Conference Agenda

The study of Computer Science (CS)/coding in schools has gained renewed interest in recent decades after the early optimism of the late 1970 and 1980s abated because of the rise of interest in the integration of ICT across the curriculum in schools in the 1990s (Brown et al., 2014). This resurgence of interest has since accelerated (Yadyav et al, 2016; Williamson et al, 2019). These initiatives are often in the form of ‘learning to code’ courses or specific CS subjects (Ottestad & Gudmundsdottir, 2018). Analysis by Heintz et al (2016) and Keane and McInerney (2017) suggests that this interest has mainly materialised as standalone subjects on the curriculum rather than being integrated within existing subjects. Williamson et al (2019) argue that such is the level of attention afforded to CS/coding it has become a transnational policy movement. They also remark that despite the relatively quick materialisation of CS/coding in schools as a major policy agenda, the area remains under-researched.

Despite the interest and attention afforded to CS/Coding in schools, the rationales for its introduction vary ranging from explicit economic rationales concerned about national economic competitiveness (Tucker, 2003; McGarr & Johnston, 2020) to broader social and educational justifications aimed at addressing inequalities of opportunity or developing specific cognitive skills for students such as problem solving and analytical skills. These different agendas and rationales are a product of the messiness of the policy making process where multiple stakeholders elbow for influence (Williamson et al, 2019).

When one considers how CS/Coding is materialised in schools, as a stand-alone subject rather than being integrated across the curriculum, it appears to contradict the rationales put forward for its integration in schools. There appears to be a disconnect between the policy rhetoric for a coding for all agenda and how it is ultimately realised in schools as an optional discrete subject.

To explore the rationale for this ‘gap’, this paper explores the recent interest in CS/Coding in schools through the lens of curriculum ideologies (Ross, 2000). Viewing the recent attention in this area as an example of a contemporary curriculum change, this paper aims to undertake a theoretical exploration of the literature surrounding the history of CS/coding in schools using the theoretical lens of curriculum ideologies. It therefore aims to use this historical exploration to help explain past and present rationales for its study in schools. In doing so it aims to highlight the contradictions in current policies.

Computational Thinking (CT) regained interest from researchers, policymakers, and educators in the aftermath of Wing’s (2006) position article where CT was defined as a fundamental skill that “includes a range of mental tools that reflect the breadth of the field of computer science” (p. 33). However, despite the apparent consensus that CT is a crucial skill that has been implemented in school curricula in several countries (Hsu et al., 2019), there is limited consensus as to how CT is defined. Brennan and Resnick’s (2012) definition divides CT into three key dimensions: computational thinking concepts which are understanding fundamental programming concepts like loops, operators, and conditionals; computational practices which are about the processes of thinking and learning; and computational perspectives which are about how the person understand themselves, the connection with others, and the technical world surrounding them and how these understandings evolve. However, Brennan and Resnick’s (2012) understanding of CT is closely connected to the Scratch environment which might introduce constraints to their framework. Based on their review of the state of the field, Grover and Pea (2013) described CT as comprising abstractions and pattern generalizations; systematic processing of information; algorithmic notions of flow of control; structured problem decomposition; conditional logic; efficiency and performance constraint; debugging and systematic error detection; and iterative, recursive, and parallel thinking. Weintrop et al.’s (2016) CT definition focuses on four main categories: data practices, modelling and simulation practices, computational problem-solving practices, and systems thinking practices, aiming at developing a nuanced understanding of CT in mathematics and science. Shute et al. (2017) proposed a model for CT that aimed at being useful between disciplines and instructional settings. They defined “CT as the conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts” (p. 151). Consequently, they understood CT as a logical way of thinking. Further, they categorized CT into decomposition, abstraction, algorithms, debugging, iteration, and generalization.

