One major challenge the educational community is facing relates to how to effectively integrate computational thinking (CT) concepts and ideas into a particular school curriculum. Acquiring CT-skills by means of STEM offers rich opportunities within students ́ education which may lead to learning gains. Previous research has shown that, to maximize the appeal and potential of CT learning environments, a precondition must be set first. The materials used must invite problem-based, inquiry-based and self-discovery learning, must be used without creating misconceptions and, above all, must give students the opportunity to acquire knowledge that can be directly transferred to everyday practice in an accessible manner. All the above puts demands on teachers who carry out learning and teaching in these environments. The EU funded TACTIDE project has tried to incorporate relevant curricular components into a coherent task, implementing assignments and challenges across different subjects and curricula of three different European countries. Based on the analysis of each national curricula, common topics have been identified and sub-scenarios have been developed. These sub-scenarios have been conceived to promote the integration between the topics mediated by CT. To achieve this objective, a greenhouse scenario has been conceptualized and designed towards teaching CT, by the use of microcontrollers such as the BBC micro :b it and the Calliope Mini, as an overarching STEM-topic. Using available sub-scenarios, a Moodle-course for teachers was developed for daily school activities to which other subjects in the core curriculum were interconnected in order to integrate CT skills and abilities. Scalability across different school levels and heterogeneous groups of learners, especially focusing prior knowledge, have been considered important design elements.

Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners’ interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the student’s learning style, study schedule, knowledge, and performance. Quizzing might be used to help to create individualized/personalized spaced repetition algorithm in order to improve long-term retention of knowledge and provide efficient learning in online learning platforms. Current spaced repetition algorithms have pre-defined repetition rules and parameters that might not be a good fit for students’ different learning styles in online platforms. This study uses different machine learning models and a rich context model to analyze quizzing and reading records from e-learning platform called Hypocampus in order to get some insights into the relevant features to predict learning outcome (quiz answers). By knowing the answer correctness, a learning system might be able to recommend personalized repetitive schedule for questions with maximizing long-term memory retention. Study results show that question difficulty level and incorrectly answered previous questions are useful features to predict the correctness of student’s answer. The gradient-boosted tree and XGBoost models are best in predicting the correctness of the student’s answer before answering a quiz. Additionally, some non-linear relationship was found between the reading learning material behavior in the platform and quiz performance that brings added value to the accuracy for all used models.

In recent years, educational practitioners and learning technologists have been designing, developing and deploying a wide range of innovative environments to enhance meaningful, seamless and exciting learning experiences. In these learning environments, students may interact as individuals or in groups across several learning spaces, while utilizing various technologies that support different contexts of use. However, despite these efforts and progress in this field, there are still a number of technical challenges associated with software architecture issues related to how to implement tools that support seamless learning. In this chapter, we describe our ongoing research related to the design, development and deployment of different software solutions to support seamless learning activities across a variety of settings. Here, we examine and propose a candidate software architecture that is inspired by our implementation of seamless learning activities. In particular, we direct our efforts towards enhancing and facilitating these activities by supporting the reuse and remixing of digital content generated by learners and teachers. The features of the proposed architecture also support the reusability and interoperability of software components and micro-services for different interactions. We address architectural challenges identified in activities we have carried out during the last five years. Three different use cases of seamless learning activities in different subject areas are analysed and the supporting technical and software solutions examined, in order to identify and extract the salient features and functional requirements of the proposed architecture. In addition, we use these cases to examine, analyse and propose new and refined ways to cope with challenges related to the activities that need to be implemented for seamless learning flow. We present and discuss our results describing an architecture capable of supporting meaningful, appealing and seamless learning experiences that are independent of subject matter, context of use and supporting technological solutions. © 2019, Springer Nature Singapore Pte Ltd.

