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Promoting Computational Thinking, Computational Participation, and Spatial Reasoning with LEGO Robotics

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Abstract

This paper examines the context for developing computational thinking (CT), computational participation (CP), and spatial reasoning among predominantly Black elementary students in grades 5 and 6, who attended a public school in the urban fringe of a large city in the Eastern United States. A case study is presented as the method to examine two African-American female teachers’ use of culturally relevant pedagogy to enhance these students’ learning and engagement in a 10-week informal after-school program where LEGO® robotics tasks were implemented. The processes of coding, constant comparison, and convenience sampling were used to analyze data using qualitative methods. Results revealed evidence of CT among nine focal students. CP was not observed at the baseline but was evident at the endpoint. Moreover, six of the nine focal students demonstrated a variety of spatial reasoning skills during LEGO® robotics tasks, which are correlated with higher mathematics achievement and academic success. Further analysis revealed that these focal students persisted in problem solving during the LEGO® robotics program. Focus group interviews with the two teachers and eight randomly selected students support these outcomes.

Résumé

Cet article examine le contexte qui favorise le développement de la pensée computationnelle (PC), de la participation computationnelle et du raisonnement spatial chez des élèves de cinquième et sixième année du primaire, majoritairement noirs, qui fréquentent une école publique située en périphérie urbaine d’une grande ville de l’est des États-Unis. On présente une étude de cas comme méthode pour examiner l’utilisation de la pédagogie culturelle par deux enseignantes afro-américaines dans le but d’améliorer l’apprentissage et l’engagement de ces élèves dans le cadre d’un programme informel de dix semaines qui se déroule après l’école et dans lequel des tâches de robotique LEGOMD sont employées. Les processus de codage, de comparaison continue et d’échantillonnage de commodité ont été utilisés pour analyser les données à l’aide de méthodes qualitatives. Les résultats montrent que neuf élèves ciblés ont fait preuve de PC. Celle-ci n’a pas été observée dans les conditions de départ, mais elle était évidente au résultat final. De plus, six des neuf élèves ciblés ont démontré une variété de compétences de raisonnement spatial dans les tâches de robotique LEGOMD, qui sont corrélées avec des résultats plus élevés en mathématiques et la réussite scolaire. Une analyse plus approfondie a révélé que ces élèves ciblés ont continué à résoudre des problèmes pendant le programme de robotique LEGOMD. Les entrevues de groupe menées avec les deux enseignantes et huit élèves choisis au hasard confirment ces résultats.

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Acknowledgements

The material presented in this paper is based upon work supported by the National Science Foundation under Grant No.1311810. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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National Science Foundation, 1311810, Jacqueline Leonard.

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Authors and Affiliations

  1. School of Teacher Education, University of Wyoming, Laramie, WY, USA

    Jacqueline Leonard & Aylin S. Carey

  2. Educational Studies, Rhodes College, Memphis, TN, USA

    Cara Djonko-Moore

  3. University of Calgary, Calgary, AB, Canada

    Krista R. Francis

  4. MERAssociates, LLC, Vienna, VA, USA

    Monica B. Mitchell

  5. Department of Teaching and Learning, Policy and Leadership, University of Maryland at College Park, College Park, MD, USA

    Imani D. Goffney

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  1. Jacqueline Leonard
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  2. Cara Djonko-Moore
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Appendix

Appendix

Computational Thinking (CT) Rubric for Robotics*

CT categories

Emergent (1)

Moderate (2)

Substantive (3)

Formulating problems [abstraction]

Follows specific steps to complete robotics task(s)

Deviates from specific directions somewhat to tweak robotics task(s) using trial and error

Follows unique instructions based on prior experience to complete robotics task(s)

Abstraction

Visualizes model to be constructed based on pictures (i.e., 2D model to 3D construction)

Visualizes model to be constructed by tinkering with physical parts

Visualizes model to be constructed from prior experience

Logical thinking [abstraction]

Uses sequential steps to complete robotics task(s)

Alters steps as needed to complete robotics task(s) using trial and error

Uses logical reasoning to move from one step to the next to complete robotics task(s)

Using algorithms [automation]

Uses MINDSTORMS® coding as described in a manual or online

Describes or tinkers with MINDSTORMS® coding based on trial and error

Uses MINDSTORMS® to create unique code based on the task(s) to be performed

Analyzing and implementing solutions [analysis]

Identifies a problem and/or considers alternatives

Tinkers with design or debugs code using trial and error to problem solve

Offers solutions to design or coding problems based on prior knowledge or experience

Generalizing and problem transfer [analysis]

Generalizes learning for a single step in a task

Generalizes learning from one step in a task to another step using trial and error

Generalizes or transfers learning from one task to another task using prior knowledge or experience

  1. *Adapted from Repenning et al. (2015) and ISTE (2011)

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Leonard, J., Djonko-Moore, C., Francis, K.R. et al. Promoting Computational Thinking, Computational Participation, and Spatial Reasoning with LEGO Robotics. Can. J. Sci. Math. Techn. Educ. 23, 120–144 (2023). https://doi.org/10.1007/s42330-023-00267-0

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