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The term "personality" can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers-Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

Mathematics is fundamentally important for Science and Technology, as well as in engineering. Mathematics is compulsory for students since all engineering subjects were Mathematically oriented. However, the preliminary study found that students’ achievement in Mathematics courses have been associated with three main factors, namely interest, attitude and learning habit, as in the KASH Model (Knowledge, Attitude, Skills and Habits). This Model stipulated that poor performance is not just lacking in knowledge and skills but also including poor attitude and habits. Therefore, this study aims to investigate the students’ level and relationship between interest, attitude and learning habit based on KASH Model. A total of 58 students were selected as a sample of the study, who enrolled in the Thermodynamics, Fluid Mechanics and Solid Mechanics subjects. A set of questionnaires with 21 items was used to collect data; a descriptively analysis was used to find the mean and percentage, as well as correlation index using Pearson. The results; high level of factor of interest, attitude and learning habit, and high correlation between interest, attitude and habit. The implication is that teaching and learning process must equally fostering all these variables to achieve a high level of students’ achievement, especially in Mathematics subjects.

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