Computational Thinking (CT) has emerged in recent years as a thematic trend in education in many countries and several initiatives have been developed for its inclusion in school curricula. There are many pedagogical strategies to promote the development of elementary school students’ CT skills and knowledge. Unplugged learning tasks, block-based programming projects, and educational robotics are 3 of the most used strategies. This paper aimed to analyze the effect of Scratch-based activities, developed during one scholar year, on the computational thinking skills developed and concepts achieved by 4th-grade students. The study involved 189 students from two school clusters organized into an experimental group and a control group. To assess students’ computational knowledge, the Beginners Computational Thinking Test developed by Several Zapata-Cáceres et al. (2020) was used. The results indicate statistically significant differences between the groups, in which students in the experimental group (who performed activities with scratch) scored higher on the test than students in the control group (who did not use Scratch).
In this study we investigate the effects of long-term technology enhanced learning (TEL) in mathematics learning performance and fluency, and how technology enhanced learning can be integrated into regular curriculum. The study was conducted in five second grade classes. Two of the classes formed a treatment group and the remaining three formed a control group. The treatment group used TEL in one mathematics lesson per week for 18 to 24 months. Other lessons were not changed. The difference in learning performance between the groups tested using a post-test; for that, we used a mathematics performance test and a mathematics fluency test. The results showed that the treatment group using TEL got statistically significantly higher learning performance results compared to the control group. The difference in arithmetic fluency was not statistically significant even though there was a small difference in favor of the treatment group. However, the difference in errors made in the fluency test was statistically significant in favor of the treatment group.
The aim of this study is to develop a self-efficacy measuring tool that can predict the computational thinking skill that is seen as one of the 21st century's skills. According to literature review, an item pool was established and expert opinion was consulted for the created item pool. The study group of this study consists of 319 students educated at the level of secondary school. As a result of the exploratory factor analysis, the scale consisted of 18 items under four factors. The factors are Reasoning, Abstraction, Decomposition and Generalization. As a result of applied reliability analysis, the Cronbach Alpha reliability coefficient can be seen to be calculated as .884 for the whole self-efficacy scale consisting of 18 items. Confirmative factor analysis results and fit indexes were checked, and fit indexes of the scale were seen to have good and acceptable fits. Based on these findings, the Computational Thinking Self-efficacy Scale is a valid and reliable tool that may be used in measuring to predict Computational Thinking.
The use of computers as teaching and learning tools plays a particularly important role in modern society. Within this scenario, Brazil launched its own version of the 'One Laptop per Child' (OLPC) program, and this initiative, termed PROUCA, has already distributed hundreds of low-cost laptops for educational purposes in many Brazilian schools. However in spite of the numerous studies conducted in the country since PROUCA was launched, Brazil shows a lack of proficiency in basic information crucial for managing and improving any OLPC initiative (e.g., number of effectively used laptops, use time and distribution per subject, use location and school performance of users, and others). Therefore, the focus of this article is to introduce MEMORE, a computational environment for longitudinal on-line data collection, integration and an analysis of how PROUCA laptops are used by schools. Technical details about MEMORE's architecture, database and functional models are supplied and the results from real data collected from Brazilian public schools are presented and analyzed. They elucidate how MEMORE can be a valuable management tool in OLPC contexts.
Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document classification techniques. Firstly, we propose two classification models for cheating detection by using a decision tree supervised algorithm. Then, both classifiers are compared against the result produced by a domain expert. The results point out that one of the classifiers achieved an excellent quality in detecting and evaluating cheating in exams, making possible its use in real school and college environments.