IADE's automated feedback characteristics and Help Options were appropriate for targeted learners, which speaks of adequate Learner Fit. Learners' focus on the functional meaning of discourse and construction of such meaning served as evidence of strong Meaning Focus. The strength of Language Learning Potential was supported by evidence of noticing of and focus on discourse form, improved rhetorical quality of writing, increased learning gains, and relative helpfulness of practice and modified interaction. Its effectiveness was a result of combined strengths of its Language Learning Potential, Meaning Focus, Learner Fit, and Impact qualities, which were all enhanced by the program's automated feedback.
The findings indicate that IADE can be considered an effective formative assessment tool suitable for implementation in the targeted instructional context. Qualitative data contained students' first and last drafts as well as transcripts of think-aloud protocols and Camtasia computer screen recordings, observations, and semi-structured interviews. Quantitative data consisted of Likert-scale, yes/no, and open-ended survey responses automated and human scores for first and last drafts pre-/post test scores and frequency counts for draft submission and for access to IADE's Help Options. To achieve this goal, the study sought evidence of IADE's Language Learning Potential, Meaning Focus, Learner Fit, and Impact qualities outlined in Chapelle's (2001) CALL evaluation conceptual framework.Ī mixed-methods approach with a concurrent transformative strategy was employed. The major purpose of the dissertation was to implement IADE as a formative assessment tool complementing L2 graduate-level academic writing instruction and to investigate the effectiveness and appropriateness of its automated evaluation and feedback. It introduces IADE (Intelligent Academic Discourse Evaluator), a new web-based AWE program that analyzes research article Introduction sections and generates immediate, individualized, discipline-specific feedback. This dissertation presents an innovative approach to the development and empirical evaluation of Automated Writing Evaluation (AWE) technology used for teaching and learning.
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