7. CONCLUSIONS
WBI creates learning opportunities for everyone if suitable considerations are made in the design process. Otherwise, they can impose needless barriers to equal participation in educational settings. The experimental results obtained in this study suggest that cognitive style plays an influential role in student learning patterns withinWBI. Field-independent and field-dependent learners have different preferences for locating information, especially for the selection of navigation tools and display options. Thus, there is a need to be aware of cognitive styles when planning to improve the usability and functionality of WBI programs.
The contribution of this study includes three aspects: theory, methodology, and applications. In terms of theory, this study deepens the understanding of the importance of cognitive styles in the development of WBI programs by providing empirical evidence. Cognitive styles, gender differences, and system experience are factors that are frequently considered in the literature of individual differences but it is inconclusive as to their relative importance. The findings of this study indicated that cognitive style is a major factor that influences student learning patterns. However, it was only one relatively small study. Further work needs to be undertaken with a larger sample to provide additional evidence.
With regard to methodology, this study analyzed the experimental data witha data mining approach, which used both clustering and classification techniques. These two techniques are complementary in that they integrate the analysis of macro and micro levels. The results from clustering present an overall picture of the students’ learning patterns, whereas those from classification provide the detailed rules for the automatic identification of students’ cognitive styles based on their learning patterns. However, this study only used two methods, that is, K-means (clustering) and decision trees (classification). Given any dataset, there are often no strict rules that impose the use of a specific method over another in its analysis. Therefore, it is necessary to conduct further work to analyze student learning patterns using other clustering or classification methods, for examples self-organizing maps and support vector machines. It would be interesting to see whether similar results can be found by using these methods.
As far as the application is concerned, this study recognized the importance of versatility in the development of WBI programs and developed a model to illustrate the needs of different cognitive styles. In addition, several design approaches were proposed to accommodate the preferences of both fieldindependent and field-dependent learners. In the future, the rationale of the model and the design approaches can be used to improve the development of existing WBI programs and other Web-based applications such as digital libraries, search engines, and electronic journals. Finally, it would be valuable to see whether such WBI programs and the Web-based applications can promote learners’ performance and increase their satisfaction.
7. CONCLUSIONSWBI creates learning opportunities for everyone if suitable considerations are made in the design process. Otherwise, they can impose needless barriers to equal participation in educational settings. The experimental results obtained in this study suggest that cognitive style plays an influential role in student learning patterns withinWBI. Field-independent and field-dependent learners have different preferences for locating information, especially for the selection of navigation tools and display options. Thus, there is a need to be aware of cognitive styles when planning to improve the usability and functionality of WBI programs. The contribution of this study includes three aspects: theory, methodology, and applications. In terms of theory, this study deepens the understanding of the importance of cognitive styles in the development of WBI programs by providing empirical evidence. Cognitive styles, gender differences, and system experience are factors that are frequently considered in the literature of individual differences but it is inconclusive as to their relative importance. The findings of this study indicated that cognitive style is a major factor that influences student learning patterns. However, it was only one relatively small study. Further work needs to be undertaken with a larger sample to provide additional evidence.With regard to methodology, this study analyzed the experimental data witha data mining approach, which used both clustering and classification techniques. These two techniques are complementary in that they integrate the analysis of macro and micro levels. The results from clustering present an overall picture of the students’ learning patterns, whereas those from classification provide the detailed rules for the automatic identification of students’ cognitive styles based on their learning patterns. However, this study only used two methods, that is, K-means (clustering) and decision trees (classification). Given any dataset, there are often no strict rules that impose the use of a specific method over another in its analysis. Therefore, it is necessary to conduct further work to analyze student learning patterns using other clustering or classification methods, for examples self-organizing maps and support vector machines. It would be interesting to see whether similar results can be found by using these methods.As far as the application is concerned, this study recognized the importance of versatility in the development of WBI programs and developed a model to illustrate the needs of different cognitive styles. In addition, several design approaches were proposed to accommodate the preferences of both fieldindependent and field-dependent learners. In the future, the rationale of the model and the design approaches can be used to improve the development of existing WBI programs and other Web-based applications such as digital libraries, search engines, and electronic journals. Finally, it would be valuable to see whether such WBI programs and the Web-based applications can promote learners’ performance and increase their satisfaction.
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