I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Transformational Leadership: Industrial, Military, and Educational Impact

This is the first handbook where the world’s foremost “experts on expertise” review our scientific knowledge on expertise and expert performance and how experts may differ from non-experts in terms of their development, training, reasoning, knowledge, social support, and innate talent. Methods are described for the study of experts’ knowledge and their performance of representative tasks from their domain of expertise. The development of expertise is also studied by retrospective interviews and the daily lives of experts are studied with diaries. In 15 major domains of expertise, the leading researchers summarize our knowledge of the structure and acquisition of expert skill and knowledge and discuss future prospects. General issues that cut across most domains are reviewed in chapters on various aspects of expertise, such as general and practical intelligence, differences in brain activity, self-regulated learning, deliberate practice, aging, knowledge management, and creativity.

The Cambridge Handbook of Expertise and Expert Performance

Learning in Adulthood: A Comprehensive Guide (3rd ed.)

We present an analysis of instructional design quality of 76 randomly selected Massive Open Online Courses (MOOCs). The quality of MOOCs was determined from first principles of instruction, using a course survey instrument. Two types of MOOCs (xMOOCs and cMOOCs) were analysed and their instructional design quality was assessed and compared. We found that the majority of MOOCs scored poorly on most instructional design principles. However, most MOOCs scored highly on organisation and presentation of course material. The results indicate that although most MOOCs are well-packaged, their instructional design quality is low. We outline implications for practice and ideas for future research. Instructional design quality of 76 randomly selected MOOCs was assessed.Quality was determined from first principles, using a Course Scan instrument.The majority of MOOCs scored poorly on most instructional design principles.Most MOOCs scored highly on organisation and presentation of course material.Although most MOOCs are well-packaged, their instructional design quality is low.

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Instructional quality of Massive Open Online Courses (MOOCs)

Massive open online courses (MOOCs) require individual learners to be able to self-regulate their learning, determining when and how they engage. However, MOOCs attract a diverse range of learners, each with different motivations and prior experience. This study investigates the self-regulated learning (SRL) learners apply in a MOOC, in particular focusing on how learners' motivations for taking a MOOC influence their behaviour and employment of SRL strategies. Following a quantitative investigation of the learning behaviours of 788 MOOC participants, follow-up interviews were conducted with 32 learners. The study compares the narrative descriptions of behaviour between learners with self-reported high and low SRL scores. Substantial differences were detected between the self-described learning behaviours of these two groups in five of the sub-processes examined. Learners' motivations and goals were found to shape how they conceptualised the purpose of the MOOC, which in turn affected their perception of the learning process.

Learning in MOOCs: Motivations and self-regulated learning in MOOCs

Current systems used in education follow a consistent design pattern, one that is not supportive of lifelong learning or personalization, is asymmetric in terms of user capability, and which is disconnected from the global ecology of Internet services. In this paper we propose an alternative design pattern for educational systems that emphasizes symmetric connections with a range of services both in formal and informal learning, work, and leisure, and identify strategies for implementation and experimentation.

Personal Learning Environments: Challenging the Dominant Design of Educational Systems.

Connectivist massive open online courses (cMOOCs) represent an important new pedagogical approach ideally suited to the network age. However, little is known about how the learning experience afforded by cMOOCs is suited to learners with different skills, motivations, and dispositions. In this study, semi-structured interviews were conducted with 29 participants on the Change11 cMOOC. These accounts were analyzed to determine patterns of engagement and factors affecting engagement in the course. Three distinct types of engagement were recognized – active participation, passive participation, and lurking. In addition, a number of key factors that mediated engagement were identified including confidence, prior experience, and motivation. This study adds to the overall understanding of learning in cMOOCs and provides additional empirical data to a nascent research field. The findings provide an insight into how the learning experience afforded by cMOOCs suits the diverse range of learners that may coexist within a cMOOC. These insights can be used by designers of future cMOOCs to tailor the learning experience to suit the diverse range of learners that may choose to learn in this way.

/pdf/patterns-of-engagement-in-connectivist-moocs-6rpu10q847.pdf

Patterns of engagement in connectivist MOOCs

/pdf/personal-learning-environments-challenging-the-dominant-41h37nx89s.pdf

Personal Learning Environments : challenging the dominant design of educational systems.

Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs. A learner's current context and role influences overall self-regulation in a MOOC.The contextualisation of learning activities promotes self-regulated learning.Connecting learning in MOOCs to 'real-world' contexts makes learning more accessible.

Colin Milligan

Papers

Learning in MOOCs: Motivations and self-regulated learning in MOOCs

Personal Learning Environments: Challenging the Dominant Design of Educational Systems.

Patterns of engagement in connectivist MOOCs

Personal Learning Environments : challenging the dominant design of educational systems.

Context counts