Estimating students´ knowledge and performance, modeling their learning behaviors, and discovering and analyzing their different characteristics are some of the main tasks in the field of research called educational data mining (EDM). According to Chounta (2017), the predicted probabilities that a student will answer a question correctly can provide some insights into the student´s knowledge. Based on this point of departure, the main objective of this paper is to apply different data mining techniques to predict the probabilities that students will answer questions correctly by using their interaction records with a web-based learning platform called Hypocampus. Five different machine learning algorithms and a rich context model were used on the Hypocampus dataset. The results of our evaluation indicate that the gradient-boosted tree and the XGboost algorithms are best in predicting the correctness of the student’s answer

The term Computational Thinking is closely related to efforts connected to teach a systematic and well-structured way of problem solving that includes a set of tools and techniques used in Computer Science. While substantial research in this field has shown promising outcomes concerning distinct intervention programs and teaching initiatives, the term Computational Thinking itself requires to be revised in order to get a wider consensus about its meaning and purpose. This paper contributes to the ongoing quest concerning the definition of the term by starting with a fundamental perspective on computational theory and corresponding concepts in order to describe the theoretical building blocks of a systematic view to further elaborate on an approach for teaching and learning about Computational Thinking. Additionally, based on this foundational effort, more advanced concepts are presented and discussed in order to better understand this domain. Finally, the paper identifies and discusses a set of relevant challenges taking a cognitive psychology perspective on Computational Thinking.

In this paper, we deliver insights into our work with the Calliope system. Weunderline the usefulness of the Calliope system and its positive characteristics relating to anevaluation we did with 50 pupils in German schools. Additionally we report about thelearning scenarios we conducted and illustrated how the learning of the IFTTT conditionsand according to that Computational Thinking skills can be taught during school lessons byusing computational devices like the Calliope mini.

Industry 4.0 is known as the fourth industrial revolution which refers to the integration of technologies that make the factories interoperable by seamlessly connecting machines, employees and sensors for communication. In Industry 4.0, one of the key features is the use of new technologies to recognize the current context. Thus, the employees are supported with contextual information for speeding up decision-making during various processes related to planning, production, maintenance, etc. As a contribution to this area, the work described here aims to introduce a cyber-physical system (CPS) approach to provide context-based and intelligent support to employees in heavy industries using new technologies, especially in the field of mobile devices. In this work, mobile device sensors and image processing techniques are used to recognize the context which requires specific support. In addition, new scenarios and associated processes are developed to support the employees on the basis of new, flexible, adaptive and mobile technologies.

Nowadays, teachers and students utilize different ICT devices for conducting innovative and educational activities from anywhere at any time. The enactment of these activities relies on robust communication and computational infrastructures used for supporting technological devices enabling better accessibility to educational resources and pedagogical scaffolds, wherever and whenever necessary. In this paper, we present EDU.Tube: an interactive environment that relies on web and mobile solutions offered to teachers and students for authoring and incorporating educational interactions at specific moments along the time line of occasional YouTube video-clips. The teachers and students could later experience these authored artefacts while interacting from their stationary or mobile devices. We describe our efforts related to the design, deployment and evaluation of an educational activity supported by the EDU.Tube environment. Furthermore, we illustrate the specific teachers’ and students’ efforts practiced along the different phases of this educational activity. The evaluation of this activity and results are presented, followed by a discussion of these findings, as well as some recommendations for future research efforts further elaborating on EDU.Tube’s aspects in relation to learning analytics.

This chapter describes our research efforts related to the design of mobile learning (m-learning) applications in cloud-computing (CC) environments. Many cloud-based services can be used/integrated in m-learning scenarios, hence, there is a rich source of applications that could easily be applied to design and deploy those within the context of cloud-based services. Here, we present two cloud-based approaches—a flexible framework for an easy generation and deployment of mobile learning applications for teachers, and a flexible contextualization service to support personalized learning environment for mobile learners. The framework provides a flexible approach that supports teachers in designing mobile applications and automatically deploys those in order to allow teachers to create their own m-learning activities supported by mobile devices. The contextualization service is proposed to improve the content delivery of learning objects (LOs). This service allows adapting the learning content and the mobile user interface (UI) to the current context of the user. Together, this leads to a powerful and flexible framework for the provisioning of potentially ad hoc mobile learning scenarios. We provide a description of the design and implementation of two proposed cloud-based approaches together with scenario examples. Furthermore, we discuss the benefits of using flexible and contextualized cloud applications in mobile learning scenarios. Hereby, we contribute to this growing field of research by exploring new ways for designing and using flexible and contextualized cloud-based applications that support m-learning.