view” (Wilkie, 2014). At a tertiary education level the “Literature is very sparse on indigenous students who have successfully completed a computer degree, a discipline which is predominantly seen as non-Māori orientated” (Rakena & Fisher, 2010).
Hangarau (technology) Matihiko (digital) is an approach where Māori practices and knowledge reinforces the kaupapa of understanding how Māori students are able to build on their digital capability and enhance their Computational Thinking. “The lens of kaupapa Māori principles of tino rangatiratanga (relative autonomy/self-determination), taonga tuku iho (cultural aspirations), ako (reciprocal learning), kia piki ake i nga raruraru o te kainga (mediation of socio-economic and home difficulties), whānau (family) and kaupapa (collective vision, philosophy) provide opportunities to change power relationships in classrooms and schooling through a range of approaches” (Y.-C. Hsu et al., 2019).
The New Zealand Digital Skills Forum Digital Skills Effort Survey 2021 highlights Māori and Pasifika. ”For Year 11-13, survey evidence shows low rates (30%) of student participation in technology curriculum education in secondary schools. Only 39 percent of technology standards participants being girls, 14 percent Māori and 9 percent Pacific peoples. This flows through all levels of study and into the workforce where only 27 percent of digital technology employees are women, 4 percent Māori and 2.8 percent Pacific peoples” (New Zealand Digital Skills Forum, 2021).
“Pacific students are still experiencing significant disparities in achievement. In the past, this underachievement of Pasifika students was often attributed to a lack of proficiency in English combined with differing cultural norms. Sometimes a student’s first language was regarded as "interfering" with the learning of a second language, a concept known as a subtractive view of bilingualism. In addition, deficit thinking about Pasifika students' bilingualism often affected teacher expectations” (Ministry of Education, 2022b).
Mohaghegh & McCauley’s ‘Computational Thinking: The Skill Set of the 21st Century’ (Mohaghegh & McCauley, 2016) research article observes that "Traditionally, Computer Science has not been as popular an area for Māori students as others such as literature, design and arts. In terms of Computational Thinking, there is currently no major work involving, or of significant influence to Māori. However, the need to promote technology and Computer Science to Māori has been identified by a number of groups. While there is evidence of early steps being taken to introduce Computational Thinking and problem solving skills in Kaupapa Māori, there is still much room for improvement. Certain groups are taking great steps in the development in this area – such as White (2015) – however this needs to be scaled to a greater degree, with a greater awareness of the value of Computational Thinking and problem solving” (Mohaghegh & McCauley, 2016).
Dr Te Taka Keegan, the University of Waikato computer scientist who in 2017 won the Prime Minister’s Supreme Award for Excellence in Tertiary Teaching, uses a Māori teaching philosophy and is the only person known to have taught a Computer Science paper completely in te reo Māori (University of Waikato, 2022a). “I teach using kaupapa Māori methods, even though the students probably aren’t aware of this. My teaching philosophy is based around important Māori principles, including kia hiki te wairua (lifting the spirits), kia hihiko te kaupapa (inciting the passion) and kia hora te aroha (sharing the love).” (Careers with STEM, 2018).
Dr Keegan’s research article, Hangarau mete Māori: Māori and Technology (2018), explores a wide variety of Māori use of technology “Māori have a long, but mostly unrecognised, history of ingenious innovation and adoption of new technologies. This has shown through both historical Māori and modern Māori examples, the innovativeness and rapidity with which Māori have created and adopted new technologies. At the present time the impact has been minimal because we are yet to see a full adoption of te reo Māori in modern technology” (Keegan & Sciascia, 2018). In this article there was no reference to Computer Science or Computational Thinking.
