Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Mazidi, Abdallah Al; Abusham, Eimad

doi:10.1177/1847979018807020

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…93 The information obtained can be used to build predictive models to analyze which videos better stimulate user-generated content. 94 Finally, future research could also emphasize consumer attributes such as trust. 95…”

Section: Discussionmentioning

confidence: 99%

The influence of firm-Generated video on user-Generated video: Evidence from China

2022

International Journal of Engineering Business Management

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We examined the impact of firm-generated content on firm-related user-generated content. Researches have proven that firm-related user-generated content impacts firm revenue. Therefore, it is necessary to determine what factors stimulate the creation of firm-related user-generated content. Creating user-generated videos related to companies (e.g. E-sports) on Chinese online video platforms often includes the use of firm-generated videos as material. It may suggest that firm-generated content may play an essential role in influencing Internet users to create firm-related user-generated content. We collected a unique dataset using a Python web crawler and used the LSDV model for empirical analysis, including 2977 firm-generated videos and 49860 user-generated videos, to explore the impact of firm-generated video attributes on user-generated videos. The results show that some attributes of firm-generated video have a significant impact on user-generated video. The number of comments and coins on firm-generated videos positively affects user-generated videos, while the number of favorite on firm-generated videos negatively affects user-generated videos. We also found that the difference in how users feel about firm-generated videos affects the likelihood of users creating their original videos. User engagement and user brand identity in FGV positively impact stimulating user-generated videos. The authors suggest that companies can maximize the impact on user-generated content by targeting the creation of firm-generated content based on these video attributes. The authors also suggest that firms further investigate theories related to motivational factors, such as the impact of consumer engagement, brand identity, and perceived usefulness on user-generated content.

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Section: Discussionmentioning

confidence: 99%

The influence of firm-Generated video on user-Generated video: Evidence from China

2022

International Journal of Engineering Business Management

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“…In contemporary educational research, several studies unlocked the power of data analytics and mining approaches, for example, cluster analysis and decision tree, to discover the hidden values of the educational data during teaching and learning activities (Al Mazidi and Abusham, 2018;Križanić, 2020). Further, the growth of social media and e-learning platforms creates more opportunities in the data collection related to teaching and learning activities.…”

Section: Soft Systems Methodology and Its Applicationsmentioning

confidence: 99%

Immersive Learning Design for Technology Education: A Soft Systems Methodology

Tang

Tsang

et al. 2021

Front. Psychol.

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Science, technology, engineering and mathematics (STEM) education is a globalized trend of equipping students to facilitate technological and scientific developments. Among STEM education, technology education (TE) plays a significant role in teaching applied knowledge and skills to create and add value to systems and products. In higher education, the learning effectiveness of the TE assisted by the immersive technologies is an active research area to enhance the teaching quality and learning performance. In this study, a taught subject of radio frequency identification (RFID) assisted by using mixed reality technologies in a higher education institution was examined, while the soft systems methodology (SSM) was incorporated to evaluate the changes in learning performance. Under the framework of SSM, stakeholders’ perceptions toward immersive learning and RFID education are structured. Thus, a rich picture for teaching activities is established for subject control, monitoring, and evaluation. Subsequently, the design of TE does not only satisfy the students’ needs but also requirements from teachers, industries, and market trends. Finally, it is found that SSM is an effective approach in designing courses regarding hands-on technologies, and the use of immersive technologies improves the learning performance for acquiring fundamental knowledge and application know-how.

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“…Reference [1] captured the test data of 6000 students and used a predictive data prediction method to study diploma student performance in school. The decision tree as a means of separation has been used and a predictive model has been developed.…”

Section: Related Workmentioning

confidence: 99%

Model for Prediction of Student Grades using Data Mining Algorithms

Shibli

Abri

Sunny³

et al. 2022

COMPUTE

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There has been a rapid growth in the educational domain since education has become an important need. Data is collected in this domain which can be put to meaningful use to derive a lot of benefits to the students. Predicting student performance can help students and their teachers keep track of student progress. Mining Educational data helps to uncover invisible patterns, relationships, or trends in the unstructured data and helps in delivering logical and meaningful recommendations. Several kinds of research are being conducted across the world to analyze the data regarding student learning to identify the factors affecting performance and to provide support to students to help them improve. It is the objective of the proposed research to conduct a detailed study in the Sultanate of Oman regarding the existing toolsets, systems, and mode of data collection that are used currently in the Education sector for the prediction of Student Grades. Taking this as the baseline, later a model that will feature different prediction algorithms which are more accurate in predicting the grades of a student will be developed. The objective of this research is to understand the various predictive methods used to predict student performance and to propose a machine learning model to predict student grades.

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Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Cited by 10 publications

References 11 publications

The influence of firm-Generated video on user-Generated video: Evidence from China

The influence of firm-Generated video on user-Generated video: Evidence from China

Immersive Learning Design for Technology Education: A Soft Systems Methodology

Model for Prediction of Student Grades using Data Mining Algorithms

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