The article describes a study carried out on pupils aged 12-13 with no prior programming experience. The study examined how they learn to use loops with a fixed number of repetitions. Pupils were given a set of programming tasks to solve, without any preparatory or accompanying instruction or explanation, in a block-based visual programming environment. Pupils’ programs were analyzed to identify possible misconceptions and factors influencing them. Four misconceptions involving comprehension of the loop concept and repeat command were detected. Some of these misconceptions were found to have an impact on a pupil’s need to ask the computer to check the correctness of his/her program. Some of the changes made to tasks had an impact on the frequency of these misconceptions and could be the factors influencing them. Teachers and course book writers will be able to use the results of our research to create an appropriate curriculum. This will enable pupils to acquire and subsequently deal with misconceptions that could prevent the correct understanding of created concepts.
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).
Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.
In a previous publication we examined the connections between high-school computer science (CS) and computing higher education. The results were promising—students who were exposed to computing in high school were more likely to take one of the computing disciplines. However, these correlations were not necessarily causal. Possibly those students who took CS courses, and especially high-level CS courses in high school, were already a priori inclined to pursue computing education. This uncertainty led us to pursue the current research. We aimed at finding those factors that induced students to choose CS at high school and later at higher-education institutes. We present quantitative findings obtained from analyzing freshmen computing students' responses to a designated questionnaire. The findings show that not only did high-school CS studies have a major impact on students’ choice whether to study computing in higher education—it may have also improved their view of the discipline.
This paper presents an educational setting that attempts to enhance students’ understanding and facilitate students’ linking-inferencing skills. The proposed setting is structured in three stages. The first stage intends to explore students’ prior knowledge. The second stage aims to help students tackle their difficulties and misconceptions and deepen their understanding of the topics under study. This is attempted through individual student engagement in suitably-designed activities and relative feedback. As recorded in previous research, students’ difficulties feedback on the material development. The third stage of the educational setting exploits social interaction to help students reorganize their knowledge of the concepts under study. The web-based application of the proposed educational setting indicated improvement in first-year Computer Science (CS) students’ understanding of fundamental Computer Architecture concepts and progress in students’ linking-inference skills. These results encourage integration in the instructional process of interventions designed according to the proposed setting in order to support and enhance students’ understanding of troublesome concepts and their interrelations.
The teaching and learning of programming has proven to be a challenge for students of computer courses, since it presents challenges and requires complex skills for the good development of students. The traditional teaching model is not able to motivate students and arouse their interest in the topic. The tool proposed herein, the REA-LP, aims to facilitate the study and retention of content related to the discipline of programming logic at the technical level by presenting its content through various types of media, in addition to allowing students to actively participate in the construction of their knowledge, favoring engagement and motivation. From the results of an empirical study with 39 students, it can be concluded that the tool was very well accepted, being effective in facilitating and assisting participants in their learning, motivation, and interest in classes, mainly due to the way in which the content is presented by REA-LP.
Nowadays, few professionals understand the techniques and testing criteria to systematize the software testing activity in the software industry. Towards shedding some light on such problems and promoting software testing, professors in the area have established Massive Open Online Courses as educational initiatives. However, the main limitation is the professor’s lack of supervision of students. A conversation agent called TOB-STT has been defined in trying to avoid the problem. A previous study introduced TOB-STT; however, it did not analyze its efficacy. This article addresses a controlled experiment that analyzed its efficacy and revealed it was not expressive in its current version. Therefore, we conducted an in-depth analysis to find what caused this result and provided a detailed discussion. The findings contribute to the TOB-STT since the experimental results show that improvements need to be made in the conversational agent before we use it in Massive Open Online Courses.
The purpose of this study is to reveal the status of scientific publications on learning analytics from the past to the present in terms of bibliometric indicators. A total of 659 publications on the subject between the years 2011-2021 were found in the search using keywords after various screening processes. Publications were revealed through descriptive and bibliometric analyses. In the study, the distribution of publications by years and citation numbers, the most published journals on the subject, the most frequently cited publications, the most prolific countries, institutions and authors were examined. In addition, the cooperation between the countries, authors and institutions that publish on the subject was mentioned and a network structure was created for the relations between the keywords. It has been determined that research in this field has progressed and the number of publications and citations has increased over the years. As a result of the bibliometric analysis, it was concluded that the most influential countries in the field of learning analytics are the USA, Australia and Spain. The University of Edinburgh and Open University UK ranked first in terms of the number of citations and Monash University as the most prolific institutions in terms of the number of publications. According to the keyword co-occurrence analysis, educational data mining, MOOCS, learning analytics, blended learning, social network analysis keywords stand out in the field of learning analytics.
Industry 4.0 technologies are being applied in the teaching and learning process, called Education 4.0. However, there is no specification of what is being considered when developing technologies for education in the 4.0 context. Therefore, we performed a Systematic Mapping Study to investigate the information and communication technologies (ICTs) proposed to Education 4.0. From a search in four search engines, 81 articles had data extracted. The results elucidated aspects considered as Education 4.0, such as contextualized learning and student-centered learning. Besides, some applied ICTs are not in agreement with the ICTs considered as 4.0 in the literature, the focus on ICTs to engineering education and to be applied to higher education. As implications of the results obtained, it is necessary to understand why some ICTs are not aligned with 4.0 literature and apply these ICTs in knowledge areas beyond STEM.