Big Data Adoption and Knowledge Management Sharing: An Empirical Investigation on Their Adoption and Sustainability as a Purpose of Education

Al-Rahmi, Waleed Mugahed; Yahaya, Noraffandy; Aldraiweesh, Ahmed; Alturki, Uthman; Alamri, Mahdi M.; Saud, Muhammad; Kamin, Yusri; Aljeraiwi, Abdulmajeed A.; Alhamed, Omar Abdulrahman

doi:10.1109/access.2019.2906668

Cited by 81 publications

(72 citation statements)

References 81 publications

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“…Through these characteristics, public sharing of information, engagement, and collaborative learning became famous features of these social networking sites [5,12,25,41,50]. This research supports the SMU to improve the students skills via their peer interactions [51,52]. Their satisfaction in SMU boosts their technology use, and in turn, this use enhances their skills and improves their achievement through collaboration and interaction [3,5,7,53,54].…”

Section: B Discussion and Implicationsmentioning

confidence: 74%

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Alyoussef¹,

Alamri²,

Al-Rahmi³

2019

IJRTE

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Abstract: This research aims to explore the factors of teaching and learning measurement through using social media, incorporating the literature of social media adoption on resource sharing, collaborating and communicating for educational purpose. The current research explore factors that perceived usefulness (PU), perceived ease of use (PEU), attitude toward use social media use have certain influence on adoption of resource sharing, collaboration and communication for educational use. Therefore, resource sharing, collaboration and communication influence educational use, while educational use influences PU, PEU, SMU and attitude toward the use (AT) of SMU for teaching and learning. Both the processes of collecting and analyzing the data followed the quantitative method. The main tool of data collection was a questionnaire that has been distributed among 236 students using stratified random sampling technique. The view of the students and their implication of social media use for teaching and learning were solicited through the questionnaire. The Statistical Package for the Social Sciences (SPSS) was used as the main tool in the process of data examination. The results of this research were related to two main constructs: teaching and learning as well as educational use.According to the results, it appears that perceived usefulness, perceived ease of use, attitude toward use, and social media are considered powerful determinants of the former while resource sharing, collaboration and communication were significant indicators of the latter. Educational use, PU, PEU, AT succeeded in explaining 74.9% of SMU for teaching and learning.

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Section: B Discussion and Implicationsmentioning

confidence: 74%

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Alyoussef¹,

Alamri²,

Al-Rahmi³

2019

IJRTE

Self Cite

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“…Therefore, students have to track and analyze the collaboration patterns that occur during ACL. ACL and motivating cognitive skills, reflection and metacognition, are fundamental to SMAs for learning [8]. The current research looks at the issues of education sustainability by using SMAs, thus, some studies have demonstrated that a higher level of learning was achieved as a result of using SMAs for student assignments [9].…”

Section: Introductionmentioning

confidence: 99%

Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education

2020

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Nowadays, social media applications (SMAs) which are quite popular among students have a significant influence on education sustainability. However, there is a lack of research that explores elements of the constructivist learning approach with the technology acceptance model (TAM) in higher education. Therefore, this research aimed to minimize the literature gap by examining the SMA factors used for active collaborative learning (ACL) and engagement (EN) to affect the students’ academic performance in measuring education sustainability, as well as examining their satisfaction from its use. This study employed constructivism theory and TAM as the investigation model, and applied a quantitative method and analysis through surveying 192 university students at King Faisal University. Using structural equation modeling (SEM), the responses were sorted into nine factors and analyzed to explain students’ academic performance in measuring education sustainability, as well as their satisfaction. The results were analyzed with structural equation modelling; it was shown that all the hypotheses were supported and positively related to sustainability for education, confirming significant relationships between the use of SMAs and the rest of the variables considered in our model (interactivity with peers (IN-P), interactivity with lecturers (IN-L), ACL, EN, perceived ease of use (PEOU), perceived usefulness (PU), SMA use, student satisfaction (SS), and students’ academic performance (SAP).

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“…Therefore, the proposed approach is a data analysis technique that should be useful to those who seek to use UGC to improve their communication or marketing strategies. Also, it should be useful to educational institutions that may be able to enhance their offerings by identifying needs and trends based on technologies previously investigated [52][53][54][55]. Figure 1 shows the training process of the SVM algorithm and the classification according to the feelings that (a) represents the training process of a sentiment analysis algorithm with a feature extractor and a machine learning algorithm, and (b) represents the prediction process of a sentiment analysis algorithm with a feature extractor and a classifier model [2].…”

Section: Knowledge-based Methods To Extract Insights From Ugcmentioning

confidence: 99%

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

2019

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New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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Big Data Adoption and Knowledge Management Sharing: An Empirical Investigation on Their Adoption and Sustainability as a Purpose of Education

Cited by 81 publications

References 81 publications

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

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