CHAPTER 4 CONCLUSION
4.5. Directions for Future Studies
For future studies, improvement of the unsupervised genre analysis approach is worth pursuing. The results from Section 3.3 revealed connections of language used in RA sections and abstract moves, which means the use of n-gram features is important. However, they may be insufficient; thus, more features should be investigated. The unsupervised approach shows promise, because when comparing the results of this dissertation to Pendar and Cotos’ (2008) study, the unsupervised approach was only 10% lower than the supervised approach. Therefore, to obtain better accuracy for the
automated analysis on moves in abstracts, more features for SVM classifiers should be investigated, such as verb categories, sentence subjects, and position/location of sentences in an abstract. By improving the SVM classifiers, the unsupervised approach could possibly perform better.
Once the accuracy of SVM classifiers is improved, the unsupervised genre analysis approach could be applied on a large scale to different discipline-specific genres of writing. Depending on the purposes, it may be possible to collect a corpus representing another genre of text, or to expand the range of sub-disciplines represented in the current corpus, to generate materials for the implementation of AWE tools by replicating the procedure utilized in this dissertation.
To summarize, writing English RA abstracts is an authentic task that Taiwanese engineering graduate students must complete to succeed academically and professionally, but they lack resources to learn this essential skill. The existing body of scholarship presented possible solutions to fill this gap. To manage this genre-specific task, this dissertation attempted to develop an AWE tool, and to evaluate its appropriateness and effectiveness. Guided by the DBR paradigm, the development and evaluation of the AWE tool went through six iterations and design principles were synthesized according to the results. It is hoped that these design principles could serve as guidance for
developing genre-specific AWE tools in the future. Finally, although the results of the unsupervised Stat-NLP approach for genre analysis were insufficient for immediate practical applications, it showed potential for automation of materials development for genre-based teaching and learning. Thus, this requires more exploration in future work. In doing so, more students’ academic needs related to discipline-specific tasks could be satisfied.
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