There are many industry initiatives, educational programmes and website resources that support Māori and Pasifika in Computer Science and Computer Programming, examples include; Combining Māori and Digital worlds (Education Gazette, 2020), Finding links between coding and Te Reo (Brown, 2016), Kiwi students learn to code Te Reo Māori (Microsoft NZ News Centre, 2018), Individuals who made significant contributions to Māori ICT (Taiuru, 2017), Growing Māori leaders in ICT (Ministry of Māori Development, 2019), Atea (Science for Technological Innovation, 2020), Code Club Aotearoa (Code Club Aotearoa, n.d.), Devacademy (Dev Academy, 2021), OMG Tech (OMGTech!, 2022), the South Auckland STEAM Equity Collective (South Auckland STEAM Equity Collective, 2021) and the Pam Ferguson Charitable Trust (Pam Fergusson Charitable Trust, n.d.).
The following graphs compiled by the Ministry of Education represent the number of Māoriand Pasifika who enrolled in tertiary Computer Science in 2020.
Image shows fields of specialisation for Māori students in New Zealand 2020, 690 students enrolled in tertiary Computer Science. Statistics by the Ministry of Education (Ministry of Education, 2021b).
Image shows fields of specialisation for Pasifika students in New Zealand 2020, 510 students enrolled in tertiary Computer Science. Statistics by the Ministry of Education (Ministry of Education, 2021c).
Chapter 5 - Teaching Computational Thinking and Computer Science
5.1 Challenges and Opportunities
There is an international prevalence of Computational Thinking and Computer Science in compulsory education however in many cases teachers are ill prepared to teach these subjects (Harris, 2018).
Successful Computational Thinking integration in compulsory education faces unresolved issues and challenges (Bocconi et al., 2016). There is a mixed consensus over definitions of Computational Thinking (García-Peñalvo & Mendes, 2018), preconceived notions of computing that are difficult to overcome (Lamprou & Repenning, 2018), teachers have misconceptions with the complexity of terminology (Duncan et al., 2017), teachers are reluctant to teach it (Munasinghe et al., 2021), diversity of technical words, computer jargon, metaphors and phrases in different contexts can make their meanings confusing, ambiguous or misunderstood (Munasinghe et al., 2021), teachers are ill prepared to teach the subject and there is little best practice research for teacher professional development or allocated school time for professional development and a lack of teacher confidence and competence regarding Computer Science could harm student attitudes towards the subject later (Harris, 2018).
The effectiveness of teaching Computational Thinking has been underexplored, preventing efforts to cross the large gap between early adopters and the early majority, conceptualised as the Computer Science Education chasm (Lamprou & Repenning, 2018). Issues associated with integrating Computational Thinking into existing already congested timetables can be mitigated by incorporating Computational Thinking into Mathematics curriculums because “computational modelling is an effective approach for learning challenging science and maths concepts (Hambrusch, Hoffmann, Korb, Haugan, & Hosking, 2009). “Imaginative programming is the most crucial element of computing because it closely aligns mathematics with computing and, in this way, brings mathematics to life” (Felleisen & Krishnamurthi, 2009; García-Peñalvo & Mendes, 2018).
Challenges in teaching Computational Thinking extend to different interpretations of the terms, variety and complexity of teaching resources and approaches available to students in the classroom including “semantics rather the syntax of a specific language, those that prefer some kind of programming environment based on blocks such as Scratch or based on most traditional coding languages, those that control robots or those that build physical kits to control things” (García-Peñalvo & Mendes, 2018).
The international challenge in teaching Computational Thinking and Computer Science is the scope of support required to prepare teachers to teach these subjects, examples of which extends to more than 2 million primary teachers and 2.5 million secondary teachers in 28 EU countries (European Coding Initiative, n.d.), 60,000 primary school teachers in South Korea, 14,000 secondary school ICT teachers and 200,000 primary school teachers in England (Bocconi et al., 2016).
Switzerland’s Lehrplan21 national curriculum includes a mandatory Computer ScienceEducation course that includes basic understanding of Computer Programming that pre-service primary school teachers must pass. The data from this initiative suggest that “it is possible to teach programming to pre-service primary school teachers however, it is less clear how much or what kind of Computational Thinking is conveyed, here appears to be preconceived notions of computing that are difficult to overcome and that though Computational Thinking has close connections to programming the first cannot be automatically learned through teaching the latter” (Lamprou & Repenning, 2018).
