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Gamification is the use of game elements in a non-gaming environments. The aims of implementing game elements in gamifying educational environment is to make the learners engaged and motivated in the learning process. But, some of the research result giving bad impacts in implementing gamification in education. The objective of this research is to study the impact of implementing game elements in education. The aim of this research is to study which game elements is most commonly used in gamifying educational environment. The study is done by reviewing previous research on impacts of implementing game element in education from 2008 until 2018 to see whether the elements giving positive or negative impacts on education. The results shows the most common game elements used in gamification is rewards, feedback, challenge, quest/mission/goal, level/stage, point/score, avatars/players, task, character, time-limit, narrative/dialogue, leaderboards, progress bars, and badges. Majority of the research on gamification in education reports positive impacts by introducing gamification in education. In conclusion, by implementing game elements in the gamifying educational environment is a good method to create a good adjustment in learners’ behavior and assertiveness in the learning process, it can develop engagement and motivation of the learners’.

Analysing data can be quite a challenge sometimes due to the nature of the data and the vast options of methods and techniques that can be used on the data. In this study, for example, a six years Cleft Lip and Palate dataset were gathered on these patients’ conditions in the quest to identify the contributing factors for a successful pre-graft orthodontic treatment. The challenges faced was in the small number of datasets and imbalance sample class. Therefore, this study had taken a step back and tried to approach the dataset with a combination of unsupervised and supervised learning methods to tackle the challenges by incorporating clustering - for testing records creation and; resampling - for balancing sample class. We also observed if the auto-created testing records are replaceable with the manually selected testing records by looking at the performances of the classification models. Based on the feature that was selected, k-Means and PAM were implemented as the clustering algorithm using the Euclidean formula as the distance measure. Resampling was done using SMOTE and Random Forest as the classification model. When the comparison was done on the models, the ones that were fed by resampled training records showed an increase in the AUC values and decrease in the OOB error. Comparable results were also achieved between the training records produced by PAM and by manual selection as both models, based on the AUC values, was classified as excellent classification models.

This paper will discuss the solution of twodimensional partial differential equations (PDEs) using some parallel numerical methods namely Gauss Seidel and Red Black Gauss Seidel. The selected two-dimensional PDE to solve in this paper are of parabolic and elliptic type. Parallel Virtual Machine (PVM) is used in support of the communication among all microprocessors of Parallel Computing System. PVM is well known as a software system that enables a collection of heterogeneous computers to be used as coherent and flexible concurrent computational resource. The numerical results will be presented graphically and parallel performance measurement by Gauss Seidel and Red Gauss Seidel methods will be evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance. Performance evaluations are critical as this paper aimed to fabricate an efficient Two-Dimensional PDE Solver (TDPDES). This new well-organized TDPDES technique will enhance the research and analysis procedure of many engineering and mathematic fields.

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