Game based learning has been used as a learning tool for centuries. This study proposes a development of game-based learning in Statistics subject. The game is a 2D prototype and the game is tested and adjusted to be more effective learning tool for learning. Statistics subject has been used for this study, known as a dreaded subject that causes many negative perceptions as students tend to think that learning Statistics is difficult. Premised on this concern, educators should create potential teaching and learning tool as a vector to challenges the perceived difficulty of learning statistics as well engage the learners and help educators access the learning. The aims of this study are to explore students' gain of using this teaching and learning tool in Statistics for classroom session and identify the factors affect their intention to use it. A total of 195 students were involved in this study using direct questionnaire and statistically observation through pre and post-test on their statistics test. It is reported that the tests of normality on the distribution for the pre marks with (KS=0.153, SW=0.960, p-value=0.00<0.05) and post marks with (KS=0.122, SW=0.969, p-value=0.00<0.005) both were significantly not normal. Thus, Wilcoxon signed ranks test shows that there is a difference in pre and post score test. This preliminary study shows that there is an improvement in score after students used this teaching and learning tool. Multiple linear regression analysis show that students' intentions to use is significantly influenced by perceived usefulness (t=2.781, p-value=0.000), perceived ease to use (t=5.949, p-value=0.000) and attitude towards innovation (t=8.545, p-value=0.000). In terms of importance, predictor attitude towards innovation made the largest contribution to the model (Standardized coefficient=0.457).
Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build Autoregressive Integrated Moving Average (ARIMA) model on the average of monthly ozone data taken from three monitoring stations in Klang Valley for the period 2000 to 2010 with a total of 132 readings. Result shows that ARIMA (1,0,0)(0,1,1) 12 model was successfully applied to predict the long term trend of ozone concentrations in Klang Valley. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Root Mean Squared Error, Mean Absolute Percentage Error and Normalized Bayesian Information Criteria. The finding of a statistically significant upward trend of future ozone concentrations is a concern for human health in Klang Valley since over the last decade, ozone appears as one of the main pollutant of concern in Malaysia.
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