These observations from Switzerland are reinforced by similar results from pilot studies withprimary school students in New Zealand; “We had hoped that Computational Thinking skills would be taught indirectly by teaching programming and other topics in computing, but from our initial observations this may not be the case” (Lamprou & Repenning, 2018). Similar studies of the perception of Computer Programming skills suggests that pre-service teachers think that they successfully learned how to program but it is less clear what they actually learnt with respect to Computational Thinking. The disparity between teaching Computer Programming and testing Computational Thinking is consistent with findings (Chapple, 1992).
5.2 Strategies
There are a multiverse of pedagogical strategies and learning resources to teach and learn Computational Thinking and Computer Science. Hsu et al’s studies identified Project-based learning, Problem-based learning, Co-operative learning and Game-based learning as the most prevalent strategies in Computational Thinking learning activities. “Aesthetic experience, Design-based learning and Storytelling have been relatively less frequently adopted. Future research should attempt to introduce different learning strategies, including the Scaffolding Learning Strategy, Storytelling Learning, and Aesthetic Experience, so as to aid students in multiple ways in terms of the development of subjects or high-level ability training, say, training in critical thinking and problem-solving ability” (T.-C. Hsu et al., 2018).
Accepted strategies to teach and learn Computational Thinking and Computer Science include using Bebras, Code.org and CS Unplugged.
Bebras Computing Challenge is an initiative by Bebras.org (Bebras.org, 2022a) that supports teachers and students learning Computational Thinking in more than 30 countries (Bebras.org, 2022b, 2022a). This construct of gamification learning enables students from ages 6 to 18 to develop their computational and logical thinking skills by answering 15 multichoice questions in 45 minutes across easy, medium and hard difficulty levels with results compared against other schools and countries.
Code.org (Code.org, 2022a) is an established global Computer Science initiative that more than 2 million teachers and 60 million students (Code.org, 2022b), 10% of all students in the world (Bučková & Dostál, 2017), have participated in and offers teacher professional development Computer Science resources from primary to secondary school level (Code.org, 2022a, 2022c). One of the Code.org strategies for teaching include the ‘Hour of Code’ activities designed for all ages and accessed in over 180 countries and 45 languages including Te Reo Māori in Minecraft (Alfriston College, 2022). There are more than 1500 scholarly articles that reference Code.org as a positive platform for learning Computational Thinking and Computer Science (Google Scholar, 2022).
CS Unplugged (University of Canterbury, 2022b) is a free ‘Computer Science without a computer’ educational platform designed for primary to secondary students that uses printable paper based games, puzzles and other hands-on resources to learn about the principles of Computational Thinking and Computer Science without using computers.
“Students use their decomposition skills to break down simple non-computerised tasks into precise, unambiguous, step-by-step instructions (algorithmic thinking), identify any errors and correct them (simple debugging)” (Bell et al., 2009). CS Unplugged was developed in the early 1990’s by Professor Tim Bell (Computer Science Educational Research Group of Canterbury University, New Zealand) (University of Canterbury, 2022b), Professor Michael Fellows (Elite Professor of Computer Science at University of Bergen, Norway) (Fellows, 2022) and Ian Witten (University of Waikato, 2022b). CS Unplugged has widespread adoption internationally and has substantial industry support (Bell et al., 2009). The CS Unplugged resources book, freely downloadable from the Internet (University of Canterbury, 2022c), has been translated into more than 20 languages including Arabic, Chinese, France, German, Italian, Japanese, Korean and Spanish by the international community of CS Unplugged ambassadors and students.
