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Background: Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning., Aim: The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps., Sample: Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female., Method: Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed., Results: Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task., Conclusions: An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

Presents a two-phase instructional design model that focuses on optimizing step size in whole-task approaches to learning complex, mainly non-recurrent, cognitive skills. Step size in a multiple-step whole-task approach-needed for the process worksheets-is determined on the basis of estimated part-task complexity. A developmental study of the model is illustrated with examples from the domain of law. (AEF)

Multiuser immersive learning scenarios hold strong potential for lifelong learning as they can support the acquisition of higher order skills in an effective, efficient, and attractive way. Existing virtual worlds, game development platforms, and game engines only partly cater for the proliferation of such learning scenarios as they are often inadequately tuned for learning. First, this study aims to identify architectures that more effectively support the development of multiuser immersive learning scenarios. Second, this study takes up the challenge to define and assemble more flexible architectures that cater for fast and easy development, which will become important in the current period of economic breakdown. Third, this study describes how such architectures should enable research into guidelines for multiuser immersive learning scenario design and development. This study outlines a method for defining and setting up such architectures by using experts and existing literature. [ABSTRACT FROM AUTHOR]

The need to support users of the Internet with the selection of information is becoming more important. Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research questions regarding efficiency and effectiveness deal with adequate navigation support through recommender systems. To answer some of these questions an experiment was set up within an Introduction Psychology course of the Open University of the Netherlands. Around 250 students participated in this study and were monitored over an experimental period of four months. All were provided the same course materials, but only half of them were supported with a personalised recommender system. This study examined the effects of the navigation support on the completion of learning activities (effectiveness), needed time to comply them (efficiency), actual use of and satisfaction with the system, and the variety of learning paths. The recommender system positively influenced all measures, by having significant effects on efficiency, satisfaction and variety. [ABSTRACT FROM AUTHOR]

In an e-learning environment there is a need to integrate various e-learning services like assessment services, collaboration services, learning design services and communication services. In this article we present the design and implementation of a generic integrative service framework, called CopperCore Service Integration (CCSI). We will concentrate on the integration of two services: CopperCore, an IMS Learning Design service and an IMS Question and Test Interoperability service called Assessment Provision through Interoperable Segments (APIS). One of the design goals of the architecture was to minimize the intrusion for both the services as well as any legacy client that already uses these services. The result of this work is that the flow of learning activities can be made dependent on test results. [ABSTRACT FROM AUTHOR]

Abstract: Whole tasks for acquiring complex skills are often too difficult for novices. To solve this problem, process support divides the problem solving into phases, offers driving questions, and provides feedback. A multimedia program was used to teach sophomore law students (N =82) to prepare and carry out a plea. In a randomised 2×2 design with the factors number of phases and availability of driving questions, students solving a task with fewer phases performed better and more efficiently than students exposed to more phases. Also, students receiving driving questions performed better, although not more efficiently than students not receiving such questions. The results indicate that whole tasks should be accompanied by process support, although task characteristics might restrict the benefits of driving questions in this support. [Copyright &y& Elsevier]

An instrument for measuring the complexity of learning tasks in the field of Law was developed and tested in 3 experiments. In Experiments 1 and 2, teachers used the card-sort method to rate the complexity of learning tasks. Based on the outcomes, a benchmark scale with 4 criterion tasks was used in Experiment 3. The results showed the benchmark instrument to be valid and easy to use, allowing instructional designers to design competency-based learning environments that better take task complexity into account. The Instructional Design model, in which teachers determine task complexity, is briefly described. [ABSTRACT FROM AUTHOR]

Lifelong learners who select learning activities to attain certain learning goals need to know which are suitable and in which sequence they should be performed. Learners need support in this way-finding process, and we argue that this could be provided by using Personalised Recommender Systems (PRSs). To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way-finding presents personalised recommendations in relation to information about learning goals, learning activities and learners. A PRS has been developed according to this model, and recommends to learners the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed. [ABSTRACT FROM AUTHOR]