Assessing implicit computational thinking in Zoombinis puzzle gameplay

Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners' CT test (BCTt) and the competent CT test (cCTt)

Introduction

With the increasing amount of research around Computational Thinking (CT) and endeavors introducing CT into curricula worldwide, assessing CT at all levels of formal education is of utmost importance to ensure that CT-related learning objectives are met. This has contributed to a progressive increase in the number of validated and reliable CT assessments for K-12, including primary school. Researchers and practitioners are thus required to choose among multiple instruments, often overlapping in their age validity.

Methods

In this study, we compare the psychometric properties of two of these instruments: the Beginners' CT test (BCTt), developed for grades 1-6, and the competent CT test (cCTt), validated for grades 3-4. Classical Test Theory and Item Response Theory (IRT) were employed on data acquired from 575 students in grades 3-4 to compare the properties of the two instruments and refine the limits of their validity.

Results

The findings (i) establish the detailed psychometric properties of the BCTt in grades 3-4 for the first time, and (ii) through a comparison with students from the same country, indicate that the cCTt should be preferred for grades 3-4 as the cCTt is able to discriminate between students of low and medium ability. Conversely, while the BCTt, which is easier, shows a ceiling effect, it is better suited to discriminate between students in the low ability range. For these grades, the BCTt can thus be employed as a screening mechanism to identify low ability students.

Discussion

In addition to providing recomendations for use of these instruments, the findings highlight the importance of comparing the psychometric properties of existing assessments, so that researchers and practitioners, including teachers and policy makers involved in digital education curricular reforms, may take informed decisions when selecting assessments.

Assessing Algorithmic Thinking Skills in Relation to Age in Early Childhood STEM Education

In the modern digital era, intensive efforts are made to inject computational thinking (CT) across science, technology, engineering, and mathematics (STEM) fields, aiming at formulating a well-trained citizenry and workforce capable of confronting intricate problems that would not be solvable unless exercising CT skills. Focusing on contributing to the research area of CT assessment in the first two years of primary school, we investigated the correlation of algorithmic thinking skills, as a fundamental CT competency, with students’ age in early childhood settings. This article reports a relevant research study, which we implemented under the umbrella of quantitative methodology, employing an innovative assessment tool we constructed for serving the needs of our study. The research was conducted within the context of the environmental study course, adding to the efforts of infusing CT into STEM fields. The study results shed light on the correlation between algorithmic thinking skills and age in early childhood, revealing that age is a predictor factor for algorithmic thinking and, therefore, for CT.

Processing Analysis of Swift Playgrounds in a Children’s Computational Thinking Course to Learn Programming

Computational thinking courses can cultivate students’ ability to apply logic in the fields of mathematics and information science. The new 12-year Basic Education Curriculum Guidelines were implemented in Fall 2019 in Taiwan. Courses on computational thinking, problem solving, and programming are contained in the technology education field in junior and senior high schools. Swift Playgrounds is an innovative app for the iPad and Mac that makes learning Swift interactive and fun. No programming knowledge is required to use Swift Playgrounds, making it very suitable for beginners. This study was carried out by letting elementary school teachers and students participate in Swift Playgrounds computational thinking courses. By trying this app, teachers of different disciplines attempted to realize more learning situations. Students learned how to cope with functions and loop skills by playing with “Byte”, which is a character in Swift Playgrounds. There were three purposes for this study: first, designing a computational thinking course for the most basic part, “Hello! Byte”, in Swift Playgrounds; second, assigning elementary school teachers to assess the qualitative analysis of tasks in Swift Playgrounds; and third, assigning elementary school students to do the tasks and assign a difficulty index in Swift Playgrounds after learning with this app. The results show that most teachers considered this approach to be able to improve logical thinking and inferential capability after assessing, and most students considered functions and loops quite difficult after using the app. According to the students’ indices, about 86 percent of students considered that adding commands is easy, and about 37 percent of students considered that functions are easy. On the other hand, about 24 percent of students considered that applying the Slotted Stairways is difficult, and about 34 percent of students considered that using loops is hard. It is suggested that more instructions for the course or extendibility for classes is required.