Collections > UNC Chapel Hill Undergraduate Honors Theses Collection > Mixed-focus Difficulty-Triggered Collaborative Writing: Interoperable Architecture, Implementation, and Evaluation
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Many students face difficulty when writing documents due to various reasons such as language barriers, content misunderstanding, or lack of formal writing education. They are often too shy or busy to visit a writing center or speak with a professor during office hours. Technology also falls short in this arena. Asynchronous collaboration systems require students to self-report when they are struggling and many students tend to under-report difficulty. Synchronous collaboration systems eliminate the need for self-reporting, but require teachers to constantly monitor their students. By combining synchronous and asynchronous collaboration paradigms, this project is able to create a mixed-focus collaborative writing system in which students and teachers engage in collaboration only when triggered by an automatically or manually generated event that indicates the student is facing difficulty. This mixed-focus system was created by combining two existing architectures: 1) the EclipseHelper difficulty architecture for inferring programming difficulty, and 2) the Google Docs collaborative writing environment. The new, combined architecture allows teachers to intervene and offer remote assistance to their students when they are automatically notified that a student is facing difficulty. A user study was conducted to evaluate this new architecture. Students used the system to complete a two-page paper given in a class they were taking, and data were recorded during the writing and help-giving process. Using both qualitative and quantitative analysis, the data were evaluated. Overall, students found the help-giving model easy to use and appreciated the feedback they received. However, difficulty was predicted infrequently, likely as a result of inherent differences between writing and programming. Future work will involve further analysis of the data in order to improve the difficulty prediction algorithm.