Advancing the concept of computational thinking

1. Academic summary

Computational Thinking (CT) is a group of problem-solving abilities that are considered increasingly relevant in everyday life and education. CT is viewed fundamental in adapting to data-driven environments and underlies effective and efficient interaction with information systems for computer programmers and casual users alike. Despite its growing international recognition and a drive to overhaul education curricula with CT in the fore, a unified definition of CT is lacking, and further research is needed to investigate its relationships with other, theoretically well-established mental abilities (e.g., general intelligence and visuospatial ability). Although the development psychometric instruments to measure CT has seen recent progress, its measurement in most of the available academic literature is not standardised and varies greatly between application contexts. Gender differences in the growing CT literature (predominantly to the advantage of males) also need further investigation, as it seems plausible that the operationalisation and assessment of CT may be biased in certain directions, such as an over-representation of logical and visuospatial tasks. The proposed project will contribute to the establishment of a theoretically grounded and empirically supported nomological network for CT.

2. Research questions and goals

The project is based on research questions related to four broad areas: (1) the relationship between CT and intelligence, visuospatial ability, and gender; (2) the relationship of CT with educational performance and attitudes; (3) differences in CT performance due to type of task; (4) the role of contextual factors in CT performance. These questions are fundamentally related to the reliable measurement of CT. Existing standardised measurement of CT will be used to generate data on a large sample of Hungarian students in secondary and higher education to test the scale’s psychometric properties (structure, sensitivity, reliability and validity). The project will be realised in two phases: Phase 1 examines the relationships between CT and key areas of mental ability and other differentiating factors, notably intelligence, gender, and visuospatial ability; Phase 2 concerns the relationship between CT and CT outcomes, and involves experimental manipulation of task- and context factors, supported by the collection and analysis of longitudinal data. The general aim of the proposed project is to advance knowledge of Computational Thinking by addressing the above areas through producing empirical research, summarising the results in a conceptual model of CT, and disseminating the findings in international academic journals and conferences.

3. Significance

CT is a relatively new field of enquiry width its widely recognised seminal paper dating back to 2006. Although the importance of CT both in theory and practice has been readily recognised and several countries made notable attempts to incorporate CT development in school curricula, the field is still considered to be in an early stage, mostly due to a lack of a generally accepted definition and measurement. Additionally, empirical research in the field is predominantly related to “if” or “whether” types of questions (e.g., “Is there a relationship between CT and X or Y?”), and have not made suitable progress towards “when” questions (e.g., “When/under what conditions is there a relationship between CT and X or Y?”) and “how” questions (e.g., “How/in what way is CT related to X or Y?”). The proposed project aims to bridge this gap by adopting a modelling approach, in which various aspects of CT (e.g., mental abilities, individual differences, types of task, context, etc.) will be considered simultaneously in the analysis of cross-sectional data, and manipulated separately in experimental work. “When” questions are conceptualised as interactions between CT and other factors; “how” questions are conceptualised as mediation effects between CT and other factors. Confounding and epiphenomenal associations will be explicitly addressed, most notably to clarify the relationship between CT and general intelligence while controlling for covariates and moderating factors (e.g., age, gender, visuospatial ability, etc.). Collecting longitudinal data will allow for analysing temporal trends not sufficiently addressed in the available literature (i.e., contrasting age groups typically uses between-subject analyses of cross-sectional data). The proposed project is thus expected to yield significant theoretical and methodological advancement in the field, with practical relevance to application in education research and policy development.

The skills needed to successfully navigate everyday life has seen rapid change within a single generation. With the accelerating proliferation of computers in every area of activity, be it leisure, learning, work, or social interactions, it has become increasingly relevant to learn how to use these systems effectively. Most of us use computers daily to all kinds of tasks. Our ability that underlies successful interaction with these systems is called Computational Thinking (CT). Although CT is especially relevant to computer programming, it is also important to people in all walks of life, as those with high CT skills are expected to perform better in everyday tasks and at the workplace. In recognition of this, many countries have started to remodel education curricula with a focus on improving CT skills; however, it is not clear how these skills can be best promoted, especially considering people’s different needs, interests, and abilities. It is important to understand how CT is related to other abilities such as general intelligence, and to individual differences like gender. Our research project addresses these questions by assessing CT and other abilities in students in secondary and higher education. For example, we will examine if men and women who do not differ in terms of intelligence are different in their CT abilities, and if these differences depend on the type of tasks people have to solve using computers. Another example is to see how performance in different school subjects are related to CT, and how tasks and circumstances can be tailored to support CT performance. The project will progress our understanding of human abilities and inform education practice.