Logical Reasoning in Formal and Everyday Reasoning Tasks
By Hugo Bronkhorst, G. Roorda, Cor J. M. Suhre, Martin J. Goedhart. 2020
89 citations recorded by the source corpus.
Logical reasoning is of great societal importance and, as stressed by the twenty-first century skills framework, also seen as a key aspect for the development of critical thinking. This study aims at exploring secondary school students’ logical reasoning strategies in formal reasoning and everyday reasoning tasks. With task-based interviews among 4 16- and 17-year-old pre-university students, we explored their reasoning strategies and the reasoning difficulties they encounter. In this article, we present results from linear ordering tasks, tasks with invalid syllogisms and a task with implicit reasoning in a newspaper article. The linear ordering tasks and the tasks with invalid syllogisms are presented formally (with symbols) and non-formally in ordinary language (without symbols). In tasks that were familiar to our students, they used rule-based reasoning strategies and provided correct answers although their initial interpretation differed. In tasks that were unfamiliar to our students, they almost always used informal interpretations and their answers were influenced by their own knowledge. When working on the newspaper article task, the students did not use strong formal schemes, which could have provided a clear overview. At the end of the article, we present a scheme showing which reasoning strategies are used by students in different types of tasks. This scheme might increase teachers’ awareness of the variety in reasoning strategies and can guide classroom discourse during courses on logical reasoning. We suggest that using suitable formalisations and visualisations might structure and improve students’ reasoning as well.
Subfields: Artificial Intelligence, Developmental and Educational Psychology, Education
View Paper: https://www.semanticscholar.org/paper/61ff8457f0ff89fc16d284effa4f960cff4e9162
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Citations in the corpus
- Towards Reasoning in Large Language Models: A Survey (2022, background): “We refer the reader to Galotti (1989); Bronkhorst et al. (2020) for a detailed distinction between them.”