Diversity and anxiety: A case study on collaborative testing - Pang, Yulei; Mugno, Raymond; Xue, Xiaozhen (Frontiers in Education Conference, 2015)
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.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Collaborative testing; Evaluation strategies; Group diversity; Positive correlations; Students; Testing; collaborative testing; dept.mat; group diversity; test anxiety

Debugging in parallel or sequential: an empirical study. - Pang, Yulei; Xue, Xiaozhen; Namin, Akbar Siami (Journal of Software, 2015)
3 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/084-351-006-485-38X tex.type: [object Object]
Keywords: dept.mat

Predicting vulnerable software components through n-gram analysis and statistical feature selection - Pang, Yulei; Xue, Xiaozhen; Namin, Akbar Siami (International Conference on Machine Learning and Applications, 2015)
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.
36 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Android applications; Application programs; Artificial intelligence; Computer software; Continuous sequences; Feature extraction; Feature selection; Feature selection algorithm; Forecasting; Java programming language; Learning systems; N-gram; N-grams; Prediction techniques; Research questions; Statistical features; Vulnerability prediction; Wilcoxon test; dept.mat

Constructing collaborative learning groups with maximum diversity requirements - Pang, Yulei; Mugno, Raymond; Xue, Xiaozhen; Wang, Huaying (International Conference on Advanced Learning Technologies, 2015)
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.
3 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Collaborative learning; Education; Engineering education; Group diversity; Grouping model; Learning outcome; Multi-start algorithm; Multistart; Random grouping; Strategic oscillations; Students; Tertiary institutions; dept.mat

A predator–prey interaction from micro to macro perspectives - Wang, Huaying; Pang, Yulei; Zhang, Luona; Martin, Clyde F (Nonlinear Dynamics, 2015)
This paper deals with predator–prey dynamics in individual and population perspectives. First, we build a discrete Markov model on predator–prey interactions in individual perspective. By shortening the time gap, from discrete time to continuous time, and increasing the number of individuals to infinity, a continuity equation on the predator–prey interactions is derived in a large population regime. Then, with the leading items of the continuity equation, that is the mean-field equation, following the approximate model, which entails qualitative analysis, we can obtain an asymptotically stable closed orbit or simply put, the parameter conditions where equilibrium point exists. These qualitative conclusions are the performance of individual microscopic interactions on macro-level groups, or can be treated as one component of microscopic models of various random statistical average results.This paper explored the accuracy and operability of the model constructed on individual level, which differs from traditional method, constructing population model directly via differential equations and difference equations. Therefore, by operating variables and data from individual behavior, it is probable for us to construct more accurate models for population dynamic. © 2014, Springer Science+Business Media Dordrecht (outside the USA).
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/041-431-988-565-054 tex.type: [object Object]
Keywords: Asymptotically stable; Continuity equation; Continuity equations; Continuous time systems; Difference equations; Differential equations; Discrete Markov model; Individual interaction; Macro-level groups; Markov processes; Microscopic interaction; Parameter conditions; Population statistics; Predator prey systems; Qualitative analysis; Stable limit cycle; dept.mat