Abstract: Innovation plays an important role in economic
growth and development. Evolutionary economics has entrepreneurs
at the centre of the innovation system, but includes all other
participants as contributors to the performance of the innovation
system. Education and training institutions, one of the participants in
the innovation system, contributes in different ways to human capital.
The gap in literature on the competence building as part of human
capital in the analysis of innovation systems is addressed in this
paper. The Mpumalanga Province of South Africa is used as a case
study. It was found that the absence of a university, the level of
education, the quality and performance in the education sector and
the condition of the education infrastructure have not been conducive
to learning.
Abstract: The explosion of the World Wide Web and the
electronic trend of university teaching have transformed the learning
style to become more learner-centered, which has popularized the
digital delivery of mediated lectures as an alternative or an adjunct to
traditional lectures. Despite its potential and popularity, virtual
lectures have not been adopted yet in Jordanian universities. This
research aimed to fill this gap by studying the factors that influence
students’ willingness to accept virtual lectures in one Jordanian
University. A quantitative approach was followed, by obtaining 216
survey responses and statistically applying the UTAUT model with
some modifications. Results revealed that performance expectancy,
effort expectancy, social influences, and self-efficacy could
significantly influence students’ attitudes towards virtual lectures.
Additionally, Facilitating conditions and attitudes towards virtual
lectures were found with significant influence on students’ intention
to take virtual lectures. Research implications and future work were
specified afterwards.
Abstract: This paper introduces a proposal scheme for an
Intelligent System applied to Pedagogical Advising using Case-Based
Reasoning, to find consolidated solutions before used for the new
problems, making easier the task of advising students to the
pedagogical staff. We do intend, through this work, introduce the
motivation behind the choices for this system structure, justifying the
development of an incremental and smart web system who learns
bests solutions for new cases when it’s used, showing technics and
technology.
Abstract: This article discusses ways to implement a
differentiated approach to developing academic motivation for
mathematical studies which relies on defining the primary structural
characteristics of motivation. The following characteristics are
considered: features of realization of cognitive activity, meaningmaking
characteristics, level of generalization and consistency of
knowledge acquired by personal experience. The assessment of the
present level of individual student understanding of each component
of academic motivation is the basis for defining the relevant
educational strategy for its further development.
Abstract: In the past few decades, the field of chemistry
education has grown tremendously and researches indicated that after
traditional chemistry instruction students often lacked deep
conceptual understanding and failed to integrate their ideas into
coherent conceptual framework. For several concepts in chemistry,
students at all levels have demonstrated difficulty in changing their
initial perceptions. Their perceptions are most often wrong and don't
agree with correct scientific concepts. This study explored the
effectiveness of intervention discussion sections for a college general
chemistry course designed to apply research on students
preconceptions, knowledge integration and student explanation.
Three interventions discussions lasting three hours on bond energy
and spontaneity were done tested and intervention (treatment)
students’ performances were compared with that of control group
which did not use the experimental pedagogy. Results indicated that
this instruction which was capable of identifying students'
misconceptions, initial conceptions and integrating those ideas into
class discussion led to enhanced conceptual understanding and better
achievement for the experimental group.
Abstract: This study aimed to identify the alignment of
understanding and assessment practices among secondary school
teachers. The study was carried out using quantitative descriptive
study. The sample consisted of 164 teachers who taught Form 1 and 2
from 11 secondary schools in the district of North Kinta, Perak,
Malaysia. Data were obtained from 164 respondents who answered
Expectation Alignment Understanding and Practices of School
Assessment (PEKDAPS) questionnaire. The data were analysed
using SPSS 17.0+. The Cronbach’s alpha value obtained through
PEKDAPS questionnaire pilot study was 0.86. The results showed
that teachers' performance in PEKDAPS based on the mean value
was less than 3, which means that perfect alignment does not occur
between the understanding and practices of school assessment. Two
major PEKDAPS sub-constructs of articulation across grade and age
and usability of the system were higher than the moderate alignment
of the understanding and practices of school assessment (Min=2.0).
The content focused of PEKDAPs sub-constructs which showed
lower than the moderate alignment of the understanding and practices
of school assessment (Min=2.0). Another two PEKDAPS subconstructs
of transparency and fairness and the pedagogical
implications showed moderate alignment (2.0). The implications of
the study is that teachers need to fully understand the importance of
alignment among components of assessment, learning and teaching
and learning objectives as strategies to achieve quality assessment
process.
Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Learning Management System (LMS) is the system
which uses to manage the learning in order to grouping the content
and learning activity between the lecturer and learner including
online examination and evaluation. Nowadays, it is the borderless
learning era so the learning activities can be accessed from
everywhere in the world and also anytime via the information
technology and media. The learner can easily access to the
knowledge so the different in time and distance is not a constraint for
learning anymore.
The learning pattern which was used in this research is the
integration of the in-class learning and online learning via internet
and will be able to monitor the progress by the Learning management
system which will create the fast response and accessible learning
process via the social media. In order to increase the capability and
freedom of the learner, the system can show the current and history
of the learning document, video conference and also has the chat
room for the learner and lecturer to interact to each other.
So the objectives of the “The Design and Applied of Learning
Management System via Social Media on Internet: Case Study of
Operating System for Business Subject” are to expand the
opportunity of learning and to increase the efficiency of learning as
well as increase the communication channel between lecturer and
student. The data of this research was collect from 30 users of the
system which are students who enroll in the subject. And the result of
the research is in the “Very Good” which is conformed to the
hypothesis.
