10,049 research outputs found
Personalised trails and learner profiling within e-learning environments
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
Modelling human teaching tactics and strategies for tutoring systems
One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers
The Tutor's Role
This chapter addresses three questions about being an effective online tutor: 1. Why do we still think that online tutoring can principally draw its basis from face-to-face group processes and dynamics or traditional pedagogy? 2. Does the literature tell us anything more than we would make as an intelligent guess? 3. Do we really know what an ‘effective’ online tutor would be doing? The OTiS participants have gone some way to answering these questions, through the presentation and discussion of their own online tutoring experiences. Literature in this area is still limited, and suffers from the need for timeliness of publication to be useful. Intelligent guesses are all very well, but much better as a source of information for online tutors are the reflections and documented experiences of practitioners. These experiences reveal that face-to-face pedagogy has some elements to offer the online tutor, but that there are key differences and there is a need to examine the processes and dynamics of online learning to inform online tutoring
Adaptive Educational Hypermedia based on Multiple Student Characteristics
The learning process in Adaptive Educational Hypermedia (AEH) environments is complex and may be influenced by aspects of the student, including prior knowledge, learning styles, experience and preferences. Current AEH environments, however, are limited to processing only a small number of student characteristics. This paper discusses the development of an AEH system which includes a student model that can simultaneously take into account multiple student characteristics. The student model will be developed to use stereotypes, overlays and perturbation techniques. Keywords: adaptive educational hypermedia, multiple characteristics, student model
Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches
Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described
Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning
The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning
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