Figure 1 - uploaded by Mohd Shahid Husain
Content may be subject to copyright.
The social media analytics framework to increase student interests' in STEM and TVET
Source publication
Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective studen...
Context in source publication
Context 1
... target of the framework audiences is prospective students in Malaysia who might enroll in STEM and TVET courses in the higher education institutions in Malaysia. The architecture of the social media framework is depicted in Figure 1. There are four primary components of the framework namely social media, role model/mentoring, MOOC and big data analytics. ...
Citations
... Gamification may also be implemented to improve students' mood as the one done by paper [15]. While a full-fledged smart system may also be developed and utilized to monitor and manage the behaviour of students as experimented by researchers in paper [16] along with analyzing their social media activities -in order to detect their growing interest in STEM subjects [17]. From additional perspective, stress levels of STEM-enrolled students may also be affected by the psychological condition and/or stress level of the corresponding lecturer [18], hence this factor may be taken into account in the future experiments. ...
The high availability of technology-related jobs is currently not fulfilled by enough supply of technology graduates, specifically the ones coming from science, technology, engineering, and mathematics (STEM) courses. This is related to the low enrolment rate of STEM-based courses at Universities. The notion that STEM subjects are intense and difficult to pass could be the reason of such phenomena. This research experimented different lecture delivery methods comprising of different seating arrangements, inclusion or exclusion of break, group size, and room size to investigate the generated levels of stress among STEM students, so that the method that would induce minimum stress could be discovered. The analysis of the generated heart rates as stress indication concludes that the level of difficulty of STEM subject's topic is the one that most affected students' level of stress, while inclusion of break during class is imperious to destress students. Class arrangement with far seating was discovered to be potentially more stressful for certain students indicated by the high level of resting heart rate in one of the experiments. Importantly, it was also found that regardless of lecture delivery method, STEM subjects in general remained challenging that was suggested by the consistently high recorded heart rates, which was also a sign of active learning engagement-besides implying the level of stress. Finally, gender-wise, there is no visible data pattern that could hint that certain gender is more vulnerable to stress when handling STEM subjects.
... This term was often used in conjunction with "media" to highlight the role of social media in online learning and collaboration, such as in [10]. It was also used to indicate the use of social media as a source of data collection, such as in [24]. ...
... • Topic 1 (cluster 0): The analysis of this topic reveals a high frequency of words such as "higher", "lifelong", "adult", "social", "international", "country", "unesco", and "literacy". In this topic, several themes were identified: i) The first theme concerns the utilization of various ICT tools, such as Simulation and Visualization tools [29], Mobile Learning [30], and Social Networks [24], and their potential impact on TVET pedagogy. ii) The second theme addresses the potential role of TVET in attaining the Sustainable Development Goals (SDGs), e.g. ...
he labor market is constantly affected by global events and techno-
logical advancements, leading to a need for the workforce to continuously ac-
quire new skills. Technical and Vocational Education and Training (TVET)
can provide such opportunities, with Information and Communication Tech-
nology (ICT) playing a crucial role in facilitating the process. However, the
full potential of ICT in TVET has not been fully realized, and there is a
gap in the knowledge regarding its role. To contribute to the body of knowl-
edge, this study has conducted a systematic literature review using Natural
Language Processing (NLP) tools, including Word Cloud and Topic model-
ing. The findings have shown common themes addressing the role of ICT
in creating and distributing educational material, creating collaborative and
interactive learning environments, and providing personalized learning ex-
periences. This study has revealed challenges related to ICT infrastructure,
availability of ICT skills, and technical challenges including cyber-security
and technical support. Furthermore, the study has identified a knowledge
gap in understanding the role of ICT in the learning value-creation process.
As a practical implication, this study highlights the need for future research
in the field, to address the knowledge gap related to the role of Artificial
Intelligence (AI) technologies in facilitating the value creation process in
TVET. The findings have social implications for the development of TVET
programs and the development of sustainable workforce, thus contributing
to the achievement of the United Nations’ Sustainable Development Goals.
Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSViewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data.
