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Collections represent an opportunity for editors to collect related articles on a topic of contemporary interest to RISE readers and the broader science education research community. RISE's current “artificial intelligence (AI) in science education” collection explores the various ways AI tools have been and are used in science education during what we call the period of pregenerative or predictive AI and the period of postgenerative AI. Both pre- and post-generative AI use machine learning algorithms, but they differ in their goals and functions. Predictive AI, as the name suggests, makes predictions, recommendations and makes decisions through a variety of machine learning and modeling techniques using structured data. Generative AI comprises deep learning models capable of generating high-quality text, images, code and related content derived from the large unstructured data on which they were trained. AI has a rich history of anticipation and promise, but the tipping point marked by the public launch of content generation tools like ChatGPT in late 2022 presents potential opportunities and challenges to revolutionize science education. As can be seen from the variety of archived articles published between 1992 and 2023, RISE has a long history of publishing on the application of pregenerative or predictive AI in science education. With the increasing prevalence of ChatGPT, Gemini, Bing AI Co-pilot, and other AI tools, we anticipate more articles on generative AI will be published in the future that will add to the ongoing conversation illustrated in this collection.
Lifelong learning is one of the Sustainable Development Goals proposed by the United Nations to promote adaptability in the current complex context. Given this proposal, self-regulated learning is essential to achieve lifelong learning. Thus, the development of self-regulated learning must be included as an essential part of education and worked on from different approaches. Peer evaluation is an approach that seeks to facilitate critical reflection for the analysis and assessment of products or actions developed by peers, contributing to the development of self-regulated learning. Technology facilitates peer evaluation tasks, since it allows the inclusion of recommendations from the literature that have proven to be the most effective in developing self-regulation of learning. Among them, the research highlights anonymous evaluation between peers, the possibility of offering instant feedback that allows better reflection on the work and offering several iterations between evaluator and evaluated. Also notable is the ease with which an evaluation can be configured consisting of a rubric accompanied by qualitative comments that facilitate the identification of weaknesses and suggested proposals for improvement. This assessment allows them to improve their strategies to achieve self-regulated learning in two ways. On the one hand, a formative approach to peer assessment should be adopted, which includes comments on the performance of the assessed peer's task. In this case, feedback becomes feedforward, since it must be accompanied by improvement proposals that allow the task and learning to evolve. Receiving feedback from colleagues makes it easier to identify strengths and weaknesses in your work, fostering metacognitive skills to plan and guide your learning. On the other hand, to achieve good reflection that allows self-regulation of learning, the evaluation of tasks can be accompanied by rubrics or criteria that allow the student to focus on assessing what is important. In this way, students must reflect to provide quality feedback, which translates into a better understanding of the objectives and evaluation criteria. This will help students reflect on their own work and how they can improve it. This collection will focus on how peer assessment can facilitate self-regulated learning. Specifically, this collection will focus on studies on the effectiveness of peer assessment programs, research on which aspects of peer assessment are most effective for self-regulated learning, and how peer assessment can be implemented with technology to enhance self-regulated learning. We accept a wide range of research articles, including but not limited to: Empirical research Systematic reviews Conceptual works Case studies Keywords Peer evaluation Self-regulated learning Metacognition Reflection Forward feedback
Science education is essential for training future scientists and developing scientific literacy in the general population, but in the later years of school, only a small number of students, mainly from elite schools and communities, continue to study science. Some of those who complete the bachelor's degree in science become science teachers, and the educational model continues. According to the adage attributed to Albert Einstein, if we continue doing what we have always done, we will continue to obtain the same results. The world our young people grow up in is different from the one most science educators have experienced. The future decisions that today's students will need to make will require an understanding of science and how to address socio-scientific issues, artificial intelligence, misinformation, and ecological crises such as climate change and biodiversity loss. These complex challenges will require competencies derived from science, as well as other knowledge, to find possible solutions. Science education today is a complex task that requires us all to work together in evidence-based ways for the benefit of our young people and their future, and the future of humanity and our planet (adapted from Stewart et al., 2024).
The International Journal of Science and Mathematics Education is pleased to announce an online collection on “Mathematics in and for STEM Education.” This collection aims to publish high-quality research articles that explore the fundamental role of mathematics in the broader context of science, technology, engineering and mathematics (STEM) education. Mathematics is a fundamental discipline that supports and enables advances in the fields of science, technology, engineering and mathematics (STEM). As such, this collection will encompass two complementary perspectives: Mathematics for STEM education: Entries in this category will examine the role of mathematics as a subject facilitating success in other STEM disciplines. Contributions to this category will include the application of mathematical concepts, techniques, and problem-solving skills in science, technology, and engineering contexts. Additionally, papers may address the integration of mathematical concepts into STEM curricula, the development of quantitative and analytical skills for STEM careers, and interdisciplinary approaches to teaching and learning mathematics for STEM education.
In this special issue, we invite articles that extend the conversation started in Fake Degrees and Fraudulent Credentials in Higher Education (Eaton, Carmichael & Pethrick, eds.). Topics may include, but are not limited to: Diploma mills and degree mills Predatory educational programs and schools Fake and fraudulent degrees Accreditation mills and accreditation fraud Admissions fraud Transcript tampering Fraud in standardized testing (e.g., language proficiency tests) The societal impact of fake, fraudulent, and questionable credentials As a transdisciplinary publication, the International Journal for Educational Integrity invites submissions from across disciplines that employ a variety of research methodologies. Submissions must be evidence-based and grounded in scholarly literature. All submissions undergo double-anonymized peer review.
Collections represent an opportunity for editors to collect related articles on a topic of contemporary interest to RISE readers and the broader science education research community. The current collection features previously published articles that highlight key issues related to science, technology, engineering, and mathematics (STEM) and engineering or design processes, in particular how teachers organize for instruction and how students experience this. The collection focuses on the question of how teachers learn about STEM and, more importantly, how they translate that knowledge into classroom practice. The set includes (relatively) recent articles from RISE to collectively analyze conceptions of STEM as interdisciplinary and how teachers have taken up the challenge presented by public and political rhetoric to grow the "STEM pipeline." More particularly, the collection represents a cross-section of teachers and teaching approaches across the preschool/early years sector through secondary/middle school levels. We encourage interested readers to follow the cited references and the latest research to explore the area and help develop this field further.