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Understanding how elementary students approach computer science (CS) and computational thinking problems is best informed by cognitive, motivational, and metacognitive theories.
Cognitive Theory
Cognitive theory emphasizes the processes and structures involved in learning and knowledge acquisition (Piaget, 1952). Piaget’s theory of cognitive development posits that children progress through distinct stages, each characterized by specific ways of thinking and understanding the world. While there have been extensive challenges to the mechanistic Piagetian approaches it still serves as a guard against considering young learners as "little adults". In the context of CS, understanding where elementary students fall within these stages can shed light on their capacity to grasp abstract concepts, logic operations, and problem decomposition, fundamental aspects of computer science.
Metacognitive Theory
Metacognition refers to the awareness and understanding of one's own thought processes. Flavell (1979) introduced the concept of metacognitive knowledge, comprising the awareness of one’s cognitive processes and the ability to control and regulate those processes. For CS education, this translates into students' abilities to plan, monitor, and evaluate their problem-solving strategies (Yadav et al., 2022). If students are equipped with metacognitive skills, they tackle the problem at hand and reflect on their approach, making adjustments as necessary. This builds the skills and stamina to tackle increasingly hard CS problems.
Incorporating developmental and metacognitive theories provides a holistic understanding of elementary students' CS problem-solving approaches. While cognitive theories can help in discerning their readiness and capability to engage with CS concepts, metacognitive theories can illuminate the self-regulatory strategies they employ, offering insights into how they perceive, plan, and reflect on their problem-solving journey.
Beyond the cognitive and metacognitive frameworks, motivation plays a pivotal role in how elementary students engage with computer science problems. Bandura (1977) introduced the concept of self-efficacy, which refers to an individual's belief in their capacity to execute tasks and reach goals. Central to this is the efficacy feedback loop: as students engage with a task and experience success, their self-efficacy increases, leading to greater motivation and persistence in future tasks.
This feedback loop can be particularly potent in the realm of CS education. When students solve a computational problem successfully, the immediate feedback (often in the form of a working program or a solved algorithm) can bolster their confidence and eagerness to tackle more complex problems. This is where the concept of attention becomes critical. As posited by Keller's ARCS model of motivation (Keller, 1987), attention is the first step to engage learners. Intriguing problems, relatable real-world applications of CS, and interactive learning platforms can capture students' attention, thus initiating the cycle of engagement, learning, and increased self-efficacy.
Incorporation into the Study
The study can offer a multi-faceted perspective by integrating the motivational dimension, especially the efficacy feedback loop and the element of attention. This would entail not just how elementary students solve CS problems from a cognitive and metacognitive standpoint but also what motivates them, how immediate feedback enhances their self-belief, and the role of engaging content in retaining their attention and fostering deeper involvement in problem-solving. Such insights would be instrumental in designing CS curricula that are not only instructionally sound but also inherently motivating for young learners.
By framing the study within these theoretical backgrounds, it becomes possible to develop teaching strategies that are attuned to elementary students' cognitive developmental stage and that foster their metacognitive abilities, ensuring a more holistic and effective CS education for young learners.
Participants
Fifty six elementary students, aged between 6-11 yearsold from two different midwestern schools, were recruited for this study. The participants were evenly distributed in terms of gender and grade (k-5), including a diverse sample representing various socio-economic, ethnic, racial, and linguistic backgrounds.
Materials
Board Game: A custom-designed board game was used, which integrates fundamental computer science concepts such as algorithms, commands, loops, and debugging. The game involves moving pieces, command cards, and task cards, introducing CS problems that become progressively more difficult.
Observation Checklist: Researchers will use this to record observed problem-solving strategies and collaborative behaviors during gameplay.
Procedure
Instructions: The rules and objectives of the board game will be clearly explained to the students. An example task was demonstrated to ensure understanding.
Gameplay Session: Students work individually to play the game for 15-30 minutes. Researchers observed each group, noting problem-solving strategies, debugging methods, and any challenges faced using the observation checklist.
Data Analysis
Data from the game was analyzed using a regression analysis to determine the relationship between strategy use, debugging practices, grade and the students' computer science knowledge. Observation data will be categorized based on different problem-solving strategies, debugging methods, and challenges to understand the most prominent methods employed by the students during gameplay.
Ethical Considerations
Consent was obtained from both school authorities and parents/guardians for each participating student. Students could opt-out of the study at any point without any repercussions, and two chose to do so.
What strategies are elementary students using when solving a spatial programming challenge?
We identified four strategies: Counting/Touching spaces, Vocalizing, Embodiment, and Moving the play tile
25% were unable to use any strategy.
30% used a signle strategy (and stuck to it)
37.5% students used 2 strategies
7.5% students used more than 2 strategies
What strategies are most predictive of student use of debugging?
Grade level and use of the board were the most predictive strategies.
What strategies are most predictive solving spatial programming challenges?
Grade level and embodiment strategies predicted problem solving.
What are the developmental constraints for strategy use and problem solving in spatial programming challenges?
Grade level as a proxy to developmental level predicted problem solving success, the use multiple of strategies.
The educational significance of examining how elementary students grasp computer science (CS) concepts via a board game provides an important step to understanding how to support elementary students learning in CS. Such a study underscores the potential of innovative teaching methods, suggesting that hands-on, game-based approaches might provide an assessment entry point for younger students, especially in traditionally complex subjects like CS. Early exposure to CS through interactive mediums fosters foundational understanding but also nurtures strategic thinking. Moreover, this method enhances overall student motivation and engagement, presenting an appealing way to introduce CS and potentially bridge gender and socio-economic disparities in the field. The insights can guide curriculum development, emphasizing the need for more attention to age-appropriate problem-solving strategies and influencing teacher training programs to adopt alternative instructional techniques.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2-10.
Piaget, J. (1952). The origins of intelligence in children. New York, NY: International Universities Press.
Yadav, A., Ocak, C., & Oliver, A. (2022). Computational thinking and metacognition. TechTrends, 66(3), 405-411.
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