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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

Recommender systems (RS) offer personalized services for facilitating the process of appropriate item selection. To perform this task, user profiling mechanisms should be implemented to automatically construct and update meaningful user profiles. These profiles can drive the RS in providing informed recommendations suited to the unique characteristics of each user. In the context of technology enhanced learning (TeL) Recommender Systems, the majority of research focus directly on learners’ profiling and ignore the potential benefits of profiling teachers’ professional capacities too. As a result, limited previous works exist on effectively capturing and utilizing individual teachers’ particular professional characteristics, such as their Digital Competences (commonly referred to as ICT Competences) and exploiting these in systems that support their teaching preparation and practice, for example in the selection of appropriate educational resources. This paper proposes a RS which targets to support teachers in selecting learning objects (LO) from existing LO repositories (LORs) in a unified manner, namely by (a) automatically constructing their ICT Competence Profiles based on their actions within these LORs and (b) exploiting these profiles for more efficient LO selection. Experiments with data from three real-life LORs are presented and evaluation results are discussed to demonstrate the benefits of the proposed system.

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.

Orchestrating holistic school improvement requires school leaders to effectively engage in the tasks of collecting and processing diverse educational data from the school ecosystem, and more importantly, to be able to 'translate' these analyses to specific remedying actions for targeted improvement. However, these processes can be cumbersome, especially given that existing 'School Analytics' methods aiming to support them have mainly focused on the former task, but have yet to explicitly address the latter. In this context, the paper presents and initially validates a novel School Analytics approach, which employs fuzzy-set Qualitative Comparative Analysis as the means to provide leaders with actionable insights on how to create the school conditions for fostering students' learning outcomes, focusing on 'digital skills' as a case study. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Teacher inquiry is identified as a key global need for driving the continuous improvement of the teaching and learning conditions for learners. However, specific barriers (mainly related to teachers’ data literacy competences), can defer teachers from engaging with inquiry to improve their teaching practice. To alleviate these barriers and support teacher inquiry, the concept of Teaching and Learning Analytics (TLA) has been proposed, as a complementing synergy between Teaching Analytics and Learning Analytics. Teaching and Learning Analytics aims to provide a framework in which the insights generated by Learning Analytics methods and tools can become meaningfully translated for driving teachers’ inquiry to improve their teaching practice, captured through Teaching Analytics methods and tools. In this context, TLA have been identified as a research challenge with significant practical impact potential. This chapter contributes the first systematic literature review in the emerging research field of Teaching and Learning Analytics. The insights gained from the systematic literature review aim to (a) transparently outline the existing state-of-the-art following a structured analysis methodology, as well as (b) elicit insights and shortcomings which could inform future work in the Teaching and Learning Analytics research field.

Project-based learning (PjBL) is a promising approach for supporting learning of computer programming by addressing the gap between the attainment of abstract knowledge and the application of this knowledge to authentic programming tasks. The World Wide Web has considerable potential to expand and improve PjBL environments. However, making implicit aspects of a programming task accessible to learners and instructors to support reflection and improvement can be challenging. This paper discusses the challenge of PjBL in programming education and outlines a set of key aspects and an analysis framework to inform design and analysis of PjBL programming in web-based environments, by exploiting the emerging field of Teaching and Learning Analytics. Based on the proposed analysis framework, the design of an empirical study is outlined, capitalizing on a novel web-based PjBL environment that makes complex cognitive processes accessible and affords the analysis of its effects on learning programming in multiple aspects.

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences. Kraftsamling Smarta Nät

Massive open online courses (MOOCs) are a recent trend in the field of online learning. Their potential distinctive educational innovation is attributed mainly to their “Massiveness” and “Openness” dimensions. These two characteristics introduce new educational design considerations for accommodating the challenging demands of “Massiveness” and “Openness.” This is particularly pressing, since commonly recognized “shortcomings” of MOOCs, such as high participant drop-out rates and low levels of participant engagement and motivation, have been associated to the educational design of MOOCs. Within this context, this chapter discusses issues related with the educational design of MOOCs with emphasis to cultural and motivational issues, presents an analysis of existing educational design frameworks and/or guidelines for MOOCs and, finally, proposes an ADDIE-based educational design considerations framework (EDCF) for MOOCs, which incorporate the “Massiveness” and “Openness” requirements.

Providing appropriate tool-supported guidance to students is an essential aspect of technology-supported inquiry-based STEM education, in order to facilitate them in engaging in diverse inquiry tasks. However, analyzing educational designs and evaluating the level of tool-supported guidance provided towards reflective remedying actions is not a trivial task, especially for novice STEM teachers. In this context, the paper presents the design and preliminary evaluation of an inquiry-based STEM Teaching Analytics Tool. This tool aims to visually analyze and evaluate existing educational designs in terms of the level of technology-supported guidance they offer and support teachers' reflective (re) design. The preliminary evaluation results attest to the high levels of accuracy of the proposed Teaching Analytics Tool and provide evidence of its capacity to inform future Teaching and Learning Analytics tools for facilitating STEM teachers to engage in data-driven reflective (re) design of their practice.

Data-driven decision-making in education has received an increasing level of attention on a global scale, especially with the raising interest on big data. This trend has led to the development of two core analytics strands, namely Academic Analytics and Learning Analytics. The former focuses mainly on the macro layer of the organization and is addressed to higher education, while the latter focuses mainly on the micro/meso layers of the organization. Considering the diverse focal points and contexts of application of the two existing analytics strands, the ecosystemic nature of K-12 schools as social complex adaptive systems, as well as, the need for data-based evidence-driven school complexity leadership, we claim that a holistic decision support approach for addressing the full spectrum of school leaders’ tasks is required, beyond the existing analytics strands. Therefore, in this book chapter, we introduce the concept of School Analytics as a holistic, multilevel analytics framework aiming to integrate data collected from all micro-, meso- and macro- organizational layers. We analyze them in an intertwining manner towards providing continuous feedback loops and systemic decision support to K-12 school leaders.