Abstract:

Computational Thinking (CT) has been establishing itself as a relevant transversal skill in mathematics education, associated with problem-solving, logical reasoning, and self-regulated learning. Some authors state that, linked to problem-solving, logical reasoning, and the ability to formulate, test, and reformulate solutions systematically, CT has been emerging as a relevant transversal skill in the educational context. In mathematics education, the mobilization of CT is pertinent, as it involves cognitive processes such as decomposition, pattern recognition, abstraction, algorithmic thinking, and debugging—all intrinsic to mathematics. Researchers reinforce the need for pedagogical practices that support the development of CT in STEM contexts, including task design, teacher mediation, and diverse learning environments. In parallel, some researchers recognize feedback as one of the most influential factors in learning, shaping how students interpret mistakes, adjust strategies, and regulate their performance. Feedback can take different forms: human feedback, H—from teachers, T, and peers, P—and digital feedback, D. Feedback plays a central role in teaching and learning processes, influencing how students interpret errors, adjust their strategies, and engage in tasks. This study examines students' opinions in the 2nd CEB (ages 10-12) regarding different types of feedback (from T, P, and D-platforms) and how these opinions relate to their performance on tasks that develop CT. This research employs a qualitative, descriptive methodology, as we are still in the preliminary phase of analyzing students' opinions on the implementation of CT tasks in the classroom. Six mathematical tasks were implemented, each developed in two modalities—unplugged and plugged in—using digital tools: Scratch, GeoGebra, and Excel. Data collection included direct classroom observation, logbook entries, audio recordings of sessions, and multimodal narratives. Data analysis was performed through the systematic coding of feedback episodes, using a matrix that cross-classifies CT dimensions and feedback types (T, P, and D-platform), enabling us to relate observed evidence, performance, and student opinions. The interim research results allow us to identify trends in the roles of H and D feedback in mobilizing learning capacity (LC) for task completion. Students recognize different types of feedback and assign distinct functions to them in the learning process. T-feedback is valued for its clarity, guidance, and validation of strategies, contributing to confidence and focus on the task. The sharing of strategies, the discussion of errors, and the negotiation of meanings distinguish P-feedback, thereby promoting engagement and persistence. D-feedback is perceived primarily as implicit, associated with trial-and-error solutions, solution validation, and self-regulation. Furthermore, it fosters the autonomy of some students. The results indicate that H and D-feedback play distinct but complementary roles in mobilizing the dimensions of LC and in student performance, and reinforce the importance of pedagogical practices that integrate different forms of feedback in mathematics teaching.

Keywords:

Tasks, Computational Thinking, Student Performance, Human Feedback, Digital Feedback, Students' opinions.