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In university-level education, the computer network is a mandatory course for computer science and engineering majors. A computer network course provides theoretical and hands-on foundations for network connectivity, network topologies, internet protocols, networking devices, and gateways. In the traditional classroom, students face challenges in gaining essential knowledge and self-efficacy. Teachers face challenges in assessing students' quality of knowledge due to the abundance of online computer science teaching resources. Chatbot technologies have the potential to overcome these challenges and enhance essential knowledge and self-efficacy. However, the argument remains whether a course-specific standard chatbot or a generalizable chatbot could produce high-quality answers. Therefore, in this paper, we research 1) to what extent does using chatbots improve higher education students' essential knowledge of computers network? and 2) to what extent does using chatbots improve the self-efficacy of higher education students? Our first insight indicates that groundbreaking generalizable chatbots such as You and ChatGPT produce rather detailed answers to the questions associated with computer network courses, but those answers may not be the model answers that teachers expect from the students. Hence, research-based course-specific standard chatbots are required to teach computer network topics effectively. [ABSTRACT FROM AUTHOR]

• Creative learning in computer science is necessary and important to define. • The variable creative learning in computing needs to be operationalized and measured through universal metrics and scales. • The development of creative learning in computer science does not depend entirely on the curriculum but on how it is taught. The training of creative computer professionals is considered a strategic process in different countries because it is essential for the computerization of society. This paper proposes an approach to the study of creative computer science learning for the digital transformation of modern society. The aim of the research was to evaluate the creative learning of computer science during the training of computer science professionals. An instrument derived from previous research and metrics was applied to evaluate creative learning in the two groups of terminal years of the computer engineering course at the University of Matanzas with a mixed paradigm. The quantitative analysis diagnosed that more than sixty-five percent of the students are evaluated as poor and fair in both years. The qualitative analysis revealed the inadequacies of the teaching staff in the use of computer science teaching approaches, and the four stages of formation of creative learning in computer science in the degree course: pre-university stage, basic stage, professional stage and research stage. They also revealed three key factors in the formation of creative learning in computer science: the integration of subjects, the solution of real projects, and the mainly emotional support provided by teachers and other professionals to students during their integration into the computerization of organizations. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]

With current developments in robotics and artificial intelligence (AI) comes an increased focus on the ethical production and use of such novel technologies. Accordingly, a trend toward "embedded ethics" is seen in recent research, reflecting an increase in efforts to integrate social and ethical considerations in computer science education and early in the development phases of AI and robotics. What remains to be established, however, is a more concrete understanding of the best working modalities for such interdisciplinary collaborations. In this brief discussion paper, we provide reflections derived from our interdisciplinary Responsible Robotics project which integrates ethicists and social scientists at early stages of development. We put forward several suggestions on how to integrate ethics and social science within technological development. We believe these suggestions can serve as a working taxonomy of best practices for embedded ethics. [ABSTRACT FROM AUTHOR]

A new school subject, Leaving Certificate Computer Science, was introduced in September 2018 into Irish schools in a period of curriculum reform and change. Drawing on concepts of teacher agency and learner agency, and viewed through a figurational sociology lens, this paper explored the realities of teaching LCCS and the role teacher agency and learner agency played in such enactment. Through a case study design, four teachers were interviewed in a focus group semi-structured manner. To delve deeper into the pedagogical approaches used in LCCS, a further one teacher and their nine students were interviewed. The findings highlight an influential relationship (positive and negative, encouraging and discouraging) between teachers' agentic decisions in their process of achieving teacher agency and the students process of achieving learner agency. The paper offers a figurational viewpoint of the relationship between teacher agency and learner agency through a constructed Figure. We explicitly argue that the responsibility should not be placed on the teacher and student in achieving agency (as a product) but it is a combination of the teacher and the students and the other interdependent relationships within their figurations, alongside contextual factors, and educational social processes, which determine the fluctuation of the process of achieving teacher and learner agency. The paper provides considerations for computer science teacher education. • Explores the relationship between teacher agency and learner agency in Computer Science subject. • Uses figurational sociology to further understand the process of achieving agency. • Qualitative research study. • Considerations provided for Computer Science teacher education. [ABSTRACT FROM AUTHOR]

Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. One reason for this is that teaching the subject of software testing is difficult as it is a complex intellectual activity for which students need to allocate multiple cognitive resources at the same time. A myriad of primary and secondary studies have tried to solve this problem in education, however still with very limited results. Before we can design interventions to improve our pedagogical approaches, we need to gain more in-depth understanding and recognition of sensemaking as it is happening when students design test cases. An initial exploratory study identified four different sensemaking approaches used by students while creating test models. In this paper we present a follow-up study with 50 students from a large university in Spain. The used methodology was based on the previous study with the improvements that originated from its evaluation. We asked the participants to create a test model based on a description of a test problem using a specialized web-based tool for modeling test cases. We measured how well these models fit the test problem, the sensemaking process that students went through when creating the models, and the students' perception of the modeling task. The participants received no compensation for their efforts, and we scheduled the experiment during a regular class. Apart from the created models and their metadata, we also collected recordings of the students' computer screens made during the experiment and used a questionnaire to study their perspectives on the assignment. All the collected textual, graphical, and video data was analyzed using an iterative inductive analysis process to allow new information about the different sensemaking approaches to emerge. We gained better insights into the sensemaking processes of students while modeling test cases for a problem. The results enabled us to refine our previous findings, and we identified new sensemaking approaches. Based on these results, we can further investigate ways to influence the sensemaking process in education, the possible misconceptions that have a negative influence on it, and the desired mental model we want our students to have to design test cases. [ABSTRACT FROM AUTHOR]

• This systematic review examined how CS education was implemented in schools and its efficacy for developing students' computational thinking (CT). • Although educational interventions have not always been successful in CS education, this review provides strong evidence that overall, CS education promotes the development of students' CT in the K-12 setting while improving their creative and critical thinking skills. • When integrated into other subjects, CS education increases students' motivation, commitment, participation, and interest in the subject matter. • We recommend early access to CS education, various innovative instructional approaches to CS education and appropriate support and guidance for student learning. Also, there is a need for professional development opportunities for CS educators. This systematic review examined how computer science (CS) education was implemented in schools and its efficacy for developing students' computational thinking (CT). Sixty-six papers were selected for this systematic review and analyzed for patterns in relation to the implementation of CS education in K-12 schools and its impact on the development of students' CT skills. Although educational interventions have not always been successful in CS education, this review provides strong evidence that overall, CS education promotes the development of students' CT in the K-12 setting while improving their creative and critical thinking skills. We recommend early access to CS education, various innovative instructional approaches to CS education and appropriate support and guidance for student learning. [ABSTRACT FROM AUTHOR]