An Integrated Pedagogical Strategy for Robotics Olympiad Preparation: Task Decomposition, Mechanical Design, and Sensor Placement

Sadakbayeva Akmaral, Адиканова Салтанат, Symbat Nurgaliyeva

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.59941/2960-0642-2026-3-192-205
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Robotics Olympiads provide an authentic environment for developing students’ programming, problem-solving, and engineering-design skills; however, preparation practices frequently emphasize algorithm development while giving less systematic attention to the interaction between software control, mechanical design, and sensor configuration. This study examines an integrated pedagogical strategy for robotics Olympiad preparation that combines task decomposition, analysis of mechanical parameters, deliberate sensor placement, and iterative refinement of robot control. A pedagogical experiment was conducted with 24 students in grades 7–8 who voluntarily enrolled in an elective robotics course and were randomly assigned to a control group and an experimental group (n = 12 in each). Both groups completed the same LEGO Mindstorms EV3 line-following task. The control group followed a programming-focused approach, whereas the experimental group applied the integrated strategy during robot construction and task preparation. Performance was evaluated in terms of task completion accuracy, trajectory stability, execution time, and number of errors. The experimental group achieved higher task accuracy (88.3% vs. 72.5%) and trajectory stability (86.7% vs. 69.8%), while completing the task in less time (11.2 s vs. 14.6 s) and with fewer errors (1.4 vs. 3.1). The reported between-group differences were statistically significant (p < 0.05). These findings suggest that robotics competition preparation can be strengthened when programming instruction is integrated with task decomposition and systematic analysis of mechanical and sensor-related design decisions. The proposed strategy provides a structured framework for connecting programming with engineering reasoning in school robotics training.