Learning to coding comprises activities to create, modify, and understand codes along with the knowledge about coding concepts and procedures. Computer science educators consider coding a vehicle to teach computational thinking which can be broken down into:
abstraction and pattern generalization
systematic information processing
symbol systems and representations
debugging and systematic error detection.
These skills share considerable similarities with general problem solving and problem solving in specific domains. Thus, it is reasonable to expect some level of transfer effects between coding and problem solving. Similarly, solving math problems requires students to decompose a problem into its parts (variables), understand their relations (functions), use mathematical symbols to represent these relations (equations), and apply algorithms to obtain a solution - activities mimicking the coding process.
Despite the conceptual backing of such transfer effects, is there actually evidence exists to back these claims empirically?
Liao and Bright (1991) - First evidence that learning to code aided the acquisition of other cognitive skills
In 1991, Liao and Bright reviewed 65 empirical studies on the effects of learning-to-code interventions on measures of cognitive skills. Drawing from the published literature between 1960 and 1989, the authors included experimental, quasi-experimental, and pre-experimental studies in classrooms with a control group (non-coding) and a treatment group (coding).
This meta-analysis resulted quantifying the existence of transfer effects. This study indicated that learning coding aided the acquisition of other skills to a considerable extent. Although this meta-analysis was ground-breaking at the time, transferring it into today's perspective on coding and transfer is problematic for several reasons: First, during the last three decades, the tools used to engage students in computer coding have changed substantially, and visual programming languages. Second, Liao and Bright included any cognitive outcome variable without considering possible differences in the transfer effects between skills (e.g., reasoning may be enhanced more than reading skills).
Tondeur et al. (2019) - Learning to code had a medium effective on cognitive skills
This meta-analysis included experimental and quasi-experimental intervention studies with pretest-posttest and posttest-only designs. Each educational intervention had to include at least one control group and at least one treatment group with a design that allowed for studying the effects of coding. Finally, the outcome measures were performance-based measures, such as the Torrance Test of Creative Thinking or intelligence tests.
This meta-analysis showed that learning to code had a positive and strong effect on coding skills (g¯¯¯ = 0.75) and a positive and medium effect on cognitive skills other than coding (g¯¯¯ = 0.47). The authors distinguished further between the different types of cognitive skills and found a range of effect sizes, g¯¯¯ = −0.02–0.73 (Figure 1). Ultimately, they documented the largest effects for creative thinking, mathematical skills, metacognition, reasoning, and spatial skills (g¯¯¯ = 0.37–0.73).
While there is some evidence to suggest that coding has transfer effects on other cognitive skills, proof of causation has remained elusive
These studies while interesting have their limitations due to the difficulty to measure cognitive benefits, resulting in study designs that cannot prove strictly causal conclusions. Instead, one may conclude that learning to code was associated with improvements in other skills measures.
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