An intelligent model to assess the level of the computational algorithmic component of students’ computational thinking

Abstract

The importance of developing students’ computational thinking (CT) determines the need to devise an objective and valid method for assessing its level. In this regard, it is relevant to build a universal model for diagnosing computational thinking on the basis of mathematical methods. The article aims to develop a model for diagnosing students’ computational algorithmic thinking. This model makes it possible to automate assessment procedures and contributes to developing techniques for enhancing this cognitive ability of learners. The diagnostic evaluation of students’ computational algorithmic component of CT includes an assessment of their subject knowledge, cognitive abilities, and cognitive activity. In this regard, we identified three diagnostic criteria. The content criterion (CC) is determined by the knowledge acquired through subject-specific learning. The operational criterion (OC) is a set of identified intellectual strategies necessary to solve problems using computers. The cognitive activity criterion (CAC) is a cognitive-psychological response to the cognitive process. It is manifested in the intensification of learning activity, a strong focus on a proactive attitude, independence, and a creative approach to learning. It is necessary to establish indicators to provide three assessment levels for each criterion, namely low, medium, and high. The integrated assessment of the level of the computational algorithmic component of a student’s CT is determined by the total of these three criteria values. For the adopted diagnostic model, we have developed a method based on cluster analysis to determine the CT level. The object of recognition is a learner with an information vector (CC, OC, CAC). The weights of the vector elements are determined by experts. The array of students with their information vectors is divided into three classes: L1 (low level), L2 (medium level), L3 (high level). The diagnostics of the computational algorithmic component level of a particular student’s CT consists in determining their belonging to the corresponding group. The method devised to assess the level of the computational algorithmic component of students’ CT was implemented as a software product. The proposed original model for diagnosing students’ CT provides an opportunity to automate assessment procedures. It also facilitates the integrated assessments of the diagnostic characteristics of the learning process. The materials of this work are of interest to teachers and employees of the educational departments of universities, and also have theoretical significance for the development of the theory and practice of diagnosing educational results.