Academic research literature overwhelmingly supports the instructionally effective use of CS Unplugged to learn Computational Thinking and Computer Science without a computer. “Unplugged computing makes Computer Science more accessible. Teachers can integrate these activities in their classrooms to teach Computational Thinking skills as they do not require any prerequisite technical knowledge” (Delal & Oner, 2020). “It is recommended for school teachers teaching basic programming and Computational Thinking to consider using this offline, engaging and cost-effective approach as an alternative to computer-based methods of programming” (Threekunprapa & Yasri, 2020). “Recommendation is to adopt an analog-first approach to elementary Computer Science instruction based on Computational Thinking and implemented through the framework of cognitive acceleration” (Harris, 2018). “With the huge success of CS Unplugged worldwide, Bell & Vahrenhold, (2018) pointed out that it should be referred to as a general pedagogical approach and not be intended as a curriculum or a program of study and offers several benefits such as; No prerequisites for learning programming, Pedagogy offers spiral curriculum meaning, students first learn about basic facts of a subject or a topic and as the learning progresses more details are introduced, Tackling misconceptions about Computer Science in general including ‘how Computer Science is not just about programming’, and Easy deployment of activities, as no computers are required, hence, no technical issues” (Arora, 2019; Bell & Vahrenhold, 2018). “Teachers have found it empowering: they already know how to work with cards, string and chalk, and how to teach young children, so it provides the glue for them to do something without having to worry about digital devices crashing or being incompatible with the schools ystem” (Ministry of Education, 2017).
The technological advancements of mobile devices and the Apple iOS and Google Android apps platforms over the past decade has enabled users the opportunity to learn Computer Science and Computer Programming for free or paid applications. Examples include Lightbot (SpriteBox LLC, 2022) on iOS and Grasshopper (Grasshopper, 2022) on Android. Google’s Grasshopper JavaScript app offers the opportunity of developing Computer Science skills on a mobile device backed by Google’s extensive learning resources (Google For Education, 2022; Grasshopper, 2022). There are hundreds of apps available to learn Computer Science and Programming (Google Play, 2022) including ScratchJr. “Overwhelmingly the experience of using mobile digital devices for learning is presented as a positive experience regardless of the application or type of mobile device used” (Al-Zahrani & Laxman, 2016). “Mobile device use has become nearly universal worldwide, which can be seen from the increase in the number of mobile subscriptions per 100 people, from 12.075 in 2000 to 98.622 in 2015. Thus, it is not surprising that mobile devices are increasingly used for pedagogical purposes”(Sophonhiranrak, 2021).
There are numerous Computational Thinking Computer Science and Computer Programming learning resources on the Internet which include; Khan Academy’s free Computer Science and Programming curriculum (Khan Academy, 2022) the thousands of professional and amateur YouTube Computational Thinking, Computer Science and Programming instructional videos (YouTube.com, 2022a, 2022b, 2022c), the Massachusetts Institute of Technology OpenCourseWare platform (MIT OpenCourseWare, 2022) and MOOC platforms like Coursera (Coursera Inc, 2022), Edx (edX LLC, 2022), Udacity (Udacity Inc, 2022), Future Learn (FutureLearn, 2022) and Sololearn which offers free and paid account learning opportunities in more than 20 Computer Science courses including Python, C, C++, C#, Java, JavaScript, R, Kotlin, PHP, Swift and Ruby supported by a social network of more than 48,000,000 community members (Sololearn, 2022).
International academic consensus is that Computational Thinking is described as an essential skill set for the 21st Century (Harris, 2018; Lamprou & Repenning, 2018; Tang et al., 2020). It makes strategic sense for teachers and students to take advantage of the convenience of their mobile devices and online or app based learning resources to learn Computational Thinking, Computer Science and Computer Programming in their own time on their own devices or as part of a hybrid learning construct or in school classrooms time “undergraduate programs increasingly involve students using mobile devices for classroom activities” (Sophonhiranrak, 2021).
