This study reports the findings of a program that aims to develop pre-service science teachers’ computational problem-solving skills and views on using information and communications technology in science education. To this end, pre-service science teachers were trained on computational thinking, computational problem solving, designing an algorithm, and Python coding, and then they were asked to solve problem situations determined within the science education program using the computational problem-solving process. The study was conducted in a faculty of education in Turkey and carried out conducted in an elective course in the spring semester of the 2019 - 2020 academic year (in an online platform due to the Covid-19 Pandemic). 38 pre-service science teachers were included in the study. In this process, pre-service science teachers’ conceptual development levels regarding computational thinking and their views regarding the use of ICT in schools were collected quantitatively. The development of computational problem-solving skills of pre-service science teachers was scored by a rubric developed in this study. According to the analyzes, pre-service science teachers increased knowledge of computational thinking (t = -5,969, p = .000), enhanced views regarding the use of ICT in schools (t = -2,436, p = .020), and developed computational problem-solving skills (χ2(2) = 9.000, p = 0,011). These findings have the potential to provide evidence on how computational problem-solving skills can be integrated into science teacher education programs.
The Computational Thinking (CT) teaching approach allows students to practice problem-solving in a way that they can use the Computer Science mindset. In this sense, Collaborative Learning has a lot to contribute to educational activities involving the CT. This article presents the design and evaluation of a Collaborative Learning framework for the development of CT skills in students. To design the proposed strategy, several fundamental features of the Collaborative Learning concept of the literature have been studied and sketched. The strategy was applied to middle school students through a digital games programming workshop. Data were collected by three means: (1) collecting artifacts produced during activities; (2) recording of game programming sessions; and (3) applying a structured interview to students. The data analysis showed evidence that the strategy was able to mobilize Computational Thinking skills in addition to mobilizing collaborative skills in learners.
The digital transformation of teaching processes is guided and supported by the use of technological, human, organizational and pedagogical drivers in a holistic way. Education 4.0 aims to equip students with cognitive, social, interpersonal, technical skills, among others, in the face of the needs of the Fourth Industrial Revolution and global challenges, such as mitigating the causes and effects of climate change based on people's awareness. This work presents the development and experimentation of a method, called TADEO - acronym in Portuguese language to Transformação Digital na Educação (digital transformation in education), to guide the design and application of teaching and learning experiences from groups of drivers of the digital transformation in education, aiming to achieve Education 4.0 objectives. The TADEO method was applied in the context of classes of basic subjects of elementary and higher education to increase students' understanding of climate change through the development of projects to mitigate environmental problems caused by anthropogenic action and, at the same time, exercise students the soft and hard skills required by 21st century learning and work. The results of the evaluations of students and educators participating in the teaching and learning experiences guided by the TADEO method point to the achievement of the expected purposes.
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/diagnostic 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.
Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine learning model developed using ongoing formative assessment scores as indicators of student engagement. Visualisation of the classification tree results is employed as student engagement indicators for instructors to observe and intervene with students. The results of this study showed that ongoing assessment is related to student engagement and subsequent final programming exam performance and possible to identify students at-risk of failing the final exam. Finally, our study identified students impacted by the Covid-19 pandemic. These were students who attended the final programming exam in the semester 2019-2020 and who scored well in formative assessments. Based on these results we present a simple student engagement indicator and its potential application as a student progress monitor for early identification of students at risk.