The Relation Between Social Media and Students’ Academic Performance in Jordan: YouTube Perspective

Habes, Mohammed; Salloum, Said A.; Alghizzawi, Mahmoud; Mhamdi, Chaker

doi:10.1007/978-3-030-31129-2_35

Cited by 81 publications

(75 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They use facebook more often than they use twitter and whatsapp. This is in agreement with Habes et al (2018) and Salvation and Adzharuddin (2014) which showed that the most used social media is facebook. Results also indicate that social media has slight positive influence on the students' academic performances since 60.8% of them often engage in educational activities and most of them attested to the fact that their involvement in social networking had improved their knowledge of chemistry, grammatical expression and aided understanding of difficult chemical concept learnt.…”

Section: Discussionsupporting

confidence: 91%

“…Current university students grew up in the technology era and social media is now just part of students' daily routine. Habes et al (2018) asserted that the most used social media is facebook, and that despite spending time on social media, students are still efficient enough in their studies. This means that social media have both positive and negative impacts on the students' academic performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of social media on undergraduate students achievement and interest in chemistry in the North-central geo-political zone of Nigeria

Fatokun¹

2019

Int. J. Sci. Technol. Educ. Res.

View full text Add to dashboard Cite

This paper gives a report of the effect of social media on undergraduate students' achievement and interest in chemistry in the north-central geo-political zone of Nigeria. The research design adopted was a cross-sectional survey. The population of this study comprised all undergraduate chemistry students from the universities in the north-central geopolitical zone of Nigeria. 240 undergraduate chemistry students constituted the sample for the study and 80 students each were randomly selected from all the four levels of the three sampled Universities namely; Nasarawa State University Keffi, University of Abuja, and Federal University of Agriculture, Makurdi. Structured questionnaire tagged: Effect of social media on achievement and interest in chemistry (ESMAIC) was the only instrument used for data collection. The questionnaire used was validated by experts and pilot tested to establish a reliability coefficient of 0.88. The finding of this study reveals that 60.8% of the respondents engage on social media mainly for educational related purposes while 52.9% of them agreed that social media has helped in increasing their grade point average (GPA). The interest of 67.5% respondents was boosted when employing social media for learning, and Facebook is most frequently used among the network sites by students. Addiction, distraction, poor time management and health problems are some of the disadvantages of continuous engagement with social media. Results also show that many students did not agree that social media is not an effective tool for studying and some are still hesitant on adopting modern technology while some are not very proficient on its usage. It is therefore recommended that students should be educated on how to maximize the potential of the different social network sites for their academic benefits among others.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Effect of social media on undergraduate students achievement and interest in chemistry in the North-central geo-political zone of Nigeria

Fatokun¹

2019

Int. J. Sci. Technol. Educ. Res.

View full text Add to dashboard Cite

show abstract

“…As social media websites have attracted millions of users, these websites store a massive number of texts generated by users of these websites [14][15][16][17][18][19][20][21]. Researchers were interested in investigating these metadata for search purposes [17,18,[22][23][24][25]. In this section, a number of research papers that explored the analysis and classification of Twitter metadata were surveyed to investigate different text classification approaches [26] and the text classification results.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis in English Texts

Alshamsi¹,

Bayari²,

Salloum³

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

“…The classification of texts is a method of searching for data and exploring the data among large data and classifying them into groups for easy reference [1][2][3][4][5]. Internet pages, books [6], magazines, and social media [7][8][9][10][11][12][13][14][15][16][17][18][19][20], have become a rich source of information that needs to be categorized and organized for easy reference [21][22][23][24][25]. There has been a lot of research in this field, but most of it involves the classification of English and Spanish texts and texts in other languages.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

show abstract

The Relation Between Social Media and Students’ Academic Performance in Jordan: YouTube Perspective

Cited by 81 publications

References 34 publications

Effect of social media on undergraduate students achievement and interest in chemistry in the North-central geo-political zone of Nigeria

Effect of social media on undergraduate students achievement and interest in chemistry in the North-central geo-political zone of Nigeria

Sentiment Analysis in English Texts

A systematic review of text classification research based on deep learning models in Arabic language

Contact Info

Product

Resources

About