5.3 Teachers Professional Development
The academic research for this dissertation has established that there is a global need for teaching and learning Computational Thinking, Computer Science and Computer Programming in education and that teachers are at the forefront of enabling students to experience learning outcomes that relate to these three skill sets. “Teachers have a key role in implementing a Computational Thinking curriculum, and if they find the language used in the curriculum to be challenging then this can be a barrier to achieving the intention of the curriculum” (Munasinghe et al., 2021).
There are barriers for teachers new to computing and some academic research has found conflicting conclusions on teachers' understanding of these topics. “In some concepts presented in Computational Thinking as a foundation for Computer Science, teachers are already quite competent but in many cases primary teachers are ill prepared to teach the subject” (Harris, 2018). “Teachers new to computing who are not familiar with technical jargon can feel like they have landed in a foreign world, making them reluctant to take on the subject, and potentially leading to misconceptions and misunderstandings in the classroom. The diversity of technical words, metaphors, and phrases in different contexts can make their meanings confusing, ambiguous or misunderstood for the diverse user groups in computing education” (Munasinghe et al., 2021). “We observed issues and misconceptions that are a symptom of the large amount of new material and terminology that these topics introduce” (Duncan et al., 2017). “There is a lack of teacher training for Computational Thinking, this indicates that fostering Computational Thinking is still a challenge due to only a few teachers being trained with the knowledge and skills to integrate Computational Thinking into course curricula” (Tang et al., 2020).
Other considerations that compromise teachers professional development (PD) for learning Computational Thinking, Computer Science and Programming include;
- Board of Trustees and senior management not prioritising PD for these subjects
- Management and teachers not understanding the importance of these subjects
- Complexity of integrating these subjects into existing school curriculum
- Complexities of adding these subjects as new classes to existing timetables
- No or minimal PD timetable allowance for teachers to learn these subjects
- Teachers unwilling to spend their own time learning these subjects
- No financial incentives for teachers to learn these subjects
- Understanding the complexities of technical jargon of these subjects
- No or only a few in school experts who can provide PD for these subjects
- There are few external providers offering PD for these subjects
Research from some pilot initiatives in primary schools involving CS Unplugged Computational Thinking learning resources in Christchurch New Zealand have found conflicting results; ”Typical primary school teachers who are new to this subject material and have self-reported low levels of confidence” (Duncan et al., 2017). “Some teachers in the pilot are embracing the new material and many have reported that through Computational Thinking activities they are also teaching other curriculum areas, and hence the impact on teaching time is relatively low. Some teachers have observed that students who were previously disengaged with their learning are drawn to the Computational Thinking exercises because they are using materials and movement to solve problems” (Ministry of Education, 2017).
Contrary to these primary schools initiatives, there are currently no academic papers that relate to research that supports New Zealand secondary school teachers' professional development experiences or instances of integrating Computational Thinking into their senior curriculums.
International research suggests that; “New comprehensive approaches are needed to cope with the complexity of cognitive processes related to Computational Thinking. To help teachers assess Computational Thinking skills, new tools and criteria are required. Support from national or transnational research programmes could prove instrumental in achieving this goal. The introduction of Computational Thinking in the curriculum is creating a strong demand for large-scale in-service continual professional development (CPD), as many teachers did not learn about Computational Thinking in their initial education. It is of paramount importance that teachers and school staff should be provided with training opportunities that strongly focus on Computational Thinking pedagogy and hands-on learning which can be easily transferred to the classroom. In addition, policy actions could also include peer exchanges and community building to enable the sharing of best practices among teachers” (Bocconi et al., 2016).
Chapter 6 - Methodology
6.1 Rationale for analysis
The qualitative descriptive study of this dissertation is to look at the intricate relationship between Computational Thinking, Computer Science and Computer Programming in a school context with a lens on Māori and Pasifika students in New Zealand. This study also aims to follow the principle goal that a dissertation review is to “summarise the accumulated state of knowledge concerning the relation(s) of interest and to highlight important issues that research has left unresolved" (Al-Zahrani & Laxman, 2016).
