Effects of e-learning on Students’ Motivation☆

Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.

Data mining for providing a personalized learning path in creativity: An application of decision trees

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

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Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

A number of systematic reviews were conducted with respect to social media on the one hand, and the Technology Acceptance Model (TAM) application, on the other hand. However, analyzing the TAM in social media context is still questioned and requires further examination. The main objective of this systematic review is to analyze the up-to-date social media studies that involved the TAM as the primary theoretical model. In that, 57 research articles ranged between 2009 and 2018 were critically analyzed. The main research findings indicated that studying social media adoption and usage among students was the most frequent research problem tackled. Additionally, the majority of the analyzed studies have been extended with external factors. It was also revealed that perceived enjoyment, subjective norm, self-efficacy, perceived critical mass, perceived connectedness, perceived security, and perceived trust were the most frequent factors that significantly extended the TAM. Besides, questionnaire surveys were found to be the most commonly used methods for data collection. In addition, the majority of the analyzed studies were conducted in higher education environments. Furthermore, Facebook was found to be the most common social media tool used in most of the analyzed studies. To that end, the results of this systematic review afford a better understanding of the TAM-based social media studies and form an essential reference for future research in the social media context.

Employing the technology acceptance model in social media: A systematic review

Transparent Computing (TC) is becoming a promising paradigm in network computing era. Although many researchers believe that TC model has a high requirement for the communication bandwidth, there is no research on the communication bandwidth boundary or resource allocation, which impedes the development of TC. This paper focuses on studying an efficient transparent computing resource allocation model in an economic view. First, under the quality of experiments (QoE) ensured, the utility function of clients and transparent computing providers (TCPs) is constructed. After that, the demand boundary of communication bandwidth is analyzed under the ideal transparent computing model. Based on the above analyses, a resource allocation scheme based on double-sided combinational auctions (DCA) is proposed so that the resource can be shared by both the service side and the client side with the welfare of the whole society being maximized. Afterward, the results scheduled in different experimental scenarios are given, which verifies the effectiveness of the proposed strategy. Overall, this work provides an effective resource allocation model for optimizing the performance of TC.

A resource allocation model based on double-sided combinational auctions for transparent computing

Highlights? We design Personalized Learning Course Planner (PLCP) applied to E-learning systems. ? PLCP analyzes student's implicit requirements. ? Learning courses are organized according to characteristics and educational levels of students. ? English learning system consisting of PLCP is implemented. ? Learning effectiveness and student satisfaction is improved in experiment results. Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students' attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students' initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students' satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students' previous test scores and priorities encouraged students to concentrate on the lesson.

Personalized Learning Course Planner with E-learning DSS using user profile

E-learning systems have played an important role in the education field and have been widely employed in many educational institutions. Although the need to evaluate the quality of e-learning systems is emerging, there is currently no appropriate evaluation method due to the complicated correlations between quality attributes. This study develops a quality evaluation model that calculates the priority weights of each quality attribute while accounting for their correlations and evaluates the overall quality of a learning system with numerical results. First, the study constructs a quality attribute network that reflects the correlations between 4 main quality clusters and 19 sub-attributes. Second, it calculates the priority weights of the attributes using the Analytic Network Process (ANP). Finally, using the quality network and weights, this study evaluates three types of e-learning systems employed by Kyunghee Cyber University. The results indicate that the proposed evaluation method provides a mechanism for objectively analyzing and comparing the qualities of various kinds of learning systems and suggests guidelines for constructors and managers of learning systems.

Quality evaluation for multimedia contents of e-learning systems using the ANP approach on high speed network

As an influence of social media service (SMS) is extending enormously and SMS becomes an important marketing way and a field of modern industry beyond a method to transfer informations. Under these circumstances that the influence of SMS is getting bigger bigger, which factors make users participate and work in the social media service and how relatively important these factors are between them need to be examined. But SMS are different from the existing information systems. Most information systems are task-oriented, that is, the systems aim to provide users with useful information for better decision making. Therefore, a different approach is required. In this research, we examine and select the main factors which affect users' social media service use, and evaluate relative importance degrees of each factor on the basis of relationships between the factors with Analytic Network Process (ANP).

Relative weight evaluation of the factors inducing social media service use

The quality offered by cloud computing services is becoming an issue of the utmost priority and, regardless of the cloud delivery models, the adoption of cloud services will depend on having the capability to ensure quality of service to the users. However, studies to find and select cloud computing services according to their service quality are still in their infancy. Here, we propose a system that calculates the priority weights for each quality attribute according to the quality preference of a user and the interrelation analysis results between the attributes, and reflects the weights in selecting the cloud computing service. To calculate the quality preference of the user, we applied a pairwise comparison matrix and an eigenvector of the matrix. Through the proposed system, users can easily perform the process of calculating the weights and selecting the best services according to their quality preference. The simulation results show that the weights of the quality attributes and the quality score ranking of the sample cloud computing services vary according to users' preferences and interrelations between attributes.

Quality evaluation and best service choice for cloud computing based on user preference and weights of attributes using the analytic network process

Drastical increase of a variety of information devices with networks are based on a rapid development and expansion of network infrastructures and technology. Cloud computing is a main technology which makes the information devices lighter and allows users to access their data and applications through a variety of networks. Under the circumstances that the importance and use of cloud computing system is rapidly increasing the virtualization technology becomes one of the key components consisting the cloud computing. Therefore, a quality of a variety of cloud computing systems is affected by the virtualization quality. Many factors which decide the virtualization quality and characteristics have been studied. However, when we apply the cloud computing system to our organization the priorities of the key factors should be decided and according to the priorities resourves must be alloted. In this paper, we suggested a relative weight evaluation process applying Analytic Network Process to analyze the interrelations between the key factors and calculate the relative weights of the factors. Especially, through the demonstration we showed that the interrelations between the factors affect the relative weights at large. With the proposed method we can find hidden priority and allot our resources and efforts more effectively.

Cheol-Rim Choi

Papers

Personalized Learning Course Planner with E-learning DSS using user profile

Quality evaluation for multimedia contents of e-learning systems using the ANP approach on high speed network

Relative weight evaluation of the factors inducing social media service use

Quality evaluation and best service choice for cloud computing based on user preference and weights of attributes using the analytic network process

Relative weight comparison between virtual key factors of cloud computing with analytic network process