The impact of social media use in collaborative learning towards learning performance among research students

The use of social media for active collaborative learning and engagement to affect learning performance seems to be one of the examined topics in Information Systems domain compared to other technology adoption. However, social media uses distraction from studies and affects study habits, thus, using social media results in academic difficulties. Research students seldom use social media for educational purposes, also they do not use it interactively for collaborative learning and academic purposes. Previous frameworks and models of social media use have many significant negative impacts on student engagement, collaborative learning and learning performance. Thus, this research aims to determine the interactive factors for active collaborative learning and engagement as well as perceptual factors, and social media use for active collaborative learning and engagement to affect learning performance. This study proposes a theoretical model based on the theory of constructivism and theory of Technology Acceptance Model (TAM). A mixed method quantitative and qualitative was used to conduct a survey and interview of samples at five public universities in Malaysia. Data were analysed using AMOS, SPSS and Structural Equation Modelling (SEM) to investigate causal and mediating relationships between variables. Findings of the research revealed that interaction among research students, and interaction with lecturers or supervisors enhance active collaborative learning and engagement were significant at 60% and 73% respectively. It also indicates that active collaborative learning and engagement which affect the learning performance of research students achieved significant ratio of 74%. In addition, perceived ease of use and usefulness define a person’s social media use for active collaborative learning and engagement that enhances satisfaction and affect the learning performance of research students were 71% and 74% respectively. It is found that perceived usefulness and satisfaction of research students are insignificant because some students use social media on social purposes not for educational purposes. Hence, it is important to raise awareness by the universities and lecturers for students to use social media as an active collaborative learning purpose as it will positively affect the learning performance of research students. Finally, the results indicate that the use of social media is significant for active collaborative learning and engagement which positively affect learning performance of research students of Malaysian Higher Education

Social Media Used in Higher Education: A Literature Review of Theoretical Models

Abstract— This research provides a literature review related with social media used for collaborative learning and engagement in turn, to affect students' academic performance in higher education. Therefore, the main objective of this research is to review models on social media use for active collaborative learning and engagement by interactive and perceptual factors in turn affecting the learning performance of research students. Therefore, this study conducted on two theories constructivism theory and Technology Acceptance Model (TAM). This paper conducted an analysis of studies dedicated to social media use for collaborative learning and engagement based on previous research problems of models, and the theories. According to the literature review and discussion in this research, we noted the full satisfaction of students was through the use of social media for active collaborative learning and engagement to positively affect their learning performance.Keywords— Social Media, Collaborative Learning, Students’ Academic Performance, Theoretical Model

A model of using social media for collaborative learning to enhance learners’ performance on learning

Social media has been always described as the channel through which knowledge is transmitted between communities and learners. This social media has been utilized by colleges in a way to encourage collaborative learning and social interaction. This study explores the use of social media in the process of collaborative learning through learning Quran and Hadith. Through this investigation, different factors enhancing collaborative learning in learning Quran and Hadith in the context of using social media are going to be examined. 340 respondents participated in this study. The structural equation modeling (SEM) was used to analyze the data obtained. Upon analysis and structural model validities, the study resulted in a model used for measuring the influences of the different variables. The study reported direct and indirect significant impacts of these variables on collaborative learning through the use of social media which might lead to a better performance by learner

Big data, modeling, simulation, computational platform and holistic approaches for the fourth industrial revolution

Naturally, the mathematical process starts from proving the existence and uniqueness of the solution by the using the theorem, corollary, lemma, proposition, dealing with the simple and non-complex model. Proving the existence and uniqueness solution are guaranteed by governing the infinite amount of solutions and limited to the implementation of a small-scale simulation on a single desktop CPU. Accuracy, consistency and stability were easily controlled by a small data scale. However, the fourth industrial can be described the mathematical process as the advent of cyber-physical systems involving entirely new capabilities for researcher and machines (Xing, 2017). In numerical perspective, the fourth industrial revolution (4iR) required the transition from a uncomplex model and small scale simulation to complex model and big data for visualizing the real-world application in digital dialectical and exciting opportunity. Thus, a big data analytics and its classification are a problem solving for these limitations. Some applications of 4iR will highlight the extension version in terms of models, derivative and discretization, dimension of space and time, behavior of initial and boundary conditions, grid generation, data extraction, numerical method and image processing with high resolution feature in numerical perspective. In statistics, a big data depends on data growth however, from numerical perspective, a few classification strategies will be investigated deals with the specific classifier tool. This paper will investigate the conceptual framework for a big data classification, governing the mathematical modeling, selecting the superior numerical method, handling the large sparse simulation and investigating the parallel computing on high performance computing (HPC) platform. The conceptual framework will benefit to the big data provider, algorithm provider and system analyzer to classify and recommend the specific strategy for generating, handling and analyzing the big data. All the perspectives take a holistic view of technology. Current research, the particular conceptual framework will be described in holistic terms. 4iR has ability to take a holistic approach to explain an important of big data, complex modeling, large sparse simulation and high performance computing platform. Numerical analysis and parallel performance evaluation are the indicators for performance investigation of the classification strategy. This research will benefit to obtain an accurate decision, predictions and trending practice on how to obtain the approximation solution for science and engineering applications. As a conclusion, classification strategies for generating a fine granular mesh, identifying the root causes of failures and issues in real time solution. Furthermore, the big data-driven and data transfer evolution towards high speed of technology transfer to boost the economic and social development for the 4iR (Xing, 2017; Marwala et al., 2017)

