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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

Teaching and learning are increasingly being offered in distributed, online digital environments, often openly and at large-scale, traversing spatial and temporal boundaries. Within such environments, Learning Analytics technologies aim to provide the means for tracking and making sense of the multitude of educational data that is being generated, in order to inform educational and pedagogical decision making of different actors, such as learners, teachers, school leaders and parents. However, at the heart of Learning Analytics technologies in such distributed and open learning environments lies the Open Learner Model (OLM), that informs the data collection, processing and sense-making capabilities of the analytics technology. In this context the contribution of this chapter is to present a generic educational data-driven layered Open Learner Modelling framework, which can be used as a blueprint for the analysis (and design) of OLM instances. Furthermore, capitalizing on this framework, the chapter also performs a critical analysis of existing research in OLM works, in order to draw conclusions on the current status of this emerging field.

Remote and virtual labs (RVLs) are widely used by science education teachers in their daily teaching practice. This has led to a plethora of RVLs that are offered with or without cost. In order to organise them and facilitate their search and findability, several RVL web-based repositories have been operated. As a result, a key open challenge is to design and implement recommender systems that support science teachers in finding and selecting appropriate RVLs based on core pedagogical elements of their learning designs and their own information and communication technology (ICT)-supported teaching competence profiles. In response to this challenge, we propose a recommender system for supporting learning design driven RVLs recommendations taking into consideration teachers’ profiling elements related to the ICT-supported teaching competences for using RVLs. To achieve this, we propose an instantiation of the UNESCO ICT competency framework for teachers for the context of using RVL and capitalise on our previously proposed metadata schema for describing RVLs, which includes core elements of a learning design and teachers’ required ICT-supported teaching competences. Our evaluation results based on a user study, highlight the high level of ranking accuracy of our proposed recommender system and support its capacity to facilitate science teachers in selecting appropriate RVLs.

The concept of Smart Cities is an emerging social and technology innovation, attracting large public and private investments at a global scale, arguing for the effective exploitation of digital technologies to drive quality of living and sustainable growth. However, these investments mainly focus in smart technical infrastructure, and they have yet to be systematically complemented with efforts to prepare the human capital of future smart cities in terms of core competences anticipated for exploiting their potential. In this context, this chapter introduces “cyber-physical learning” as a generic overarching model to cultivate Digital Smart Citizenship competence. The proposed approach exploits the potential of Internet of Things technologies to create authentic blended and augmented learning experiences. Proof-of-concept case studies of the proposed cyber-physical learning approach, to develop smart household energy management competences, are presented and discussed as a field of application. Finally, the findings of a survey with university students for eliciting their attitudes to engage with cyber-physical learning environments for enhancing their digital smart citizenship competences are reported.

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.

Globally, large-scale national initiatives are being implemented towards promoting the level and quality of Information and Communication Technologies (ICT) use in school education. However, despite these efforts, the current level of ICT uptake from schools remains low. A wide range of factors have been identified as barriers, including lack of teachers' ICT Competences and lack of ICT infrastructure. Typically, these barriers are tackled separately without taking into consideration the ecosystemic nature of schools as organizations. This paper introduces a holistic approach on School ICT Competence based on combining both individual teachers' ICT Competences and schools' eMaturity. Furthermore, it presents the design of a web-based School ICT Competence Management System which aims to capture and monitor schools' overall level of ICT uptake and facilitate effective management of resources and strategic planning towards improvement, by jointly processing these data.

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

Science, Technology, Engineering, and Mathematics (STEM) Education has been associated with the need for cultivating students’ inquiry and problem-solving skills by exploiting appropriate student-centered teaching approaches. At the same time, the Flipped Classroom (FC) model has been proposed as a blended learning means to enhance such teaching approaches by effectively reallocating their learning activity distribution towards maximizing student engagement and scaffolding by the teachers. In order to reap the benefits of FC-enhanced teaching approaches when designing and delivering their STEM learning interventions, however, teachers (especially novices) could benefit from having access to “generic” teaching model templates that they can adjust in their specific needs. In this context, we have introduced a set of FC-enhanced teaching model templates focusing on two widely used STEM-appropriate teaching models, i.e., the Inquiry- and Problem-based teaching models. Capitalizing on this, the contribution of this chapter is the presentation of the implementations of the aforementioned teaching templates in Moodle, a widely used open source Learning Management System. The anticipated added value of the proposed adaptable Moodle templates is to provide support to (novice) STEM teachers, not only in their educational design (through the use of the proposed FC-enhanced teaching model templates), but also in the delivery of their lessons and/or educational scenarios.

