Abstract: The paper focuses on the distance laboratory
organisation for training the electrical engineering staff and students
in the fields of electrical drive and power electronics. To support
online knowledge acquisition and professional enhancement, new
challenges in remote education based on an active learning approach
with self-assessment have been emerged by the authors. Following
the literature review and explanation of the improved assessment
methodology, the concept and technological basis of the labs
arrangement are presented. To decrease the gap between the distance
study of the up-to-date equipment and other educational activities in
electrical engineering, the improvements in the following-up the
learners’ progress and feedback composition are introduced. An
authoring methodology that helps to personalise knowledge
acquisition and enlarge Web-based possibilities is described.
Educational management based on self-assessment is discussed.
Abstract: This study aimed to 1) develop pre-service teachers’
leadership skills through camp-based learning, and 2) develop preservice
teachers’ teamwork skills through camp-based learning. An
applied research methodology was used. The target group was
derived from a purposive selection. It involved 32 fourth-year
students in Early Childhood Education Program enrolling a course
entitled Seminar in Early Childhood Education provided during
second semester of academic year 2013. The treatment was camp-based
learning activities which applied a PDCA process including
four stages: 1) plan, 2) do, 3) check, and 4) act. Research instruments
were a learning camp program, a camp-based learning management
plan, a 5-level assessment form for leadership skills and a 5-level
assessment form for assessing teamwork skills. Data were analyzed
using descriptive statistics. Results were: 1) pre-service teachers’
leadership skills yielded the before treatment average score at x= 3.4,
S.D.=0.6 2and the after-treatment average score at x 4.29 , S.D.=0.66
pre-service teachers’ teamwork skills yielded the before-treatment
average score at x=3.31, S.D.=0.60 and the after-treatment average
score at x=4.42, S.D.=0.66 Both differences were statistically
significant at the .05 level. Thus, the pre-service teachers’ leadership
and teamwork skills were significantly improved through the camp-based
learning approach.
Abstract: The use of information technology in education have
changed not only the learners learning style but also the way they
taught, where nowadays learners are connected with diversity of
information sources with means of knowledge available everywhere.
The advantage of network wireless technologies and mobility
technologies used in the education and learning processes lead to
mobile learning as a new model of learning technology. Currently,
most of mobile learning applications are developed for the formal
education and learning environment. Despite the long history and
large amount of research on mobile learning and instruction design
model still there is a need of well-defined process in designing
mobile learning applications. Based on this situation, this paper
emphasizes on identifying instruction design phase’s considerations
and influencing factors in developing mobile learning application.
This set of instruction design steps includes analysis, design,
development, implementation, evaluation and continuous has been
built from a literature study, with focus on standards for learning,
mobile application software quality and guidelines. The effort is part
of an Omani-funded research project investigating the development,
adoption and dissemination of mobile learning in Oman.
Abstract: Technology, multimedia in Open Educational
Resources, can contribute positively to student performance in an
online instructional environment. Student performance data of past
four years were obtained from an online course entitled Applied
Calculus (MA139). This paper examined the data to determine
whether multimedia (independent variable) had any impact on
student performance (dependent variable) in online math learning,
and how students felt about the value of the technology. Two groups
of student data were analyzed, group 1 (control) from the online
applied calculus course that did not use multimedia instructional
materials, and group 2 (treatment) of the same online applied calculus
course that used multimedia instructional materials. For the MA139
class, results indicate a statistically significant difference (p = .001)
between the two groups, where group 1 had a final score mean of
56.36 (out of 100), group 2 of 70.68. Additionally, student
testimonials were discussed in which students shared their experience
in learning applied calculus online with multimedia instructional
materials.
Abstract: Guided by the theory of learning styles, this study is
based on the development of a multimedia learning application for
students with mastery learning style. The learning material was
developed by applying a graduated difficulty learning strategy.
Algebra was chosen as the learning topic for this application. The
effectiveness of this application in helping students learn is measured
by giving a pre- and post-test. The result shows that students who
learn using the learning material that matches their preferred learning
style perform better than the students with a non-personalized
learning material.
Abstract: Children today use computer based application in
various activities especially for learning and education. Many of
these tools and application such as the Computer Aided
Pronunciation Training (CAPT) systems enable children to explore
and experience them with little supervision from the adults. In order
for these tools and application to have maximum effect on the
children’s learning and education, it must be attractive to the children
to use them. This could be achieved with the proper user interface
(UI) design. As children grow, so do their ability, taste and
preferences. They interact differently with these applications as they
grow older. This study reviews several articles on how age factors
influence the UI design. The review focuses on age related abilities
such as cognitive, literacy, concentration and feedback requirement.
We have also evaluated few of existing CAPT systems and determine
the influence of age-based factors on the interface design.
