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In order to reduce students' test anxiety, collaborative testing was suggested as an evaluation strategy. However, few studies have focused on testing group construction, especially when an important factor, i.e., group diversity is taken into consideration. In this paper we conducted a case study to assess the association between group diversity and test anxiety in collaborative testing. The results observed may indicate that: 1) around 20% of students suffered from test anxiety to some extent in either an individual test or a collaborative test; 2) collaborative testing could alleviate test anxiety, whereas the effect is not statistically significant; 3) there exists a moderate positive correlation between group diversity and test anxiety in collaborative testing. The results of the study may suggest limiting group diversity in collaborative testing in order to alleviate test anxiety. © 2015 IEEE.
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Vulnerabilities need to be detected and removed from software. Although previous studies demonstrated the usefulness of employing prediction techniques in deciding about vulnerabilities of software components, the accuracy and improvement of effectiveness of these prediction techniques is still a grand challenging research question. This paper proposes a hybrid technique based on combining N-gram analysis and feature selection algorithms for predicting vulnerable software components where features are defined as continuous sequences of token in source code files, i.e., Java class file. Machine learning-based feature selection algorithms are then employed to reduce the feature and search space. We evaluated the proposed technique based on some Java Android applications, and the results demonstrated that the proposed technique could predict vulnerable classes, i.e., software components, with high precision, accuracy and recall. © 2015 IEEE.
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Due to the considerable advantages of collaborative learning, group work is widely used in tertiary institutions. Previous studies demonstrated that group diversity had positive influence on group work achievement. Therefore, an interesting question that arises is how to achieve maximum group diversity effectively and automatically, especially when the features to be considered are numerous and the number of students is large. In this paper we apply a multi-start algorithm composed by a greedy constructive and strategic oscillation improvement to group students. We evaluated the technique based on a small-scale case study. The results observed indicate that the multi-start algorithm-based grouping model is feasible. It improved the overall and average students diversity within group significantly, and it also enhanced students' collaborative learning outcomes compared to random grouping model. However, we did not find any evidence on monotonic positive relationship between diversity and students' learning outcomes. © 2015 IEEE.
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