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.

Schools are complex adaptive systems comprising a vast range of interrelating agents including among others, the school principals, teachers, students, parents, and infrastructural assets. Therefore, school leaders are required to design the strategic plan of their schools by taking into account the full spectrum of the characteristics and behaviors of these agents and the complex manner in which they interrelate and affect the overall school performance. However, considering (a) the high complexity in collecting and processing the institution-wide data and towards generating feedback loops and (b) the wide range of the tasks that school leaders are expected to engage with, this data-driven decision-making process is challenging, and requires appropriate systems to support it. The contribution of this book chapter, therefore, is the critical analysis of 70 existing school leadership decision support systems (SL-DSS), in terms of their capacity to adequately support the full spectrum of school leadership tasks, as they are defined in this book chapter. This critical analysis aims to gather insights that could drive future implementations of SL-DSS toward providing more effective decision support affordances and tackling the identified shortcomings of existing systems.

Recommender Systems (RS) have been implemented in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Object (LO) selection by teachers to support their daily teaching practice. In particular, memory-based collaborative filtering (CF) approaches have demonstrated promising results for real-life implementations of web-based Learning Object Repositories (LOR). Building on this, the contribution of this paper is an enhancement to existing memory-based CF RS methods, by adaptively selecting the teacher neighbors based on their co-rated LOs and the attribute similarity of the latter to the LO to be recommended. The evaluation results show a significant increase in the predictive accuracy of the adaptive RS approaches compared to their "traditional" benchmarks, signifying the proposed approach's capacity to enhance the accuracy of existing memory-based CF approaches.

User profiling is a technique aimed at capturing and exploiting significant characteristics of the users towards the provision of personalized services within adaptive systems such as Recommender Systems (RS). In the context of Technology enhanced Learning (TeL), from a teachers' perspective, the unique ICT competence characteristics of individuals have not been considered when providing Learning Object (LO) recommendations, despite their vital contribution to the level of ICT uptake of teachers. Moreover, there is a lack of mechanisms for automatically eliciting and updating such personal characteristics within Learning Object Repositories (LOR) in order to exploit them for enhanced LO recommendations. Towards tackling this issue, this paper proposes a teacher ICT Competence elicitation mechanism utilizing fuzzy logic for inferring teachers' ICT Competences based on their usage patterns within LOR and presents the results of its preliminary accuracy evaluation. The results indicate that the proposed approach provides high accuracy and can, therefore, construct reliable depictions of the teachers' ICT Competence Profiles.

In all European countries, initiatives are implemented towards promoting the use of Information and Communication Technologies (ICT) in schools. However, the current level of school ICT uptake remains low, due to the interrelation of a wide range of factors, including teachers' ICT competences and strategic school Leadership planning. In this context, there is a need for systems that will facilitate schools in capturing the contribution of these factors and meaningfully process the data for informed strategic planning. The aim of this paper is to present a web-based system, namely the ASK School ICT Competence Management System, which takes a step towards the aforementioned needs for effective school ICT uptake monitoring and management.

Schools have not fully exploited the potential of ICT despite the several initiatives addressing that issue. The reasons for this are manifold due to the complex nature of schools as organizations. Therefore, there is an essential need for a method to effectively capture the level of each factor's contribution to the overall schools' ICT uptake. Towards that end, a review of the existing eMaturity approaches to measuring ICT uptake in schools is performed and the results are used to evaluate whether this concept can adequately meet the above requirements. This leads to the proposal for a new framework for profiling schools' ICT competences, which incorporates currently excluded organizational competence dimensions. This could facilitate the identification of specific factors influencing school competence level and, thus, assisting in more informed corrective action planning.

Recommender Systems (RS) have been investigated in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Objects (LOs) selection and retrieval. However, most of the existing approaches focus on the learners' perspective and do not take into consideration teachers' profile. Moreover, the systems that do target teachers, do not explicitly exploit their ICT competence profiles. This can lead to recommending LOs that are beyond the teachers' current ability to use in their teaching practice. In this paper, we aim to tackle this problem and propose, as a first step, a set of mapping rules for aligning teachers' ICT competences and LO metadata elements. Moreover, a preliminary simulated evaluation is described, the results of which indicate that the mapping schema can provide robust identification of appropriate LOs based on both the users' ICT competences and the overall ratings of the educational resources.