Minds-On is a novel, theoretically-driven pedagogical approach to support the teaching and learning of science concepts and reasoning skills inupper primary science lessons. Minds-On uses a hands-on – minds-on app- roach interleaving doing experiments with creating interactive concept diagrams. Lessons are fully orchestrated in the online application. Learners complete the lessons independently and receive guidance and feedback from the software. Four lessons have been created combining the following concepts and reasoning skills: seasons/cause-effect, sound/cause-effect, fixings & animals/classificationand bicycle/systems thinking. The approach has been developed using an itera-tive design method with evaluations in each phase either in the classroom or in laboratory conditions. Results from one classroom evaluation (N = 490) showed that both teachers and learners enjoyed working with the lessons, that learners are easily able to complete the lessons within the allocated time and that the app- roach enables learners to effectively apply several types of scientific reasoning and do so more autonomously than in traditional science lessons. Results from the knowledge questionnaires in a later study of the sound lesson (N = 147) showed a significant increase in score compared to before the lesson. In this demonstration
paper, we present the Minds-On approach and associated application, discussing the design process, the theoretical principles on which our approach is based and the preliminary results from recent classroom evaluations.

Many have suggested that AI-based interventions could enhance learning by personalization, improving teacher effectiveness, or by optimizing educational processes. However, they could also have unintended or unexpected side-effects, such as undermining learning by enabling procrastination, or reducing social interaction by individualizing learning processes. Responsible scientific experiments are required to map both the potential benefits and the side-effects. Current procedures used to screen experiments by research ethics committees do not take the specific risks and dilemmas that AI poses into account. Previous studies identified sixteen conditions that can be used to judge whether trials with experimental technology are responsible. These conditions, however, were not yet translated into practical procedures, nor do they distinguish between the different types of AI applications and risk categories. This paper explores how those conditions could be further specified into procedures that could help facilitate and organize responsible experiments with AI, while differentiating for the different types of AI applications based on their level of automation. The four procedures that we propose are (1) A process of gradual testing (2) Risk- and side-effect detection (3) Explainability and severity, and (4) Democratic oversight. These procedures can be used by researchers and ethics committees to enable responsible experiment with AI interventions in educational settings. Implementation and compliance will require collaboration between researchers, industry, policy makers, and educational institutions.

Thinking back and forth between observing physical phenomena and developing scientific ideas, also known as hands-on and minds-on learning, is essential for the development of scientific
reasoning in primary science education. In the Netherlands, inquiry-based learning is advocated as the preferred teaching method. However, most teachers lack time and sufficient pedagogical content knowledge to adequately provide the teaching required for this. To address this problem, we designed and evaluated science and technology lessons, consisting of hands-on experiments combined with interactive diagrams, aimed at scaffolding primary school students (9–12 years) in the development of their scientific reasoning. Our proof-of-concept uses an online application, that lets students work through the lessons while alternating hands-on and minds-on activities. A
study was carried out (n = 490) showing that most students successfully complete the lessons within a standard lesson timeframe. The approach enables students to effectively apply
several types of scientific reasoning and to do so more autonomously than in traditional science classes.

In 2020 is vanuit het SPRONG-Educatief programma van SIA en NRO het STEM-netwerk (Science, Technology, Engineering, Mathematics) gestart. Doel hiervan is het bouwen van een stevig landelijk lerend netwerk op het gebied van STEM-onderwijs, om de kwaliteit van dit onderwijs te verbeteren. In de onderwijspraktijk worden de S-T-E-M namelijk nog vaak onafhankelijk van elkaar onderwezen en is er sprake van een breuklijn tussen het primair en voortgezet onderwijs; het ontbreekt aan een doorgaande leerlijn. Om vak-en sectoroverstijgend STEM-onderwijs te realiseren is een netwerksystematiek ontwikkeld die momenteel wordt geïmplementeerd. Deze systematiek bestaat uit het werken met een landelijk kernteam, regioteams en regionale professionele leergemeenschappen (PLGs), en het bevorderen van netwerkvorming met behulp van de sleutelfactoren hiervoor. Na twee jaar is het tijd om de balans op te maken. In deze bijdrage wordt ingegaan op de organisatie van het STEM-netwerk en de sleutelfactoren voor netwerkleren. Daarnaast wordt verslag gedaan van evaluaties van het STEM-netwerk aan de hand van de sleutelfactoren en wordt weergegeven hoe hiervan wordt geleerd.