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Python language has become the most popular computer language. Python is widely adopted in computer courses. However, Python language’s effects on the college and university students’ learning performance, motivations, computer programming self-efficacy, and maladaptive cognition have still not been widely examined. The main objective of this study is to explore the effects of learning Python on students’ programming learning. The junior students of two classes in a college are the research participants. One class was taught Java language and the other class was taught Python language. The learning performance, motivations, and maladaptive cognition in the two classes were compared to evaluate the differences. The results showed that the motivations, computer programming self-efficacy, and maladaptive cognition on the learning performance were significant in the Python class. The results and findings of this study can be used in Python course arrangement and development.

One of the difficulties faced when using XML as the data storage structure is query inefficiency. Therefore, various indexing methods have been proposed. When designing indexing methods, the first step is to choose the labeling method. Some labeling methods can work well; however, if they cannot effectively support update operations, their use is subject to considerable limitations. Most of the update-friendly labeling methods proposed in the literature assign a unique label to each node in XML and provide an expandable mechanism for future insertion. However, they encounter some difficulties, such as increasing the index space, more difficulty in evaluating the relationships between nodes, and increasing the complexity of labels. In this paper, we introduce a novel update-friendly labeling scheme called branch map, which records the correspondence between parent and child nodes instead of assigning a label to each node. The space required for the index is reduced considerably. More importantly, the branch map can maintain the profile as if it was encoded initially, even after being frequently updated. This paper also proposes a compact indexing scheme called UCIS-X. Experimental results indicate that UCIS-X performs well in terms of index size, query, and update efficiency.