Integrating computational thinking into K-12 Education has been a widely explored topic in recent years. Particularly, effective assessment of computational thinking can support the understanding of how learners develop computational concepts and practices. Aiming to help advance research on this topic, we propose a data-driven approach to assess computational thinking concepts, based on the automatic analysis of data from learners’ computational artifacts. As a proof of concept, the approach was applied to a Massive Open Online Course (MOOC) to investigate the course’s effectiveness as well as to identify points for improvement. The data analyzed consists of over 3300 projects from the course participants, using the Scratch programming language. From that sample, we found patterns in how computational thinking manifests in projects, which can be used as evidence to guide opportunities for improving course design, as well as insights to support further research on the assessment of computational thinking.
This paper proposes and validates a short and simple Expectancy-Value-Cost scale, called EVC Light. The scale measures the motivation of students in computing courses, allowing the easy and weekly application across a course. One of the factors related directly to the high rate of failure and dropout in computing courses is student motivation. However, measuring motivation is complex, there are several scales already carried out to do that job, but only a few of them consider the longitudinal follow-up of motivation throughout the courses. The EVC Light was applied to 245 undergraduate students from four universities. The Omega coefficient, scale items intercorrelation, item-total correlation, and factor analysis are used to validate and measure the reliability of the instrument. Confirmatory and exploratory factor analyses supported the structure, consistency, and validity of the EVC Light scale. Moreover, a significant relationship between motivation and student results was identified, based mainly on the Expectancy and Cost factors.
Research trends on computational thinking (CT) and its learning strategies are showing an increase. The strategies are varying, for example is using games to provide enjoyment, engagement, and experience. To improve the high level of immersion and presence of game objects, learning strategies through games can be improved by virtual reality (VR) technology and its application. However, a systematic review that specifically discusses game based in VR (GBiVR) settings is lacking. This paper reports previous studies systematically about the strategies used to learn CT through games and VR applications. 15 papers were selected through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. As the result, this study proposes a conceptual framework for designing a strategy to learn CT through GBiVR settings. The framework consists of critical aspects of variables that can be considered in the learning environment like game elements, VR features, and CT skills. All the aspects are discussed below.
With the growing search for qualified professionals in the exact area, teaching in STEM (Science, Technology, Engineering, and Mathematics) areas is gaining importance. In parallel, it appears that drones are an increasingly present reality in the civil area; however, there are few scientific studies of their application in the pedagogical environment, and their insertion is still practically nil in the school environment. Thus, this work aims to analyze the feasibility of using a set of technologies based on drones, designed based on the theory of significant learning through the use of active methodologies. The study was carried out with 30 high school students and followed a line of quali-quantitative analysis, in which the quantitative data were collected from the results obtained in a pre and post-test and the qualitative ones through recordings during the interventions, observations of the researcher, and a semi-structured press interview. Finally, a triangulation between the methodologies was carried out, looking for congruent aspects between the different techniques used. As a result, it was found that the workshops with the platform based on drones helped in the understanding, construction, and interpretation of the content covered, and it can be concluded that there is a significant relationship between the use of the technological set proposed in the pedagogical process and the possibility of significant learning in the STEM areas by the students.
Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.
Computing science which focuses on computational thinking, has been a compulsory subject in the Thai science curriculum since 2018. This study is an initial program to explore how and to what extend computing science that focused on STEM education learning approach can develop pre-service teachers' computational thinking. The online STEM-based activity-Computing Science Teacher Training (CSTT) Program was developed into a two-day course. The computational thinking test (CTT) data indicated pre-service teachers’ fundamental skills of computational thinking: decomposition, algorithms, pattern recognition, pattern generalization and abstractions. The post-test mean score was higher than the pre-test mean score from 9.27 to 10.9 or 13.58 percentage change. The content analysis indicated that there were five key characteristics founded in the online training program comprised: (1) technical support such as online meeting program, equipment, trainer ICT skills (2) learning management system such as Google Classroom, creating classroom section in code.org (3) the link among policy, curriculum and implementation (4) pre-service teachers' participation and (5) rigor and relevance of how to integrate the applications of computing science into the classroom.
Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the static modifier. Novices encounter difficulty understanding the means and implications of static attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of understanding static, and (b) a collection of questions designed to serve as a learning/practice/di-agnostic tool to address those aspects. Providing answers requires learners to apply higher-order cognitive skills and, hence, to advance their understanding of the essential meaning of the concept. Each question is analyzed according to three characteristics: (a) the static aspects that the question examines according to a detailed classification the paper provides; (b) identification of the question according: to Bloom’s revised taxonomy, to the Structure of Observed Learning Outcome (SOLO) taxonomy; and to the problem-solving keywords used in the question's formulation. Several recommendations for teaching are presented.
The purpose of the study is to examine the moderating effect of age on gender differences in teachers’ self-efficacy for using information and communication technology (ICT) in teaching as well as possible variables underlying this effect. Following Bandura’s conceptualisation of self-efficacy, we defined teachers' self-efficacy as their confidence in performing specific tasks that require the integration of ICT into the teaching practice. The study was conducted via an online questionnaire on a sample of 6613 elementary and upper secondary school teachers in Croatia. The hierarchical multiple regression analysis was applied. The findings indicate minor gender differences in self-efficacy for using ICT that are more prominent among older teachers and practically non-existent among younger teachers. These effects remain statistically significant after controlling for the type of school where the teacher works, perceived technical and professional support for using ICT in school, and frequency of use of computer programmes in teaching. The interaction effect ceases to be statistically significant after the introduction of length of computer use in teaching and/or attitudes towards computers in the model, indicating that these two variables have a role in low self-efficacy for using ICT among older female teachers. A similar level of self-efficacy for using ICT among young male and female teachers is an encouraging finding which could hopefully be followed by gender equality in other aspects of ICT use. The findings suggest that strategies for enhancing ICT self-efficacy should be particularly targeted at older female teachers. This study contributes to a better understanding of the underresearched topic of gender differences in teacher’s ICT self-efficacy.