Kelesuan akademik adalah keadaan psikologi yang buruk dan menyebabkan tekanan yang kronik di kalangan pelajar. Kajian ini dilakukan untuk mengenal pasti dimensi kelesuan akademik yang dominan (Keletihan emosi, Rasa pencapaian peribadi berkurang, dan Depersonalisasi) dalam mempengaruhi pencapaian skolastik pelajar di Universiti Teknologi MARA (UiTM), Kampus Kota Bharu. Sebanyak 282 pelajar yang mendaftar pada tahun kedua dan ketiga dipilih sebagai sampel kajian secara pensampelan berstrata. Hasil kajian menunjukkan bahawa lebih daripada separuh pelajar didapati mengalami tekanan akademik, yang membawa kepada tahap gejala kelesuan akademik yang tinggi. Terdapat juga perbezaan yang signifikan dalam tahap kelesuan akademik yang dihadapi antara pelajar lelaki dan wanita. Hasil kajian juga menunjukkan pemboleh ubah keletihan emosi yang memberikan sumbangan yang signifikan terhadap pencapaian skolastik pelajar. Kajian ini memberikan gambaran kepada pihak pengurusan dan pembuat dasar untuk menangani masalah gejala kelesuan akademik di kalangan pelajar dalam pendidikan tinggi dengan berkesan.

Air pollution is a well-known issue for all countries, including Malaysia. It has been stated that particulate matter that less than 2.5mm known as PM2.5 has a greater effect on health as the smaller particulate size can penetrate deep into the respiratory system and affect the cardiovascular system significantly. Therefore, it is necessary to estimate the concentration of PM2.5 for haze precautions. This study characterizes the pattern of PM2.5 concentrations involving seven stations including Alor Setar, Shah Alam, Pasir Gudang, Ipoh, Kuantan, Kuala Terengganu and Miri with seven indicator parameters (Carbon Monoxide, Ozone, Sulphur Dioxide, Nitrogen Dioxide, Humidity, Temperature and Wind Speed). PM2.5 concentrations were predicted for each station using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). Descriptive and trend analysis using Mann-Kandell Trend analysis was used to describe the haze characteristics and identify significant trends in the haze selected locations in Malaysia. MLR and ANN were fitted for the data. The performance of both prediction models was compared based on R2 and Mean Square Error (MSE). The results show ANN performed better than MLR with a high value of coefficient determination (R2) and low error measure. The ANN model was used to predict the occurrence of haze for the next day in the Air Quality Index (API).

Nowadays, jobs requiring a master's degree are rising in today's competitive labor market. In Malaysia, the number of master's holders are still relatively small. Thus, there is a vital need to look into ways to boost up the number of admission and production of master's graduates. This study is aimed to identify the main factors (job, self-motivation, financial aid, family) that significantly influence students` intention to pursue a master's degree. The result showed that three variables; job, self-motivation, and family have a significant impact on students` intention to pursue a master's degree. Findings of this study will be beneficial in terms of decision making and will contribute to the roles that assist the Ministry of Higher education (MOHE) marketers to plan and improve their marketing strategy for recruiting students.

In an era of Education 5.0 where technology is advancing, Science Technology Engineering Mathematics education (STEM) is one of the important aspects. Teachers play an important role to support students in developing better awareness towards the importance of STEM education. Unfortunately, recent statistics show that there is a lack of students’ participation in choosing STEM education. Therefore, the aim of the study is to determine the teacher’s perception on factors which might influence students’ lack of interest towards Science Technology Engineering Mathematics (STEM) in secondary schools located in Kota Bharu. There are several factors that affect students’ interest in STEM education which are attitude, management policy, learning method and gender of students. A cross-sectional study was carried out among 290 secondary school teachers in Kota Bharu, Kelantan. A combination of stratified sampling and cluster sampling technique was applied to collect data. Pearson Correlation and Multiple Linear Regression revealed that management policy and learning methods were significantly associated with teachers’ perception. Findings from this study indicated that it may be effective to increase the students’ interest towards STEM education by improving the management policies and learning method.

COVID-19, CoronaVirus Disease – 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg’s test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases.