Abstract:

This study presents an approbation of an adaptive methodology for assessing STEM competencies in early primary education. The assessment approach is grounded in a previously developed taxonomic framework (Kaloyanova & Papancheva, 2023), designed to support the construction of adaptive metric systems for evaluating STEM learning outcomes.

The current research focuses on the implementation of specific assessment instruments applied in an experimental study with second-grade students from two schools. The students participate in extracurricular robotics clubs, where they engage with floor robots such as Bee-Bot and Ozobot, developing key competencies related to spatial orientation and algorithmic thinking.

To evaluate the extent to which instructional goals have been achieved, a set of assessment tools was developed, integrating the use of a different type of floor robot – Cubetto – with an alternative control mechanism. The tasks are structured with increasing levels of complexity and are designed to assess key STEM competencies, including problem-solving, algorithmic thinking, spatial reasoning, and the ability to transfer knowledge across subject domains.

The collected data allow for a comparative analysis of student performance and the effectiveness of pedagogical practices in both schools. The findings contribute to validating the applicability of the proposed taxonomic framework in real classroom conditions and demonstrate concrete adaptive assessment tools for young learners in STEM education.

Keywords:

STEM competencies, adaptive assessment framework, early primary education, robotics in education, problem-solving, STEM education.