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.
Learning activities in a makerspace are hands-on and characterized by design and inquiry. Evaluation is needed both for learners and their coaches in order to effectively guide the learning process of the children and for feedback on the effectiveness of the after-school maker activities. Due to its constructionist nature, learning in a makerspace requires specific forms of evaluation. In this paper we describe the development of an instrument that facilitates and captures reflection on the activities that children undertook in a library makerspace. Our aim is to capture learning in this context with multiple instruments: analysis of the artifacts that are made, observation of hands-on activities and interviews - which all are time consuming methods. Hence, we developed an easy to use tool for self-evaluation of maker learner activities for children. We build on the design of a visual instrument used for learning by design and inquiry in primary education. The findings and results are transferable to (formative) assessment and evaluation of learning activities by learners in other types of education and specific in maker education.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low.As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers.To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is:How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems?HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases:I. Highway: non-professional drivers;II. Distribution Centre: professional drivers.Collaborative partners:Bielefeld University of Applied Sciences, Bricklog B.V., Goudappel B.V., HaskoningDHV Nederland B.V., Rhine-Waal University of Applied Sciences, Rijkswaterstaat, Saxion, Sencure B.V., Siemens Industry Software Netherlands B.V., Smits Opleidingen B.V., Stichting Innovatiecentrum Verkeer en Logistiek, TNO Den Haag, TU Delft, University of Twente, V-Tron B.V., XL Businesspark Twente.
