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BackgroundEmerging technologies and societal changes create new ethical concerns and greater need for cross-disciplinary and cross-stakeholder communication on navigating ethics in research. Scholarly articles are the primary mode of communication for researchers, however there are concerns regarding the expression of research ethics in these outputs. If not in these outputs, where should researchers and stakeholders learn about the ethical considerations of research?ObjectivesDrawing on a scoping review, analysis of policy in a specific disciplinary context (learning and technology), and reference group discussion, we address concerns regarding research ethics, in research involving emerging technologies through developing novel policy that aims to foster learning through the expression of ethical concepts in research.ApproachThis paper develops new editorial policy for expression of research ethics in scholarly outputs across disciplines. These guidelines, aimed at authors, reviewers, and editors, are underpinned by: a cross-disciplinary scoping review of existing policy and adherence to these policies;a review of emerging policies, and policies in a specific discipline (learning and technology); and,a collective drafting process undertaken by a reference group of journal editors (the authors of this paper).ResultsAnalysis arising from the scoping review indicates gaps in policy across a wide range of journals (54% have no statement regarding reporting of research ethics), and adherence (51% of papers reviewed did not refer to ethics considerations). Analysis of emerging and discipline-specific policies highlights gaps.ConclusionOur collective policy development process develops novel materials suitable for cross-disciplinary transfer, to address specific issues of research involving AI, and broader challenges of emerging technologies.

Analysing the processes and products of creativity to better understand and support individuals and teams, is a difficult and elusive challenge despite years of research in creativity. In this article, we are particularly interested in social creativity in communities of interest. Building on Guilford's classic model of Divergent Thinking of fluency, flexibility, originality and elaboration, we employ Social Network Analysis to model the creative design process. The creative process in the current study takes place in a technological environment called the ‘MC-squared platform’, in which members of a community of interest collaborate in a social, co-creative process for designing digital, mathematical textbooks. Both the technological environment and the methodology are exemplified through two case examples, one on the design process of a digital book about a bioclimatic amusement park and one on the design process of a digital book about fractions. We conclude that, for these examples, both the technological tool and the data analysis approach provide insight into the social creativity process of the community of interest.

Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.

This paper presents an analytics platform that has been developed for designers and teachers who build and use interactive e-books for learning. The analytics dashboard aims to increase awareness of the use of the e-books so that designers (and teachers in their role as designers) can make informed decisions on how to redesign and improve them taking into account both the overall learning design and the data from their usage. This paper presents architectural and design decisions on key features of the dashboard, and the evaluation of a high-fidelity prototype. We discuss findings related to use of the dashboard for exploratory data analysis and inquiry and how these generalise and can be taken into account by our future work or that of others.