C++ programming language is widely used in the industry and has become a compulsory course to learn in most universities in Malaysia. C++ programming is a powerful language that supports many ways of a program, such as procedural, object-oriented, and functional. However, this language seems difficult to learn, especially for students not in Computer Science background. The students face a problem in understanding the concept and do not enjoy the course. Thus, the students become demotivated. Games are fun activities that can enhance the process of thinking, learning, and problem-solving strategies. With these criteria, educational games are one of the best learning methods to improve the existing learning process. This study purposely develops and evaluates a computer game for C++ programming introductory based on Snake and Ladder board game. This game named SLC++ aimed to attract the students to enjoy their study and understand the concept of a programming language. The development is based on iterative methodology, consisting of planning, design, prototype, playtest, evaluate, and deployment phases. From 120 students, 88.64% strongly perceive SLC++, with 90.83% who are motivated, 90.56% attract with the c++ language, and 87.33% understand the introductory for c++ language after completing the game.
The student's performance has become the focus in higher education institutions. The ability to predict students' performance is beneficial to improve their achievement and the learning process. However, producing a prediction model for academic performance becomes challenging when an educational dataset contains various data. Many researchers have widely explored this kind of research, but many features should be investigated to affect students' achievement. Finding the potential factors influencing students' performance helps enhance students' quality. These factors will assist an institution plan a strategy for improving students' performance. This research proposes a classifier model to predict students' academic performance and define the factors influencing the performance by considering 14 attributes from demographics, learning styles, and educational background. The model development employs seven machine learning algorithms, and the best model will be selected. The factors that influence academic performance will be revealed from that model. The dataset was collected by conducting a survey at UiTM Seremban involving 233 students from Science and Technology and Social Science Streams. The Random Forest Tree produced an accurate result with the simple rules to be interpreted. The model also showed four attributes: qualification before tertiary education, SPM result, Seniority and gender positively impacting academic performance. Some factors that did not influence their performance were their parents' academic background and hometown.
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