6.2 Search process
The peer reviewed literature articles referred to in this dissertation were sourced from the University of Auckland's The Catalogue Libraries and Learning Services database and Google Scholar using keywords; ‘Computational Thinking’, ‘Computer Science’, ‘Coding’, ‘Programming’, ‘Digital Technologies’ and ‘Hangarau Matihiko’. The search was limited to articles from 2015 to ensure relevance given the rate of technological change and the recent integration of Computational Thinking in schools internationally. Exceptions were made when referencing historical articles like Wing’s 2006 highly referenced dissertation (Ministry of Education, 2017). To complement this research a variety of educational online resources such as the New Zealand Ministry of Education and TKI websites and other education focused websites are referenced throughout this dissertation.
6.3 Inclusion and exclusion criteria
Included in this study is the New Zealand Digital Skills Forum ‘Digital Skills Effort Survey 2021’ as it is the most up to date research data and addresses and raises significant issues and opportunities especially in relation to Māori and Pasifika students (New Zealand Tech Alliance, 2021). The survey is from the New Zealand Tech Alliance, a group of not-for-profit, non-governmental independent technology organisations from across New Zealand that work together to ensure a strong voice for technology (New Zealand Tech Alliance, 2022). “For the first time ever, data from our Digital Skills Survey has been aggregated across the entire digital skills pipeline, from school to tertiary education, from education to employment, from within the market and from immigration. The research found system wide challenges requiring urgent national attention, a lack of coordinated effort, dramatic skills challenges driving a heavy reliance on immigration and under investment in developing the existing workforce. The success of the digital technology sector is critical for New Zealand’s future. It is one of the fastest growing parts of the New Zealand economy, generating billions of dollars in exports and creating thousands of jobs each year. The sector is also enabling the digitalisation of the rest of the economy and underpinning this are people with digital skills" (New Zealand Digital Skills Forum, 2021).
Excluded from this study are University of Auckland's The Catalogue Libraries and Learning Services database articles sourced from keyword searches that mainly focused on Computer Programming and Coding unless they provided a significant contribution to the advancement of Computational Thinking and Computer Science in schools.
6.4 Scope and limitations of this study
This scope of this study examines current findings of Computational Thinking, Computer Science and Computer Programming in a school context with a lens on Māori and Pasifika students in New Zealand. Points of discussion include the Digital Technologies | Hangarau Matihiko curriculum, challenges and strategies in teaching Computational Thinking and Computer Science and teachers professional development in these areas.
There are significant gaps in the literature of Computational Thinking, Computer Science & Computer Programming which this dissertation does not dwell on but are significant enough for future research and are referred to in Chapter 1.4.
The limitations of this dissertation include no in-depth descriptions or academic literature research of the seven fields of Computational Thinking, the sixteen fields of Computer Science or Computer Programming.
His dissertation does not focus on STEM (Science, Technology, Engineering, Mathematics) or eLearning but acknowledges that Computational Thinking, Computer Science & Computer Programming are inherent in these fields. Each of these limitations are significant bodies of work in their own right and their impact on the broader overarching areas of Computational Thinking, Computer Science & Computer Programming.
The scope and limitations of this dissertation are also imposed by the type of study and word length requirements.
Chapter 7 - Conclusions and Future Research
7.1 Conclusions
These dissertation conclusions of Computational Thinking, Computer Science and Computer Programming literature are guided by the question; What is Computational Thinking, Computer Science and Computer Programming in a New Zealand education context with a lens on Māori and Pasifika students?
This study concludes that understanding the complexity of Computer Science and Computer Programming and the emerging status of Computational Thinking requires greater exposure, adoption and professional development at all levels of education to realise the equitable 'digital nation’ ambitions of New Zealand.