Abstract: This paper proposes a new teaching and learning approach-project and module based teaching and learning (PMBTL). The PMBTL approach incorporates the merits of project/problem based and module based learning methods, and overcomes the limitations of these methods. The correlation between teaching, learning, practice and assessment is emphasized in this approach, and new methods have been proposed accordingly. The distinct features of these new methods differentiate the PMBTL approach from conventional teaching approaches. Evaluation of this approach on practical teaching and learning activities demonstrates the effectiveness and stability of the approach in improving the performance and quality of teaching and learning. The approach proposed in this paper is also intuitive to the design of other teaching units.
Abstract: Red blood cells (RBCs) are among the most
commonly and intensively studied type of blood cells in cell biology.
Anemia is a lack of RBCs is characterized by its level compared to
the normal hemoglobin level. In this study, a system based image
processing methodology was developed to localize and extract RBCs
from microscopic images. Also, the machine learning approach is
adopted to classify the localized anemic RBCs images. Several
textural and geometrical features are calculated for each extracted
RBCs. The training set of features was analyzed using principal
component analysis (PCA). With the proposed method, RBCs were
isolated in 4.3secondsfrom an image containing 18 to 27 cells. The
reasons behind using PCA are its low computation complexity and
suitability to find the most discriminating features which can lead to
accurate classification decisions. Our classifier algorithm yielded
accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor
(K-NN) algorithm, support vector machine (SVM), and neural
network RBFNN, respectively. Classification was evaluated in highly
sensitivity, specificity, and kappa statistical parameters. In
conclusion, the classification results were obtained within short time
period, and the results became better when PCA was used.
Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.
Abstract: In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.
Abstract: Mobile Learning (M-Learning) is a new technology
which is to enhance current learning practices and activities for all
people especially students and academic practitioners UTP is
currently, implemented two types of learning styles which are
conventional and electronic learning. In order to improve current
learning approaches, it is necessary for UTP to implement m-learning
in UTP. This paper presents a study on the students- perceptions on
mobile utilization in the learning practices in UTP. Besides, this
paper also presents a survey that was conducted among 82 students
from System Analysis and Design (SAD) course in UTP. The survey
includes basic information of mobile devices that have been used by
the students, opinions on current learning practices and also the
opinions regarding the m-learning implementation in the current
learning practices especially in SAD course. Based on the results of
the survey, majority of the students are using the mobile devices that
can support m-learning environment. Other than that, students also
agreed that current learning practices are ineffective and they believe
that m-learning utilization can improve the effectiveness of current
learning practices.
Abstract: In the present study, a support vector machine (SVM) learning approach to character recognition is proposed. Simple
feature detectors, similar to those found in the human visual system, were used in the SVM classifier. Alphabetic characters were rotated
to 8 different angles and using the proposed cognitive model, all characters were recognized with 100% accuracy and specificity.
These same results were found in psychiatric studies of human character recognition.
Abstract: Modeling the behavior of the dialogue management in
the design of a spoken dialogue system using statistical methodologies
is currently a growing research area. This paper presents a work
on developing an adaptive learning approach to optimize dialogue
strategy. At the core of our system is a method formalizing dialogue
management as a sequential decision making under uncertainty whose
underlying probabilistic structure has a Markov Chain. Researchers
have mostly focused on model-free algorithms for automating the
design of dialogue management using machine learning techniques
such as reinforcement learning. But in model-free algorithms there
exist a dilemma in engaging the type of exploration versus exploitation.
Hence we present a model-based online policy learning
algorithm using interconnected learning automata for optimizing
dialogue strategy. The proposed algorithm is capable of deriving
an optimal policy that prescribes what action should be taken in
various states of conversation so as to maximize the expected total
reward to attain the goal and incorporates good exploration and
exploitation in its updates to improve the naturalness of humancomputer
interaction. We test the proposed approach using the most
sophisticated evaluation framework PARADISE for accessing to the
railway information system.
Abstract: Color image segmentation can be considered as a
cluster procedure in feature space. k-means and its adaptive
version, i.e. competitive learning approach are powerful tools
for data clustering. But k-means and competitive learning suffer
from several drawbacks such as dead-unit problem and need to
pre-specify number of cluster. In this paper, we will explore to
use competitive and cooperative learning approach to perform
color image segmentation. In competitive and cooperative
learning approach, seed points not only compete each other, but
also the winner will dynamically select several nearest
competitors to form a cooperative team to adapt to the input
together, finally it can automatically select the correct number
of cluster and avoid the dead-units problem. Experimental
results show that CCL can obtain better segmentation result.
