Review Article
Yessane Shrrie Nagendhra Rao, Chwen Jen Chen
CONT ED TECHNOLOGY, Volume 16, Issue 2, Article No: ep502
ABSTRACT
This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.
Keywords: educational data mining, big data, education, bibliometric analysis, Scopus
Research Article
Sang Eun Lee, Naya Choi, Jieun Kiaer
CONT ED TECHNOLOGY, Volume 15, Issue 3, Article No: ep424
ABSTRACT
The study explored the social perceptions of young children’s use of smart devices in South Korea using big data methodologies. Big data methodologies allowed to uncover underlying thoughts and feelings about young children’s use of smart devices that had not been discovered in existing studies. The study extracted raw data from three different groups: the public, the journalist, and academia. Then, the study conducted keyword frequency, sentiment analysis, and CONCOR analysis with UCINET 6.0. The results of the study revealed that each group viewed young children’s use of smart devices in a different way. The public was interested in effective use of smart devices while the journalist focused on educational aspects. The academia focused on parents’ perception of smart devices from a developmental perspective. Regarding the results of sentiment analysis, they showed that each group had different opinion on young children’s use of smart devices. The public had an ambivalent attitude toward young children’s use of smart devices. While the journalist showed a positively inclined attitude, the academic had a negatively inclined attitude.
Keywords: big data methodologies, smart devices, social perceptions, young children