The Effects of Adding Non-Compulsory Exercises to an Online Learning Tool on Student Performance and Code Copying

This study analyzes the impact of adding a review exercises module to an online tool used in a software engineering degree program. The objective of the module is to promote students’ self-learning effort to improve their performance. We also intend to determine if this new feature has any effect on the amount of code copies detected in lab sessions when using the same online tool. Two groups of students were compared quantitatively: the first group used the tool exclusively during lab sessions, whereas the second group had the option of employing the tool's new module to enhance their study. The tool allows us to collect interesting data related to the focus of this research: supplementary work completed voluntarily by students and the percentage of students copying others’ code during compulsory lab sessions. The results show that the students in the second group achieved better academic results and copied less in lab sessions. In the second group, the students who invested more effort in doing revision exercises and copied less in lab sessions obtained better results; and, interestingly, the effort invested in completing review exercises did not seem to compensate for the learning effort avoided by copying others’ exercises during lab sessions. The results show the advantages of a tool used with a dual orientation: compulsory and voluntary. Mandatory usage in lab sessions establishes some milestones that, eventually, act as an incentive fostering learning, while voluntary use reinforces students’ perception of the tool's usefulness in terms of learning

The effects of adding non-compulsory exercises to an online learning tool on student performance and code copying

This study analyzes the impact of adding a review exercises module to an online tool used in a software engineering degree program. The objective of the module is to promote students’ self-learning effort to improve their performance. We also intend to determine if this new feature has any effect on the amount of code copies detected in lab sessions when using the same online tool. Two groups of students were compared quantitatively: the first group used the tool exclusively during lab sessions, whereas the second group had the option of employing the tool's new module to enhance their study. The tool allows us to collect interesting data related to the focus of this research: supplementary work completed voluntarily by students and the percentage of students copying others’ code during compulsory lab sessions. The results show that the students in the second group achieved better academic results and copied less in lab sessions. In the second group, the students who invested more effort in doing revision exercises and copied less in lab sessions obtained better results; and, interestingly, the effort invested in completing review exercises did not seem to compensate for the learning effort avoided by copying others’ exercises during lab sessions. The results show the advantages of a tool used with a dual orientation: compulsory and voluntary. Mandatory usage in lab sessions establishes some milestones that, eventually, act as an incentive fostering learning, while voluntary use reinforces students’ perception of the tool's usefulness in terms of learning

The work of art in the age of artificial intelligibility

The emergence of complex deep-learning models capable of producing novel images on a practically innumerable number of subjects and in an equally wide variety of artistic styles is beginning to highlight serious inadequacies in the ethical, aesthetic, epistemological and legal frameworks we have so far used to categorise art. To begin tackling these issues and identifying a role for AI in the production and protection of human artwork, it is necessary to take a multidisciplinary approach which considers current legal precedents, the practice of software engineering, historical attitudes towards technological innovation and a sustained technical analysis of the models themselves. This paper queries the location and nature of substantive artistic work in the developmental stages of an AI-generated image, offering critiques of existing assumptions and posing questions for future research. The emergence of convincing AI creative output, artistic or literary, has significant long-term implications for the humanities, including the need for re-appraisal of foundational ideas about authorship and creativity in general. The effects of artificial intelligence, whether generalised or task-specific, cannot be ignored or displaced now that easy-access, scalable image and text production is a reality