Tang et al. (2020) argued that an essential difference between the various definitions of CT is whether the definition focuses on CT as programming and computing, and those that focus more on CT as competencies needed in both domain-specific knowledge and general problem-solving. Shute et al. (2017) raised an important issue in their definition of CT, namely the need to define CT across different contexts. Yadav et al. (2022) pointed out the lack of studies that focus on CT in initial teacher education, highlighting the need to develop not necessarily a consensual definition of CT, but rather a working framework that can span both school and teacher education.

Consequently, this study aims to develop a flexible model for CT competencies that can be used across different education levels, by teachers, teacher educators and student teachers. To do this, we investigate the following research questions:

RQ1: What characterizes the definitions and operationalizations of CT used in empirical studies of CT?

RQ2: What are the converging and diverging understandings of CT used in empirical studies of CT?

In our study, we view CT as a ‘boundary object’, drawing on Star and Griesemer (1989). As a boundary object, CT is viewed as an ‘ill-structured’ concept that has resulted in a tug-of-war. However, from a boundary object perspective, it is this very lack of consensus that can contribute to developing a model that is flexible and adaptable in different contexts.

Within the last decade, several national curricula have introduced computational thinking (CT), either through a dedicated subject or integrated within existing subjects (Bocconi et al., 2022) Several reviews of CT highlight the lack of a unified definition e.g. Weintrop et al. (2016) which in turn percolates to a disparity in the assessment of CT. Nevertheless, there is a consensus that CT includes concepts and practices which are foundational in computing and are crucial in a wide range of problem-solving. With the ever-growing use of digital tools in schools and in all kinds of professional practices, it becomes relevant to introduce CT in learning contexts.

CT frameworks vary from more generic to subject-specific. Examples of more generic frameworks are Brennan and Resnick (2012), and Grover and Pea (2013) which overlap on aspects like decomposition, abstraction, algorithms, and debugging. The more subject-specific frameworks relevant to the integration of CT into mathematics and science highlight approaches like formulating problems, gathering and analysing data, and modelling e.g. Weintrop et al. (2016). Integration of CT in curricula will affect the learning processes and, therefore, should have implications for assessment.

Only a few of the CT frameworks are operationalized as assessment frameworks. There are numerous assessment tools, but many are focused on programming practices or as part of a computer science subject. Furthermore, the tools are often automatized providing summative assessment. Many frameworks are targeted towards intervention studies rather than assessment criteria for educators. As CT is a growing educational field, there is a need for a CT framework which can be applied by teachers and inform their design of teaching and assessment. Such guidelines for both teachers and students can be understood as shared learning intentions and criteria (Wiliam, 2011) and measures that may enable educators to evaluate the effectiveness of incorporating CT in curricula (Grover & Pea, 2013).

Discussions regarding the assessment of CT have frequently focused on CT as a generic skill e.g. Román-González et al. (2019) or assessments of students’ programming or computing skills (Tang et al., 2020). There is a need for developing an assessment framework providing formative and summative assessment relevant for integrating CT into subjects (Tang et al., 2020).

CT was included in several subjects in the Norwegian curriculum in 2020. In mathematics, students are introduced to the basic concepts of programming like variables, loops, and conditions. Building on the skills developed in mathematics, the students are expected to use programming in science, arts and crafts, and music. Although the curriculum uses the term programming, it is broadly understood as a concept close to CT. Through programming, the students should explore the subject matter enhancing their learning outcome (Norwegian Directorate for Education and Training, 2019).

The aim of this study is to explore the assessment constructs aligned with the relevant CT definitions and with the subject-matter knowledge. Thus, in this study we raise the following research question:

RQ1: What constructs inform a framework for the assessment of CT?

RQ2: How can a set of CT assessment constructs support practitioners’ teaching and assessment?

Formative and summative assessment are related and they play an important role in students learning (Wiliam, 2011). CT is complex and therefore it is recommended to develop rich and complementary systems of assessment (Grover, 2017; Román-González et al., 2019). To create the framework, we draw on a literature review, teacher interviews as well as on classroom observations. The framework is furthermore tested in close collaboration with teachers.