Abstract: The development and use of mobile devices as well as its integration within education systems to deliver electronic contents and to support real-time communications was the focus of this research. In order to investigate the software engineering issues in using mobile devices a research on electronic content was initiated. The Developed MP3 mobile software solution was developed as a prototype for testing and developing a strategy for designing a usable m-learning environment. The mobile software solution was evaluated using mobile device using the link: http://projects.seeu.edu.mk/mlearn. The investigation also tested the correlation between the two mobile learning indicators: electronic content and attention, based on the Task Based learning instructional method. The mobile software solution ''M-Learn“ was developed as a prototype for testing the approach and developing a strategy for designing usable m-learning environment. The proposed methodology is about what learning modeling approach is more appropriate to use when developing mobile learning software.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: Learning is the acquisition of new mental schemata, knowledge, abilities and skills which can be used to solve problems potentially more successfully. The learning process is optimum when it is assisted and personalized. Learning is not a single activity, but should involve many possible activities to make learning become meaningful. Many e-learning applications provide facilities to support teaching and learning activities. One way to identify whether the e-learning system is being used by the learners is through the number of hits that can be obtained from the e-learning system's log data. However, we cannot rely solely to the number of hits in order to determine whether learning had occurred meaningfully. This is due to the fact that meaningful learning should engage five characteristics namely active, constructive, intentional, authentic and cooperative. This paper aims to analyze the e-learning activities that is meaningful to learning. By focusing on the meaningful learning characteristics, we match it to the corresponding Moodle e-learning activities. This analysis discovers the activities that have high impact to meaningful learning, as well as activities that are less meaningful. The high impact activities is given high weights since it become important to meaningful learning, while the low impact has less weight and said to be supportive e-learning activities. The result of this analysis helps us categorize which e-learning activities that are meaningful to learning and guide us to measure the effectiveness of e-learning usage.
Abstract: The current paper presents the findings of a research
study on learners- barriers and motivators engaged into blended
programs in a workplace context. In this study, the participants were
randomly assigned to one of four parallel e-learning courses, each of
which was delivered using a different learning strategy. Data were
collected through web-based and telephone surveys developed by the
researchers. The results showed that vague instruction, time
management, and insufficient feedback were the top-most barriers to
blended learning. The major motivators for blended learning included
content relevance, flexibility in time, and the ability to work at own
pace.
Abstract: As the web continues to grow exponentially, the idea
of crawling the entire web on a regular basis becomes less and less
feasible, so the need to include information on specific domain,
domain-specific search engines was proposed. As more information
becomes available on the World Wide Web, it becomes more difficult
to provide effective search tools for information access. Today,
people access web information through two main kinds of search
interfaces: Browsers (clicking and following hyperlinks) and Query
Engines (queries in the form of a set of keywords showing the topic
of interest) [2]. Better support is needed for expressing one's
information need and returning high quality search results by web
search tools. There appears to be a need for systems that do reasoning
under uncertainty and are flexible enough to recover from the
contradictions, inconsistencies, and irregularities that such reasoning
involves. In a multi-view problem, the features of the domain can be
partitioned into disjoint subsets (views) that are sufficient to learn the
target concept. Semi-supervised, multi-view algorithms, which
reduce the amount of labeled data required for learning, rely on the
assumptions that the views are compatible and uncorrelated. This
paper describes the use of semi-structured machine learning approach
with Active learning for the “Domain Specific Search Engines". A
domain-specific search engine is “An information access system that
allows access to all the information on the web that is relevant to a
particular domain. The proposed work shows that with the help of
this approach relevant data can be extracted with the minimum
queries fired by the user. It requires small number of labeled data and
pool of unlabelled data on which the learning algorithm is applied to
extract the required data.
Abstract: RoboCup Rescue simulation as a large-scale Multi
agent system (MAS) is one of the challenging environments for
keeping coordination between agents to achieve the objectives
despite sensing and communication limitations. The dynamicity of
the environment and intensive dependency between actions of
different kinds of agents make the problem more complex. This point
encouraged us to use learning-based methods to adapt our decision
making to different situations. Our approach is utilizing
reinforcement leaning. Using learning in rescue simulation is one of
the current ways which has been the subject of several researches in
recent years. In this paper we present an innovative learning method
implemented for Police Force (PF) Agent. This method can cope
with the main difficulties that exist in other learning approaches.
Different methods used in the literature have been examined. Their
drawbacks and possible improvements have led us to the method
proposed in this paper which is fast and accurate. The Brain
Emotional Learning Based Intelligent Controller (BELBIC) is our
solution for learning in this environment. BELBIC is a
physiologically motivated approach based on a computational model
of amygdale and limbic system. The paper presents the results
obtained by the proposed approach, showing the power of BELBIC
as a decision making tool in complex and dynamic situation.