13,667 research outputs found

    Intelligent and adaptive tutoring for active learning and training environments

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    Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used

    An evaluation of scaffolding for virtual interactive tutorials

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    Scaffolding refers to a temporary support framework used during construction. Applied to teaching and learning it describes measures to support a learner to become confident and self-reliant in a subject. In a Web environment scaffolding features need to replace the instructor. We discuss our approach to Web-based scaffolding based on the cognitive apprenticeship and activity theories. We suggest a set of four scaffold types that have made our scaffolding-supported virtual interactive tutorial successful. We present a novel evaluation approach for virtual tutorials that is embedded into an iterative, evolutionary instructional design

    Personalised correction, feedback, and guidance in an automated tutoring system for skills training

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    In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input

    Automated tutoring for a database skills training environment

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    Universities are increasingly offering courses online. Feedback, assessment, and guidance are important features of this online courseware. Together, in the absence of a human tutor, they aid the student in the learning process. We present a programming training environment for a database course. It aims to offer a substitute for classroom based learning by providing synchronous automated feedback to the student, along with guidance based on a personalized assessment. The automated tutoring system should promote procedural knowledge acquisition and skills training. An automated tutoring feature is an integral part of this tutoring system

    Support of the collaborative inquiry learning process: influence of support on task and team regulation

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    Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation). In this study, we investigate how support of collaborative inquiry learning can influence the use of regulative activities of students. Furthermore, we explore the possible relations between task regulation, team regulation and learning results. This study involves tenth-grade students who worked in pairs in a collaborative inquiry learning environment that was based on a computer simulation, Collisions, developed in the program SimQuest. Students of the same team worked on two different computers and communicated through chat. Chat logs of students from three different conditions are compared. Students in the first condition did not receive any support at all (Control condition). In the second condition, students received an instruction in effective communication, the RIDE rules (RIDE condition). In the third condition, students were, in addition to receiving the RIDE rules instruction, supported by the Collaborative Hypothesis Tool (CHT), which helped the students with formulating hypotheses together (CHT condition). The results show that students overall used more team regulation than task regulation. In the RIDE condition and the CHT condition, students regulated their team activities most often. Moreover, in the CHT condition the regulation of team activities was positively related to the learning results. We can conclude that different measures of support can enhance the use of team regulative activities, which in turn can lead to better learning results

    Stretching the limits in help-seeking research

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    This special section focuses on help seeking in a wide range of learning environments, from classrooms to online forums. Previous research has rather restrictively focused on the identification of personal characteristics that predict whether or not learners seek help under certain conditions. However, help-seeking research has begun to broaden these self-imposed limitations. The papers in this special section represent good examples of this development. Indeed, help seeking in the presented papers is explored through complementary theoretical lenses (e.g., linguistic, instructional), using a wide scope of methodologies (e.g., teacher reports, log files), and in a manner which embraces the support of innovative technologies (e.g., cognitive tutors, web-based environments)

    Teaching new media composition studies in a lifelong learning context

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    Governmental proposals for lifelong learning, and the role of Information and Learning Technologies/Information Communication Technologies (ILT/ICT) in this, idealistically proclaim that ILT/ICT empowers learners. A number of important governmental funding initiatives have recently been extended to the development of ILT in further education, which provides a particularly appropriate environment for lifelong learning. Yet little emphasis is given to more problematic research findings that students may be ‘disarmed’ in the process of learning to use technology. In the current global shift towards new forms of multimedia literacy, it is important to recognize human diversity by carrying out research focusing on the actual problems students face in adapting to Web‐based technology as a new authoring medium. A case study into multimedia creative composition carried out with FE students in 1996–9 found that students tend to experience a problematic but potentially useful period of ‘creative mess’ when authoring in multimedia, and that ‘scaffolding’ strategies can be useful in overcoming this. Such strategies can empower students to derive benefits from multimedia composition if close attention is given to the setting up of the learning environment: a teachers’ model for supporting novice hypermedia authors in further education is proposed, to assist teachers to understand and support the learning processes students may undergo in dynamic composition using new media technology

    Data mining technology for the evaluation of learning content interaction

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    Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results
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