Abstract: The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
Abstract: In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.
Abstract: Term Extraction, a key data preparation step in Text
Mining, extracts the terms, i.e. relevant collocation of words,
attached to specific concepts (e.g. genetic-algorithms and decisiontrees
are terms associated to the concept “Machine Learning" ). In
this paper, the task of extracting interesting collocations is achieved
through a supervised learning algorithm, exploiting a few
collocations manually labelled as interesting/not interesting. From
these examples, the ROGER algorithm learns a numerical function,
inducing some ranking on the collocations. This ranking is optimized
using genetic algorithms, maximizing the trade-off between the false
positive and true positive rates (Area Under the ROC curve). This
approach uses a particular representation for the word collocations,
namely the vector of values corresponding to the standard statistical
interestingness measures attached to this collocation. As this
representation is general (over corpora and natural languages),
generality tests were performed by experimenting the ranking
function learned from an English corpus in Biology, onto a French
corpus of Curriculum Vitae, and vice versa, showing a good
robustness of the approaches compared to the state-of-the-art Support
Vector Machine (SVM).
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.
Abstract: The success of an e-learning system is highly
dependent on the quality of its educational content and how effective,
complete, and simple the design tool can be for teachers. Educational
modeling languages (EMLs) are proposed as design languages
intended to teachers for modeling diverse teaching-learning
experiences, independently of the pedagogical approach and in
different contexts. However, most existing EMLs are criticized for
being too abstract and too complex to be understood and manipulated
by teachers. In this paper, we present a visual EML that simplifies the
process of designing learning scenarios for teachers with no
programming background. Based on the conceptual framework of the
activity theory, our resulting visual EML focuses on using Domainspecific
modeling techniques to provide a pedagogical level of
abstraction in the design process.
Abstract: In today's world where everything is rapidly changing
and information technology is high in development, many features of culture, society, politic and economy has changed. The advent of
information technology and electronic data transmission lead to easy communication and fields like e-learning and e-commerce, are
accessible for everyone easily. One of these technologies is virtual
training. The "quality" of such kind of education systems is critical. 131 questionnaires were prepared and distributed among university
student in Toba University. So the research has followed factors that affect the quality of learning from the perspective of staff, students, professors and this type of university. It is concluded that the important factors in virtual training are the quality of professors, the
quality of staff, and the quality of the university. These mentioned factors were the most prior factors in this education system and
necessary for improving virtual training.
Abstract: A key requirement for e-learning materials is
reusability and interoperability, that is the possibility to use at least
part of the contents in different courses, and to deliver them trough
different platforms. These features make possible to limit the cost of
new packages, but require the development of material according to
proper specifications. SCORM (Sharable Content Object Reference
Model) is a set of guidelines suitable for this purpose. A specific
adaptation project has been started to make possible to reuse existing
materials. The paper describes the main characteristics of SCORM
specification, and the procedure used to modify the existing material.
Abstract: A learning management system (commonly
abbreviated as LMS) is a software application for the administration,
documentation, tracking, and reporting of training programs,
classroom and online events, e-learning programs, and training
content (Ellis 2009). (Hall 2003) defines an LMS as \"software that
automates the administration of training events. All Learning
Management Systems manage the log-in of registered users, manage
course catalogs, record data from learners, and provide reports to
management\". Evidence of the worldwide spread of e-learning in
recent years is easy to obtain. In April 2003, no fewer than 66,000
fully online courses and 1,200 complete online programs were listed
on the TeleCampus portal from TeleEducation (Paulsen 2003). In the
report \" The US market in the Self-paced eLearning Products and
Services:2010-2015 Forecast and Analysis\" The number of student
taken classes exclusively online will be nearly equal (1% less) to the
number taken classes exclusively in physical campuses. Number of
student taken online course will increase from 1.37 million in 2010 to
3.86 million in 2015 in USA. In another report by The Sloan
Consortium three-quarters of institutions report that the economic
downturn has increased demand for online courses and programs.
