Contemporary education increasingly involves a blended learning environment, which consists of a combination of offline and online delivery methods. Blended learning environments can motivate students to learn, but designing motivating blended learning environments is challenging and can result in environments that demotivate students. This conceptual article proposes a blended learning design that helps practitioners to design motivating blended learning environments. According to self-determination theory, students are motivated to learn when their three basic psychological needs for autonomy, competence, and relatedness are supported. Competency-based education (CBE) is intended to support students’ basic psychological needs. We have constructed design guidance for CBE programmes that help practitioners to design a combination of offline and online delivery methods that (1) give students choices in time and place to support their need for autonomy, (2) adapt to students’ competency levels to support their need for competence, and (3) stimulate students’ relationship building with peers and teachers to support their need for relatedness. Although the design guidance is tentative, practitioners can experiment with it to design blended learning environments that motivate students to learn.
Review of 42 journal articles on learning environments at the school-work boundary.
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Although self-regulation is an important feature related to students’ study success as reflected in higher grades and less academic course delay, little is known about the role of self- regulation in blended learning environments in higher education. For this review, we analysed 21 studies in which self-regulation strategies were taught in the context of blended learning. Based on an analysis of literature, we identified four types of strategies: cognitive, metacognitive, motivational and management. Results show that most studies focused on metacognitive strategies, followed by cognitive strategies, whereas little to no attention is paid to motivation and management strategies. To facilitate self-regulation strategies non-human student tool interactional methods were most commonly used, followed by a mix of human student-teacher and non-human student content and student environment methods. Results further show that the extent to which students actively apply self-regulation strategies also depends heavily on teacher's actions within the blended learning environment. Measurement of self-regulation strategies is mainly done with questionnaires such as the Motivation and Self-regulation of Learning Questionnaire.Implications for practice and policy:•More attention to self-regulation in online and blended learning is essential.•Lecturers and course designers of blended learning environments should be aware that four types of self-regulation strategies are important: cognitive, metacognitive, motivational and management.•Within blended learning environments, more attention should be paid to cognitive, motivation and management strategies to promote self-regulation.
The project’s aim is to foster resilient learning environments, lessen early school leaving, and give European children (ages 4 -6) a good start in their education while providing and advancing technical skills in working with technology that will serve them well in life. For this purpose, the partnership has developed age appropriate ICT animation tools and games - as well as pedagogical framework specific to the transition phase from kindergarten to school.
The European creative visual industry is undergoing rapid technological development, demanding solid initiatives to maintain a competitive position in the marketplace. AVENUE, a pan-European network of Centres of Vocational Excellence, addresses this need through a collaboration of five independent significant ecosystems, each with a smart specialisation. AVENUE will conduct qualified industry-relevant research to assess, analyse, and conclude on the immediate need for professional training and educational development. The primary objective of AVENUE is to present opportunities for immediate professional and vocational training, while innovating teaching and learning methods in formal education, to empower students and professionals in content creation, entrepreneurship, and innovation, while supporting sustainability and healthy working environments. AVENUE will result in a systematised upgrade of workforce to address the demand for new skills arising from rapid technological development. Additionally, it will transform the formal education within the five participating VETs, making them able to transition from traditional artistic education to delivering skills, mindsets and technological competencies demanded by a commercial market. AVENUE facilitates mobility, networking and introduces a wide range of training formats that enable effective training within and across the five ecosystems. A significant portion of the online training is Open Access, allowing professionals from across Europe to upgrade their skills in various processes and disciplines. The result of AVENUE will be a deep-rooted partnership between five strong ecosystems, collaborating to elevate the European industry. More than 2000 professionals, employees, students, and young talents will benefit from relevant and immediate upgrading of competencies and skills, ensuring that the five European ecosystems remain at the forefront of innovation and competitiveness in the creative visual industry.
The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