Computational Thinking and Computer Science conclusions
Computer Science in education has widespread adoption nationally and internationally and many schools around the world are beginning to reform their curriculums to include Computational Thinking however despite this widespread interest, successful Computational Thinking integration in compulsory education still faces unresolved issues and challenges. International research identifies a lack of regular professional development opportunities for teacher training in Computational Thinking, this indicates that fostering Computational Thinking and Computer Science is still a challenge due to only a few teachers being trained with the knowledge and skills to integrate these into course curricula. Research for this dissertation supports suggestions that many New Zealand schools or teachers have yet to implement a Computational Thinking curriculum, backed by the fact there is minimal academic research into the adoption of Computational Thinking in a New Zealand school context, especially in secondary schools.
Computer Programming and Coding conclusions
International research concludes that Computing Programming and Coding is explicitly regarded as a key 21st century skill and that children as young as five years old are engaged when learning the basic constructs of programming, especially when using Scratch Jr. Children aged eight to sixteen years old engaged with higher level thinking due to the game based programming activities, peer/collaboration approaches and task-based learning using Scratch and other block-based programming platforms like Minecraft, Alice and initiatives like Code.org and CS Unplugged.
In New Zealand, the Learning Progress outcomes that all students are mandated to experience is a significant catalyst to prepare students to achieve at the New Zealand Qualification Authority Computer Programming assessments at Levels 1,2 and 3. Students need to demonstrate coding skills in various programming language syntax and research concludes that this is rather passive, difficult and inaccessible to students. Māori and Pasifika students have low achievement rates in these Computer Programming assessments so the Learning Outcomes are a positive initiative to address these issues.
Computational Thinking in New Zealand education conclusion
The rationale for adding Learning Progress Outcomes that include skills and knowledge in three key areas of data representation, algorithms, and programming from Year 1 means that by year 10 year students will be ready, with good teaching, to be successful in all of the New Zealand Qualification Authority Digital Technologies | Hangarau Matihiko assessments. To facilitate these objectives, research concludes that integrating CS Unplugged ‘computing without computers’ into junior programmes of learning and Mathematics classes enhances cross-curricular pedagogies that integrate digital technology constructs within existing curriculums. Some research supports these conclusions based on a small number of case studies in primary schools in Christchurch but there is minimal evidence of widespread implementation of these initiatives especially in secondary schools in New Zealand.
The New Zealand Government ‘digital skills for a digital nation’ vision is for all New Zealanders to thrive in a digital age. To help achieve these long term goals, Computational Thinking was integrated into the New Zealand Curriculum Technology learning area to strengthen the existing Computer Science and Programming curriculum. Learning Progress Outcomes were added to the New Zealand Digital Technologies | Hangarau Matihiko curriculum which describes the significant learning steps that students take as they develop their expertise in Computational Thinking for digital technologies from year 1 to 13, a significant shift from the existing Digital Technology Programming curriculum being optional for Years 11 to 13. Conclusions from this dissertation highlight that there is minimal research concerning the acceptance, readiness and impact of these new Learning Progress Outcomes and Computational Thinking initiatives in a New zealand context especially at a secondary school level.
Māori and Pasifika students
This dissertation highlights that academic research into Computational Thinking for Māori and Pasifika students is minimal. There is a lack of research on how teachers can undertake professional development to focus on the specific cultural needs of these students to increase their participation in the technology curriculum to improve academic achievement and success in the New Zealand Qualifications Authority Computer Science assessments. Māori and Pasifika academic results and representation in the IT workforce is very low but there are significant resources and education initiatives are an impetus to make a difference in the long term with ongoing support from the Ministry of Education and industry partners.
7.2 Recommendations for future research
This dissertation has identified a number of topics for future research that relate to Computational Thinking, Computer Science and Computer Programming.
● Professional development of teachers of Computational Thinking and Computer Science knowledge that focus on the specific cultural needs of Māori and Pasifika.
● Low achievement of Māori and Pasifika in Computer Programming assessments.
● Acceptance, readiness and impact of the new Learning Progress outcomes 1 to 8 for Data Representation, Algorithm and Programming curriculum subjects.
● How Computational Thinking is being integrated into intermediate and secondary schools.
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