Effect of bandwidth knowledge of results on the learning of a grip force control task

An experiment was conducted to investigate the persistence of the effect of ""bandwidth knowledge of results (KR)"" manipulated during the learning phase of performing a manual force-control task. The experiment consisted of two phases, an acquisition phase with the goal of maintaining 60% maximum force in 30 trials, and a second phase with the objective of maintaining 40% of maximum force in 20 further trials. There were four bandwidths of KR: when performance error exceeded 5, 10, or 15% of the target, and a control group (0% bandwidth). Analysis showed that 5, 10, and 15% bandwidth led to better performance than 0% bandwidth KR at the beginning of the second phase and persisted during the extended trials.

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Learning about knowledge management for improving environmental impact assessment in a government agency: The Western Australian experience

How does knowledge management (KM) by a government agency responsible for environmental impact assessment (EIA) potentially contribute to better environmental assessment and management practice? Staff members at government agencies in charge of the EIA process are knowledge workers who perform judgement-oriented tasks highly reliant on individual expertise, but also grounded on the agency`s knowledge accumulated over the years. Part of an agency`s knowledge can be codified and stored in an organizational memory, but is subject to decay or loss if not properly managed. The EIA agency operating in Western Australia was used as a case study. Its KM initiatives were reviewed, knowledge repositories were identified and staff surveyed to gauge the utilisation and effectiveness of such repositories in enabling them to perform EIA tasks. Key elements of KM are the preparation of substantive guidance and spatial information management. It was found that treatment of cumulative impacts on the environment is very limited and information derived from project follow-up is not properly captured and stored, thus not used to create new knowledge and to improve practice and effectiveness. Other opportunities for improving organizational learning include the use of after-action reviews. The learning about knowledge management in EIA practice gained from Western Australian experience should be of value to agencies worldwide seeking to understand where best to direct their resources for their own knowledge repositories and environmental management practice. (C) 2011 Elsevier Ltd. All rights reserved.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

Learning for fun: The unique contribution of educational leisure experiences

What do visitors want or expect from an educational leisure activity such as a visit to a museum, zoo, aquarium or other such experience? Is it to learn something or to experience learning? This paper uses the term 'learning for fun' to refer to the phenomenon in which visitors engage in a learning experience because they value and enjoy the process of learning itself. Five propositions regarding the nature of learning for fun are discussed, drawing on quantitative and qualitative data from visitors to a range of educational leisure activities. The commonalities between learning for fun and other theoretical constructs such as 'experience,' 'flow', 'intrinsic motivation', and 'curiosity' are explored. It is concluded that learning for fun is a unique and distinctive offering of educational leisure experiences, with implications for future research and experience design.

The interaction between technologies and society: Lessons learnt from 160 evolutionary years of online news services

In mapping the evolutionary process of online news and the socio-cultural factors determining this development, this paper has a dual purpose. First, in reworking the definition of “online communication”, it argues that despite its seemingly sudden emergence in the 1990s, the history of online news started right in the early days of the telegraphs and spread throughout the development of the telephone and the fax machine before becoming computer-based in the 1980s and Web-based in the 1990s. Second, merging macro-perspectives on the dynamic of media evolution by DeFleur and Ball-Rokeach (1989) and Winston (1998), the paper consolidates a critical point for thinking about new media development: that something technically feasible does not always mean that it will be socially accepted and/or demanded. From a producer-centric perspective, the birth and development of pre-Web online news forms have been more or less generated by the traditional media’s sometimes excessive hype about the power of new technologies. However, placing such an emphasis on technological potentials at the expense of their social conditions not only can be misleading but also can be detrimental to the development of new media, including the potential of today’s online news.

Parameter setting and statistical learning

Three main models of parameter setting have been proposed: the Variational model proposed by Yang (2002; 2004), the Structured Acquisition model endorsed by Baker (2001; 2005), and the Very Early Parameter Setting (VEPS) model advanced by Wexler (1998). The VEPS model contends that parameters are set early. The Variational model supposes that children employ statistical learning mechanisms to decide among competing parameter values, so this model anticipates delays in parameter setting when critical input is sparse, and gradual setting of parameters. On the Structured Acquisition model, delays occur because parameters form a hierarchy, with higher-level parameters set before lower-level parameters. Assuming that children freely choose the initial value, children sometimes will miss-set parameters. However when that happens, the input is expected to trigger a precipitous rise in one parameter value and a corresponding decline in the other value. We will point to the kind of child language data that is needed in order to adjudicate among these competing models.

Mummy! Ball! Fish!: Why English-learning children produce nouns earlier than verbs

When English-learning children begin using words the majority of their early utterances (around 80%) are nouns. Compared to nouns, there is a paucity of verbs or non-verb relational words, such as 'up' meaning 'pick me up'. The primary explanations to account for these differences in use either argue in support of a 'cognitive account', which claims that verbs entail more cognitive complexity than nouns, or they provide evidence challenging this account. In this paper I propose an additional explanation for children's noun/verb asymmetry. Presenting a 'multi-modal account' of word-learning based on children's gesture and word combinations, I show that at the one-word stage English-learning children use gestures to express verb-like elements which leaves their words free to express noun-like elements.

Fundamental Skills in Physics: Embedded Learning

Student attitudes towards a subject affect their learning. For students in physics service courses, relevance is emphasised by vocational applications. A similar strategy is being used for students who aspire to continued study of physics, in an introduction to fundamental skills in experimental physics – the concepts, computational tools and practical skills involved in appropriately obtaining and interpreting measurement data. An educational module is being developed that aims to enhance the student experience by embedding learning of these skills in the practicing physicist’s activity of doing an experiment (gravity estimation using a rolling pendulum). The group concentrates on particular skills prompted by challenges such as: • How can we get an answer to our question? • How good is our answer? • How can it be improved? This explicitly provides students the opportunity to consider and construct their own ideas. It gives them time to discuss, digest and practise without undue stress, thereby assisting them to internalise core skills. Design of the learning activity is approached in an iterative manner, via theoretical and practical considerations, with input from a range of teaching staff, and subject to trials of prototypes.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.