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Learning a program is important for all students, not only students from the field of computer science but all fields. Programming languages are different from human communication languages as they have different structural forms. This makes it difficult for beginners especially for non-computer science students to understand the structure of programming languages. Therefore, to learn and understand the programming language more effectively, this article focuses on the important structure in learning a program from the initial stage to the advanced level suitable for non-computer science students. The objective of this article is to suggest important elements that can be assessed on these students which are to measure their understanding as they learn programming languages. The questions proposed to measure students' understanding were based on Bloom's Taxonomy, which covers six levels of understanding. It is hoped that this assessment proposal can act as a guideline for educators in fully focusing on important matters during the teaching and learning process.

Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).

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