This full research paper presents a systematic literature review of research using machine learning techniques to predict student performance in introductory programming courses. The overarching research question is: How does empirical research using machine learning approach the prediction of student performance in introductory computer science courses (CS1)? The focus is on how knowledge from educational science is incorporated alongside with ethical and gender considerations. Only peer-reviewed articles, published in journals or conference proceedings between 2017 and mid 2020, reporting on empirical studies that used data on more than 30 students are included. This study addresses prevalent shortcomings in empirical CS education research, noting often inadequate descriptions of data selection, processing, and the representation and diversity of sample sizes that can limit the utility of results. It underscores the frequent omission of ethical considerations regarding students' data consent and the potential negative impacts on students' educational trajectories. Additionally, many studies fail to incorporate the educational context or address gender-related issues adequately, disconnecting the models from established knowledge about women in computer science.

This full research paper explores how novice computer science and information technology students at two Swedish universities describe the subjects of computer science and programming. Computer science is a young, dynamic and multifaceted discipline and is currently undergoing a paradigm shift, mainly due to the rapid development of Artificial Intelligence and its connection to society and individuals. Computer science is becoming integrated into elementary school curricula and these aspects combined make it important to investigate how students perceive the subject. Reflective texts from 133 computer science and information technology students have been analysed using inductive content analysis and statistically significant differences among students have been investigated using Chi-Square analysis. The results show varying perceptions of the field, from quite basic and narrow views to more nuanced understandings that encompass its multidisciplinary nature and societal implications. Interestingly, female students were more inclined to consider the societal implications of computer science, suggesting potential avenues for enhancing educational strategies and fostering gender diversity within the field. The results in this study highlight the importance of continuous investigation into students' perceptions to guide educational approaches, promote inclusivity, and cultivate diverse perspectives within the ever-evolving discipline of computer science.

This is a full research paper. Computer science is a young, dynamic and rapidly evolving discipline. Content knowledge has been the focus for higher education in the subject but none-technical skills have gained increasing attention. To develop competence in the discipline, dispositions are also needed, this aspect is however new to computer science education. This study explores novice computer science students' perceptions of essential competencies for success in the field. Reflective texts from 133 students at two Swedish universities collected during their first week have been qualitatively analysed using the Competency learning framework (CoLeaF) and tested for statistically significant differences within the student group using a Chi-Square analysis. CoLeaF describes a competence in terms of its dispositional, skill and knowledge elements. Students were primarily focusing on disposition and skill elements. Motivation, perseverance and creativity were the most common dispositions and skills related to problem solving and logic were most prominent, followed by collaboration and communication. Math was the predominant knowledge element reflected on, partic-ularly by students with more prior programming experience. Differences emerged between legal genders, with females emphasizing curiosity, being structured, having a goal, and being challenged more than males. The study highlights a need for CS education to cultivate both technical expertise and non-technical qualities alongside a deeper understanding of ethical and societal responsibilities within the field.

ABSTRACT

The growing complexity of software employed in the cyber-physical domain is calling for a thorough study of both its functional and extra-functional properties. Ethical aspects are among important extra-functional properties, that cover the whole life cycle with different stages from design, development, deployment/production to use of cyber physical systems. One of the ethical challenges involved is the question of identifying the responsibilities of each stakeholder associated with the development and use of a cyber-physical system. This challenge is made even more pressing by the introduction of autonomous increasingly intelligent systems that can perform functionalities without human intervention, because of the lack of experience, best practices and policies for such technology. In this article, we provide a framework for responsibility attribution based on the amount of autonomy and automation involved in AI based cyber-physical systems. Our approach enables traceability of anomalous behaviors back to the responsible agents, be they human or software, allowing us to identify and separate the "responsibility" of the decision-making software from human responsibility. This provides us with a framework to accommodate the ethical "responsibility" of the software for AI based cyber-physical systems that will be deployed in the future, underscoring the role of ethics as an important extra-functional property. Finally, this systematic approach makes apparent the need for rigorous communication protocols between different actors associated with the development and operation of cyber-physical systems that further identifies the ethical challenges involved in the form of group responsibilities.

Information and Communication Technology (ICT) has led to an unprecedented development in almost all areas of human life. It forms the basis for what is called "the cognitive revolution" -- a fundamental change in the way we communicate, feel, think and learn based on an extension of individual information processing capacities by communication with other people through technology. This so-called "extended cognition" shapes human relations in a radically new way. It is accompanied by a decrease of shared attention and affective presence within closely related groups. This weakens the deepest and most important bonds, that used to shape human identity. Sustainability, both environmental and social (economic, technological, political and cultural) is one of the most important issues of our time. In connection with "extended cognition" we have identified a new, basic type of social sustainability that everyone takes for granted, and which we claim is in danger due to our changed ways of communication. We base our conclusion on a detailed analysis of the current state of the practice and observed trends. The contribution of our article consists of identifying cognitive sustainability and explaining its central role for all other aspects of sustainability, showing how it relates to the cognitive revolution, its opportunities and challenges. Complex social structures with different degrees of proximity have always functioned as mechanisms behind belongingness and identity. To create a long-term cognitive sustainability, we need to rethink and design new communication technologies that support differentiated and complex social relationships.

Informational structural realism, ISR (Floridi, 2008a), describes the reality as a complex informational structure for an epistemic agent interacting with the universe by the exchange of data as constraining affordances. In conjunction with naturalist computationalism – the view that the dynamics of the nature can be understood as computation – Floridi's ISR presents a basis for the construction of the unified framework of info-computationalism. In this framework, the fundamental mechanism of all natural computation is morphological computation, expressed as a process of information self-organisation, with information structure understood in the sense of Floridi's ISR. Recently, in robotics, morphological computing has been used for decentralised embodied control of robots. In this article, we describe how appropriate body morphology saves information-processing (computation) resources as well as enables learning through self-structuring of information in an epistemic, cognizing agent.

Robots do not have any capability of taking moral responsibility. At the same time industrial robotics is entering a new era with "intelligent" robots sharing workbench with humans. Teams consisting of humans and industrial robots are no longer science fiction. The biggest worry in this scenario is the fear of humans losing control and robots running amok. We believe that the current way of implementing safety measures have shortcomings, and cannot address challenges related to close collaboration between humans and robots. We propose that "intelligent" industrial robots of the future should have moral responsibilities towards their human colleagues. We also propose that implementation of moral responsibility is radically different from standard safety measures.