From Virtual to Physical Problem Solving in Coding

Using coding education to promote computational thinking and nurture problem-solving skills in children has become an emerging global trend. However, how different input and output modalities in coding tools affect coding as a problem-solving process remains unclear. Of interest are the advantages and disadvantages of graphical and tangible interfaces for teaching coding to children. We conducted four kids coding workshops to study how different input and output methods in coding affected the problem-solving process and class dynamics. Results revealed that graphical input could keep children focused on problem solving better than tangible input, but it was less provocative for class discussion. Tangible output supported better schema construction and casual reasoning and promoted more active class engagement than graphical output but offered less affordance for analogical comparison among problems. We also derived insights for designing new tools and teaching methods for kids coding.

The Benefits and Challenges of Analogical Comparison in Learning and Transfer

There is ample evidence that analogy can be employed as a powerful strategy for learning new concepts, transferring knowledge, and promoting higher level thinking. Similarly, self-explanation has been shown as an effective strategy in learning, integrating new information with prior knowledge, and monitoring and revision of previous mental models (Chi et al., 1989). While both of these strategies are considered efficient scaffolding in the field of instruction and learning, each individual strategy has its own limitations and constraints such as overgeneralization, disregarding details, and possible erroneous reasoning. To investigate whether these constrains can be overcome, a review of literature was conducted and each individual scaffolding strategy was studied. At the end, the potential benefits of integrating both strategies – generating explanation using analogical comparison – were discussed. It was hypothesized that prompting learners to explain analogical cases (analogy induced self-explanation) may greatly enhance learning through activation of prior knowledge, structured linking, categorical learning and higher order thinking. This integration may lead to a revised model of self-explanation with higher productivity and less constraints on the process of knowledge acquisition and generalization.

From Virtual to Physical Problem Solving in Coding

Using coding education to promote computational thinking and nurture problem-solving skills in children has become an emerging global trend. However, how different input and output modalities in coding tools affect coding as a problem-solving process remains unclear. Of interest are the advantages and disadvantages of graphical and tangible interfaces for teaching coding to children. We conducted four kids coding workshops to study how different input and output methods in coding affected the problem-solving process and class dynamics. Results revealed that graphical input could keep children focused on problem solving better than tangible input, but it was less provocative for class discussion. Tangible output supported better schema construction and casual reasoning and promoted more active class engagement than graphical output but offered less affordance for analogical comparison among problems. We also derived insights for designing new tools and teaching methods for kids coding.

Evidence from machines that learn and think like people

We agree with Lake et al.'s trenchant analysis of deep learning systems, including that they are highly brittle and that they need vastly more examples than do people. We also agree that human cognition relies heavily on structured relational representations. However, we differ in our analysis of human cognitive processing. We argue that (1) analogical comparison processes are central to human cognition; and (2) intuitive physical knowledge is captured by qualitative representations, rather than quantitative simulations.