26,015 research outputs found

    Chapter 1 : Learning Online

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    The OTiS (Online Teaching in Scotland) programme, run by the now defunct Scotcit programme, ran an International e-Workshop on Developing Online Tutoring Skills which was held between 8–12 May 2000. It was organised by Heriot–Watt University, Edinburgh and The Robert Gordon University, Aberdeen, UK. Out of this workshop came the seminal Online Tutoring E-Book, a generic primer on e-learning pedagogy and methodology, full of practical implementation guidelines. Although the Scotcit programme ended some years ago, the E-Book has been copied to the SONET site as a series of PDF files, which are now available via the ALT Open Access Repository. The editor, Carol Higgison, is currently working in e-learning at the University of Bradford (see her staff profile) and is the Chair of the Association for Learning Technology (ALT)

    Blended tutoring: an exploration of tutor emotional competences valued by learners in a higher education context

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    This paper reports on research into the emotional competences that mature higher education (HE) students, working in blended learning contexts and studying part-time (PT), vocationally relevant, degrees within a School of Education, value in their tutors. A mixed methods approach was adopted to conduct a detailed exploration of eight tutors’ practice with data gathered from three principal sources. Interviews with tutors explored their approaches to delivery and considered factors that impacted on quality; students’ perceptions of their learning experiences were assessed using an attitude survey; and, an analysis of the content and communications in the virtual learning environment provided insight into tutors’ online practice. Goleman’s (2001) ‘Framework of Emotional Competences’ provided an initial structure but, after analysis, some competences were rejected and others were added. The paper suggests that a new group of competences are required that could support effective blended tutoring for mature learners as well as the recruitment and selection of tutors

    A review on massive e-learning (MOOC) design, delivery and assessment

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    MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft

    Genisa: A web-based interactive learning environment for teaching simulation modelling

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    Intelligent Tutoring Systems (ITS) provide students with adaptive instruction and can facilitate the acquisition of problem solving skills in an interactive environment. This paper discusses the role of pedagogical strategies that have been implemented to facilitate the development of simulation modelling knowledge. The learning environment integrates case-based reasoning with interactive tools to guide tutorial remediation. The evaluation of the system shows that the model for pedagogical activities is a useful method for providing efficient simulation modelling instruction

    Beyond model answers: learners’ perceptions of self-assessment materials in e-learning applications

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    The importance of feedback as an aid to self‐assessment is widely acknowledged. A common form of feedback that is used widely in e‐learning is the use of model answers. However, model answers are deficient in many respects. In particular, the notion of a ‘model’ answer implies the existence of a single correct answer applicable across multiple contexts with no scope for permissible variation. This reductive assumption is rarely the case with complex problems that are supposed to test students’ higher‐order learning. Nevertheless, the challenge remains of how to support students as they assess their own performance using model answers and other forms of non‐verificational ‘feedback’. To explore this challenge, the research investigated a management development e‐learning application and investigated the effectiveness of model answers that followed problem‐based questions. The research was exploratory, using semi‐structured interviews with 29 adult learners employed in a global organisation. Given interviewees’ generally negative perceptions of the model‐answers, they were asked to describe their ideal form of self‐assessment materials, and to evaluate nine alternative designs. The results suggest that, as support for higher‐order learning, self‐assessment materials that merely present an idealised model answer are inadequate. As alternatives, learners preferred materials that helped them understand what behaviours to avoid (and not just ‘do’), how to think through the problem (i.e. critical thinking skills), and the key issues that provide a framework for thinking. These findings have broader relevance within higher education, particularly in postgraduate programmes for business students where the importance of prior business experience is emphasised and the profile of students is similar to that of the participants in this research

    Personalised trails and learner profiling within e-learning environments

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    This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
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