Abstract: Moodle is an open source learning management
system that enables creation of a powerful and flexible learning
environment. Many organizations, especially learning institutions
have customized Moodle open source LMS for their own use. In
general open source LMSs are of great interest due to many
advantages they offer in terms of cost, usage and freedom to
customize to fit a particular context. Tanzania Secondary School e-
Learning (TanSSe-L) system is the learning management system for
Tanzania secondary schools. TanSSe-L system was developed using
a number of methods, one of them being customization of Moodle
Open Source LMS. This paper presents few areas on the way Moodle
OS LMS was customized to produce a functional TanSSe-L system
fitted to the requirements and specifications of Tanzania secondary
schools’ context.
Abstract: The implementation of e-assessment as tool to support
the process of teaching and learning in university has become a
popular technological means in universities. E-Assessment provides
many advantages to the users especially the flexibility in teaching and
learning. The e-assessment system has the capability to improve its
quality of delivering education. However, there still exists a
drawback in terms of security which limits the user acceptance of the
online learning system. Even though there are studies providing
solutions for identified security threats in e-learning usage, there is no
particular model which addresses the factors that influences the
acceptance of e-assessment system by lecturers from security
perspective. The aim of this study is to explore security aspects of eassessment
in regard to the acceptance of the technology. As a result
a conceptual model of secure acceptance of e-assessment is proposed.
Both human and security factors are considered in formulation of this
conceptual model. In order to increase understanding of critical issues
related to the subject of this study, interpretive approach involving
convergent mixed method research method is proposed to be used to
execute the research. This study will be useful in providing more
insightful understanding regarding the factors that influence the user
acceptance of e-assessment system from security perspective.
Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
Abstract: This paper describes the tradeoffs and the design from
scratch of a self-contained, easy-to-use health dashboard software
system that provides customizable data tracking for patients in smart
homes. The system is made up of different software modules and
comprises a front-end and a back-end component. Built with HTML,
CSS, and JavaScript, the front-end allows adding users, logging into
the system, selecting metrics, and specifying health goals. The backend
consists of a NoSQL Mongo database, a Python script, and a
SimpleHTTPServer written in Python. The database stores user
profiles and health data in JSON format. The Python script makes use
of the PyMongo driver library to query the database and displays
formatted data as a daily snapshot of user health metrics against
target goals. Any number of standard and custom metrics can be
added to the system, and corresponding health data can be fed
automatically, via sensor APIs or manually, as text or picture data
files. A real-time METAR request API permits correlating weather
data with patient health, and an advanced query system is
implemented to allow trend analysis of selected health metrics over
custom time intervals. Available on the GitHub repository system,
the project is free to use for academic purposes of learning and
experimenting, or practical purposes by building on it.
Abstract: The organizations in the knowledge economy era have
recognized the importance of building knowledge assets for
sustainable growth and development. In comparison to other
industries, Information Technology (IT) enterprises, holds an edge in
developing an effective Knowledge Management (KM) programmethanks
to their in-house technological abilities. This paper tries to
study the various knowledge based incentive programmes and its
effect on Knowledge Sharing and Learning in the context of the
Indian IT sector. A conceptual model is developed linking KM
Incentives, Knowledge Sharing and Learning. A questionnaire study
is conducted to collect primary data from the knowledge workers of
the IT organizations located in India. The data was analysed using
Structural Equation Modeling using Partial Least Square method. The
results show a strong influence of knowledge management incentives
on knowledge sharing and an indirect influence on learning.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: Social Media (SM) is websites increasingly popular
and built to allow people to express themselves and to interact
socially with others. Most SMT are dominated by youth particularly
College students. The proliferation of popular social media tools,
which can accessed from any communication devices has become
pervasive in the lives of today’s student life. Connecting traditional
education to social media tools are a relatively new era and any
collaborative tool could be used for learning activities. This study
focuses (i) how the social media tools are useful for the learning
activities of the students of faculty of medicine in King Khalid
University (ii) whether the social media affects the collaborative
learning with interaction among students, among course instructor,
their engagement, perceived ease of use and perceived ease of
usefulness (TAM) (iii) overall, the students satisfy with this
collaborative learning through Social media.
Abstract: Analyzing DNA microarray data sets is a great
challenge, which faces the bioinformaticians due to the complication
of using statistical and machine learning techniques. The challenge
will be doubled if the microarray data sets contain missing data,
which happens regularly because these techniques cannot deal with
missing data. One of the most important data analysis process on
the microarray data set is feature selection. This process finds the
most important genes that affect certain disease. In this paper, we
introduce a technique for imputing the missing data in microarray
data sets while performing feature selection.
Abstract: The use of information tools is a common activity for
students of any educational stage when they encounter online
learning activities. Finding the relevant information for particular
learning tasks is the topic of this paper as it investigates the use of
information tools for a group of student participants. The paper
describes and discusses the results with particular implications for
use in higher education, and the findings suggest that improvement in
assessment design and subsequent student learning may be achieved
by structuring the purposefulness of information tools usage and
online reading behaviors of university students.
Abstract: This research focused on comparing the critical
thinking of the teacher students before and after using Miller’s Model
learning activities and investigating their opinions. The sampling
groups were (1) fourth year 33 student teachers majoring in Early
Childhood Education and enrolling in semester 1 of academic year
2013 (2) third year 28 student teachers majoring in English and
enrolling in semester 2 of academic year 2013 and (3) third year 22
student teachers majoring in Thai and enrolling in semester 2 of
academic year 2013. The research instruments were (1) lesson plans
where the learning activities were settled based on Miller’s Model (2)
critical thinking assessment criteria and (3) a questionnaire on
opinions towards Miller’s Model based learning activities. The
statistical treatment was mean, deviation, different scores and T-test.
The result unfolded that (1) the critical thinking of the students after
the assigned activities was better than before and (2) the students’
opinions towards the critical thinking improvement activities based
on Miller’s Model ranged from the level of high to highest.