Abstract: This paper proposes an improvement method of classification
efficiency in a classification model. The model is used
in a risk search system and extracts specific labels from articles
posted at bulletin board sites. The system can analyze the important
discussions composed of the articles. The improvement method
introduces ensemble learning methods that use multiple classification
models. Also, it introduces expressions related to the specific labels
into generation of word vectors. The paper applies the improvement
method to articles collected from three bulletin board sites selected
by users and verifies the effectiveness of the improvement method.
Abstract: This paper describes the results and implications of a correlational study of learning styles and learner satisfaction. The relationship of these empirical concepts was examined in the context of traditional versus e-blended modes of course delivery in an introductory graduate research course. Significant results indicated that the visual side of the visual-verbal dimension of students- learning style(s) was positively correlated to satisfaction with themselves as learners in an e-blended course delivery mode and negatively correlated to satisfaction with the classroom environment in the context of a traditional classroom course delivery mode.
Abstract: There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.
Abstract: The principal focus of this study is on the
measurement and analysis of labor learnings in Pakistan. The study
at the aggregate economy level focus on the labor productivity
movements and at large-scale manufacturing level focus on the cost
structure, with isolating the contribution of the learning curve. The
analysis of S-shaped curve suggests that learnings are only below one
half of aggregate learning curve and other half shows the retardation
in learning, hence retardation in productivity movements. The study
implies the existence of learning economies in term of cost reduction
that is input cost per unit produced decreases by 0.51 percent every
time the cumulative production output doubles.
Abstract: This research aims to develop and evaluate a training
course to promote learning activities of 2nd year, Suan Sunandha
Rajabhat University, faculty of education students using multiple
intelligences theory. The process is divided into two phases: Phase 1
development of training course to promote learning activities
consisting of principles, objectives of the course, structure, training
duration, content, training materials, training activities, media
training, monitoring, measurement and evaluation quality of the
course. Phase 2 evaluation efficiency of training course was to use
the improved curriculum with experimental group which is 2nd year,
Suan Sunandha Rajabhat University, faculty of education students
was drawn randomly 152 students. The experimental pattern was
randomized Control Group Pre-Test Post-Test Design, Analysis Data
by t-Test with the software SPFSS for Windows. Research has shown
that: 1). the ability of teaching and learning according to the theory of
multiple intelligences after training is higher than before training
significantly in statistic at .01 level, 2). The satisfaction of students
to the training courses was overall at the highest level.
Abstract: This research investigates the use of digital technology
namely interactive multimedia in effective art education provided by
museum. Several multimedia experience examples created for art
education are study case subjected to assistance audiences- learning
within the context of existing theory in the field of interactive
multimedia.
Abstract: The pedagogy project has been proven as an active
learning method, which is used to develop learner-s skills and
knowledge.The use of technology in the learning world, has filed
several gaps in the implementation of teaching methods, and online
evaluation of learners. However, the project methodology presents
challenges in the assessment of learners online.
Indeed, interoperability between E-learning platforms (LMS) is
one of the major challenges of project-based learning assessment.
Firstly, we have reviewed the characteristics of online assessment
in the context of project-based teaching. We addressed the
constraints encountered during the peer evaluation process.
Our approach is to propose a meta-model, which will describe a
language dedicated to the conception of peer assessment scenario in
project-based learning. Then we illustrate our proposal by an
instantiation of the meta-model through a business process in a
scenario of collaborative assessment on line.
Abstract: This paper aims at identifying and analyzing the
knowledge transmission channels in textile and clothing clusters
located in Brazil and in Europe. Primary data was obtained through
interviews with key individuals. The collection of primary data was
carried out based on a questionnaire with ten categories of indicators
of knowledge transmission. Secondary data was also collected
through a literature review and through international organizations
sites. Similarities related to the use of the main transmission channels
of knowledge are observed in all cases. The main similarities are:
influence of suppliers of machinery, equipment and raw materials;
imitation of products and best practices; training promoted by
technical institutions and businesses; and cluster companies being
open to acquire new knowledge. The main differences lie in the
relationship between companies, where in Europe the intensity of this
relationship is bigger when compared to Brazil. The differences also
occur in importance and frequency of the relationship with the
government, with the cultural environment, and with the activities of
research and development. It is also found factors that reduce the
importance of geographical proximity in transmission of knowledge,
and in generating trust and the establishment of collaborative
behavior.
Abstract: The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).