Teoh Sian Hoon

Bio: Teoh Sian Hoon is an academic researcher. The author has contributed to research in topics: Rasch model & Psychology. The author has co-authored 1 publications.

Key Factors Influencing Graduation on Time Among Postgraduate Students: A PLS-SEM Approach

Abstract: Abstract: The information age witnessed the democratisation of education with an exponential growth in the enrolment of postgraduate students in almost all universities around the world. However, high attrition and low completion rates among students have been an immense threat to the key performance of the university system. The main purpose of this study was to investigate the influence of institutional support, supervisory practices, and students’ self-management and research skills on postgraduate students’ motivation to graduate on time (GOT). The data were collected from 191 postgraduate students from three universities in Malaysia using a survey questionnaire. The quantitative data were analysed using the PLS-SEM approach. The results revealed that research skills, institutional support and self-management skills significantly influenced the postgraduate students’ motivation to GOT. Furthermore, research skills were identified as the strongest predictor of the motivation to GOT. Additionally, research skills mediated the relationships between institutional support, students’ self-management skills and postgraduate students’ motivation to GOT. However, supervisory practices toward GOT were not supported either by direct or indirect effect. The study has far reaching implications for the postgraduate students, supervisors, and institutions of higher learning.

Predicting Students’ Mobility using Different Statistical Tools : Basis for Students’ Success

Abstract: This paper investigates students’ success at Pangasinan State University by identifying patterns and models that might be used to correctly classify and predict if a student will transfer or finish their studies. In this study, three categorical variables or attributes and one continuous variable were considered independent variables due to the availability of the data. The results from the binary logistic regression model with the high school general average and course as independent variables (Model 3), and the decision tree model with transition gain as a splitting criterion were fitted to the dataset to generate a model that possibly best describes the students’ mobility in Pangasinan State University Urdaneta City Campus. The decision tree model is better than the binary logistic regression model based on accuracy, AUC, and sensitivity values. This implies that the decision tree model is better at correctly classifying observations as "transferred" than Model 3. Thus, it was concluded that the decision tree model with information gain as the splitting criterion best describes the mobility of PSU students. The results of this paper can be used for school administration involving students’ mobility/success, particularly in classifying whether a student will transfer based on other.

Examining factors affecting knowledge sharing behaviour on social media of postgraduate students in malaysia

Abstract: Knowledge sharing is crucial for students’ learning process, yet can be an issue for some reasons, as students often refuse to share their knowledge with others. There are various personal, social and technological factors that can affect students’ knowledge sharing behaviour on social media. Hence, this study aims to investigate the factors affecting knowledge sharing behaviour among postgraduate students in Malaysia through the lens of Theory of Planned Behaviour Model and Social Cognitive Theory. This study adopted a quantitative research design and used a survey questionnaire via Google Form as its data collection technique. Simple random sampling was used, and data was collected from 406 postgraduate students in Malaysia, including from Sabah and Sarawak. Data were analysed using SPSS to construct the findings. The findings demonstrate that all independent variables (usefulness, ease of access, reputation enhancement, enjoyment in helping, sense of belonging, social interaction, trust and reciprocity) significantly affect postgraduate students’ knowledge sharing behaviour on social media. Sense of belonging and reciprocity have a strong positive relationship with knowledge sharing behaviour. Whereas, usefulness, ease of access, reputation enhancement, enjoyment in helping, trust and social interaction have a moderate positive relationship with knowledge sharing behaviour. The findings of this study may have various implications for higher education institutions (HEI) and corporate organisations, where knowledge sharing behaviour is essential for academic performance and organisational success. However, this study only used the questionnaire given to obtain data from the respondents without asking for further clarifications or any additional comments from them. Although it is convenient and easy to analyse, the questionnaire does not allow researchers to gain a deeper understanding of respondents’ motivations to share their knowledge on social media. Therefore, future studies may include qualitative research such as structured observation and interviews. A longitudinal study is also recommended for this future study.

Sustaining Students’ Quality Learning Environment by Reviewing Factors to Graduate-on-Time: A case study

Abstract: Research reveals that several factors affect the quality of learning environments. Therefore, this study investigated significant factors that affect postgraduate students’ learning environment with regard to graduate on time (GOT). The study was conducted in a private Malaysian university involving 50 PhD students. Data were collected via tests, a questionnaire, and focus-group interviews. The findings revealed that critical reading skills and supervisory factors were significant factors affecting students’ ability to GOT. This implies that universities should integrate early intervention training programs to hone students’ critical literacy skills and provide effective supervisory practices for a sustainable quality postgraduate learning environment. Keywords: postgraduate students; graduate-on-time; quality learning environment; influencing factors eISSN: 2398-4287 © 2023. The Authors. Published for AMER & cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia.. DOI: https://doi.org/10.21834/ebpj.v8i24.4649

Predicting timely graduation of postgraduate students using random forests ensemble method

Abstract: Graduation time of students, both undergraduate and postgraduate, has been a prime focus in universities recently. Over the years, there have been numerous research on using data mining techniques to forecast undergrad students' success. However, very few works have been reported on predicting graduation time of postgrads, particularly using data from Nigerian Universities. This research utilized classification techniques using supervised learning to develop a Postgraduate Student Graduation Time Prediction Model (PS_GTPM). Data was collected from Bayero University Kano and the Adaptive synthetic sampling (ADASYN) technique was applied to address the imbalance issue with the data. Then, the model was developed using the Random Forests ensemble technique. From the evaluation results, we found that the data balancing method based on ADASYN technique enhanced the ability of the data mining classifiers to forecast when students will graduate. Also, it was found that the proposed PS_GTPM based on Random Forests Ensemble Method recorded the highest prediction accuracy with more than 83% score compared to the other methods. Largely, PS_GTPM can be used to forecast whether a thesis-based graduate study shall be completed on-time or not.

Factors Associated with Attitudes toward Scientific Research Vietnam Nursing Students: A Cross-sectional Study

Abstract: Background: Nursing students do scientific research at the university level not only contributing to the development of the nursing profession but also helping themselves to practice more useful skills for future work. A positive attitude toward scientific research has been shown to motivate students to practice research. Objective: to assess attitudes toward scientific research and explore associated factors of nursing students in Vietnam. Methods: A cross-sectional study design was adopted and conducted on 238 nursing undergraduate students. Study samples were collected from October to November 2021 through a face-to-face meeting with study subjects and an accidental sampling technique. The attitudes Toward Research Scale was used in this study. Results: The study showed that 76.1% of nursing students had a positive attitude toward scientific research. Through simple linear regression analysis, it showed that having been or participating in scientific research projects (p=0.019), scientific research activities (p=0.028); intending to pursue a postgraduate degree (p=0.016); being introduced/invited to participate in scientific research projects (p=0.021) and having contact with people who can guide scientific research (p=0.033) was the factors that affect nursing students' attitudes toward scientific research. Conclusions: Many associated factors influence the attitude toward scientific research of nursing students. Further effective interventions are needed to address this issue