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Blended learning environments supported by the Flipped Classroom Model (FCM) have been repeatedly investigated in both research and practice contexts, primarily in terms of their capacity to foster students' cognitive learning outcomes and overall motivation for the learning process. However, despite the significant body of existing works studying these aspects, there are still very scarce insights regarding the potential reasons for this impact of FCM on students, from the perspective of how this model affects students' internal satisfaction dispositions and fulfills their various needs for sustaining their motivation. Therefore, by adopting Self-Determination Theory as a theoretical framework to model these student needs, this work aims to address this identified issue and investigate, in an exploratory manner, the impact of FCM-enhanced blended learning environments not only on students' cognitive learning outcomes, but more importantly on their internal dispositions (level of satisfaction) and needs for competence, autonomy and relatedness. The work builds on educational data and evidence from three different implementations of FCM in action research studies across diverse K-12 subject domains, and reports on a consistent pattern of positive findings regarding the capacity of FCM-enhanced blended learning environments to improve the aforementioned aspects of students' learning outcomes and experiences, with a particular added value for low-performing students. The Flipped Classroom model (FCM) can be beneficial for improving students' learning in K-12 education.Self-Determination Theory can provide a lens to reveal insights on the reasons for this improvement.FCM can lead to higher students' cognitive learning outcomes.FCM can better foster students' (especially low performers') satisfaction for the learning process.FCM can be used to better meet students' (especially low performers') self-determination needs.

Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top priority for school education worldwide and Inquiry-based teaching and learning is identified as one of the most dominant approaches. To effectively engage individual students in Inquiry tasks, appropriate guidance needs to be provided, usually by combining different digital tools such online labs, data analysis tools and modelling tools. This is a cumbersome task for teachers to perform manually since it involves (a) assessing during the education design, the type and level of tool-supported guidance to be provided to students and (b) potentially refining this level and types to meet the guidance needs of individual students based on educational data from the delivery of the educational design. Thus, in our research we target to investigate how to support this process with educational data analytics methods and tools from both the design and the delivery of educational designs, that inform teachers’ decision making for systematic reflection. To this end, the contribution of this paper is the design and evaluation of a novel “Teaching and Learning” Analytics method and supporting research prototype tool, extending the scope of purely learning analytics methods, to (a) analyze inquiry-based educational designs in terms of the tool-supported guidance they offer and (b) relate these analyses to students' educational data that are already being collected by existing learning analytics systems, so as to increase teachers’ awareness and understanding and scaffold their reflection. A two-layer evaluation methodology was adopted to evaluate both the capacity of our method to analyze educational designs in terms of appropriate guidance as well as to investigate whether the insights generated by the method offer statistically significant indicators that impact students' activity during the delivery of these educational designs. The results obtained, based on real-life educational data, argue that the proposed method and tool can support teachers to accurately analyse Inquiry-based educational designs and receive meaningful insights to improve and tailor students' learning experiences. The insights of this work aim to contribute in the research field of cognitive data analytics for teaching and learning, by investigating new ways to combine analyses of the educational design and the students’ activity, so as to inform teachers’ reflective decision making from a holistic perspective

School leaders around the world are increasingly required to orchestrate their school's strategic planning in order to both meet external accountability requirements and create fostering learning environments for all students. To meet these objectives in the complex school ecosystem, leaders need to make decisions based on holistic collection and analysis of diverse educational data, coming from different school actors (such as students, teachers, infrastructure, curricula, legislations etc.). However, this task can be very cumbersome, especially since contemporary ‘School Analytics’ methods aiming to support decision making have mainly accommodated the aspect of collecting and visualizing data or utilized variance-based analyses, but have yet to explicitly address the aspect of deriving insights to inform strategic school planning. In this context, the contribution of this work is to present and evaluate a novel School Analytics approach that employs fuzzy-set qualitative comparative analysis (fsQCA) as the means to bridge the qualitative-quantitative divide in learning technology research methods and provide leaders with actionable insights on how to create school conditions for fostering students' outcomes, focusing on digital skills as a case study. By employing fsQCA on data from almost 3000 schools across Europe, the paper outlines eight distinct configurations of school factors, which describe how school leaders can potentially generate nurturing environments for enhancing students' digital skills. Finally, the quantitative results from a layered evaluation protocol argue for the promising potential of the fsQCA method as a tool to unravel the complex knot of processing school-wide educational data and generating insights to inform school leaders' decision making.