Scaffolding Reflection: Prompting Social Constructive Metacognitive Activity in Non-Formal Learning

The study explores the effects of three different types of non-adaptive, metacognitive scaffolding on social, constructive metacognitive activity and reflection in groups of non-formal learners. Six triads of non-formal learners were assigned randomly to one of the three scaffolding conditions: structuring, problematising or epistemological. The triads were then asked to collaboratively resolve an ill-structured problem and record their deliberations. Evidence from think-aloud protocols was analysed using conversational and discourse analysis. Findings indicate that epistemological scaffolds produced more social, constructive metacognitive activity than either of the two other scaffolding conditions in all metacognitive activities except for task orientation, as well as higher quality interactions during evaluation and reflection phases. However, participants appeared to be less aware of their activities as forming a strategic, self-regulatory response to the problem. This may indicate that for learning transfer, it may be necessary to employ an adaptive, facilitated reflection on learners' activities

Learning across the curriculum: careers and the world of work

Highlights those statements or sections of a programme of study/learning outcomes for learners aged 11–19 that are explicitly linked to careers and the world of work (CWW)

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Review on learning orientations

The need has arises towards the consideration of individual difference to let learners engage in and responsible for their own learning, retain information longer, apply the knowledge more effectively, have positive attitudes towards the subject, have more interest in learning materials, score higher and have high intrinsic motivation level. As regard to the importance of individual differences, Martinez (2000) has grounded a new theory, which is Intentional Learning Theory that covered individual aspects of cognitive, intention, social and emotion. This theory hypothesizes that the fundamental of understanding how individual learns, interact with an environment, performs, engages in learning, experiences learning, and assimilate and accommodate the new knowledge is by understanding individual’s fundamental emotions and intentions about how to use learning, why it is important, when the suitable time, and how it can accomplish personal goals and change. The intent of this theory is to focus on emotions and intentions of an individual regarding why, when and how learning goals are organized, processed, and achieved. In conclusion, Learning Orientations introduced by this theory describes the disposition of an individual in approaching, managing and achieving their learning intentionally and differently from others

Decision making and risk management in adventure sports coaching

Adventure sport coaches practice in environments that are dynamic and high in risk, both perceived and actual. The inherent risks associated with these activities, individuals’ responses and the optimal exploitation of both combine to make the processes of risk management more complex and hazardous than the traditional sports where risk management is focused almost exclusively on minimization. Pivotal to this process is the adventure sports coaches’ ability to make effective judgments regarding levels of risk, potential benefits and possible consequences. The exact nature of this decision making process should form the basis of coaching practice and coach education in this complex and dynamic field. This positional paper examines decision making by the adventure sports coach in these complex, challenging environments and seeks to stimulate debate whilst offering a basis for future research into this topic

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group