Towards adaptive e-learning among university students: by Applying Technology Acceptance Model (TAM)

E-learning is a form of education that is increasingly being used in higher education in the developed world. The aim of the study was to evaluating students’ satisfaction of e-Learning. In this research, we apply and use the theory of technology acceptance model (TAM). We employ structural equation modelling (SEM) approach with SmartPLS software to investigate students’ adoption process. Findings indicates that the perceived ease of use, perceived usefulness and intention to use e-learning among university students have a positive impact and substantially associated with learning performance and learning satisfaction. The study concludes that university students in Malaysia have positive perceptions towards e-learning and intend to practice it for educational purposes

Modeling cost saving and innovativeness for blockchain technology adoption by energy management

In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions

A cloud based framework for e-government implementation in developing countries

Cloud Computing technology is achieving a significant cost saving, business agility, and high scalability. However, it is a relatively new technology and its successful implementation in the governmental organizations needs careful consideration due to data sensitivity. Successful adoption of cloud-based solutions is the key for realizing the expected benefits of cloud computing technologies in the public agencies. The aim of this research is to develop a strategic framework to adopt cloud-based solutions in the public sector to improve e-government processes in developing countries. The purpose of the developed framework is to reduce the time and cost of the processes that contain interaction among governmental agencies and citizens through adopting cloud-based solutions. The framework was formulated based on the collected data analysis and the conclusions from experts' interviews. This study provides detailed guidelines to a successful launch and implementation of cloud-based solutions for e-government initiatives in the public sector

The effect of social media on researchers’ academic performance through collaborative learning in Malaysian higher education

This study aims to explain the way social media contributes to the enhancement of collaborative learning among researchers Malaysian higher education. The sample comprised of 723 researchers. The findings showed that introvert researchers perceive social media to help in increasing collaborative learning and improving their performance. These researchers are more inclined to communicate through social media as opposed to face-to-face. In addition, the sample researchers are inclined to utilize social media. Therefore, Malaysian higher education institutions are recommended to employ social media in enhancing the researchers’ collaborative learning. The researcher employs the use of theory of technology acceptance model (TAM) for this purpose. The results show that collaborative learning positively and significantly relates to impact intention to use social media for collaborative learning to improve performance of researchers in Malaysian higher education

A modeling of animal diseases through using artificial neural network

This paper studied the implementation of Artificial Neural Network (ANN) where it well-known recently in veterinary disease research field in Malaysia. The parameter identification under consideration is types of animal disease, types of species and locations of disease based on the Geographical Information System (GIS) data set. There are many types of animal diseases that affect farm animals in Malaysia. In this research, the method of multilayer perceptron neural network is used as main model since it is an effective solving method in predicting the future of veterinary disease. ANN has ability to visual animal diseases involving the computational model. The model is to present the rela-tionship between causes of the species and location and consequence of animal disease without emphasizing the process, considering the initial and boundary condition and considering the nature of the relations. The data collection of animal disease is considered as a large sparse data set. Therefore method of ANN is well suited for optimizing of the data, to train the data operational and to predict the parameter identification of animal disease. The output layers of ANN are plotted in SPSS software for statistical solution and MATLAB programming for sequential ANN implemented. The ANN will be compare to genetic algorithm for the performance and effectiveness of the method. The numerical simulation of ANN helps in future prediction of animal disease based on the species and location parameters

Numerical performance of healthy processing for HMF content in honey

The objective of this study is to develop a kinetic model correlating the effect of heating temperature and the duration of thermal treatment on HMF formation for different types of honey from different geographical locations. In this study, the experimental data from previous re-search papers for European and Asian honey was collected from year 1999 to 2012. The data was analysed and performed visually in graphical representation to draw the relationship between the factors. Then, a descriptive mathematical model was developed by using Math Work to correlate the parameters and the model was validated based on the data from Malaysian and European honey samples. The study showed that both heating temperature and duration could accelerate the production of HMF content in honey. The formation of HMF con-tent is proportionally increased with the increase of heating temperature and duration