WAT-C (Writing Analytics Toolkit Classroom), an open-source web platform providing formative writing feedback¶

Element · All elements
Evidence · 1 claim (1 mixed) · 1 study (1 review), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

WAT-C is the classroom-facing implementation of the Writing Analytics Toolkit, an open-source platform providing formative feedback on student writing using natural language processing. The alpha version evaluated here "generates analytics in three categories for persuasive and source-based essays: basic analytics (e.g., word count, sentence length), a holistic overall score (1–6 scale), and advanced analytics" developed via NLP and principal component analysis. It presents 13 advanced metrics (e.g., Sophisticated Wording, Word Variety, Sentence Cohesion) on five-level continua with clickable pop-up explanations, revision suggestions, and example use cases, and includes teacher features for assigning tasks, selecting metrics, uploading rubrics, and tracking submissions.

Design Implications¶

Context¶

Requirements¶

• Requires instructor configuration of tasks, selected metrics, and uploaded rubrics or mentor texts

Constraints¶

• The alpha version was revised away from predictive scoring toward descriptive feedback after earlier instructors preferred to remain the primary evaluator of writing quality

Target Learners¶

• College students in entry-level English writing courses

Target Learning Goals¶

• Writing quality, revision, and metric-aligned writing behaviors such as lexical sophistication, sentence cohesion, and idea development

Claims¶

• AWE feedback has limited depth and questionable accuracy for evaluating complex writing aspects [~M] — a general caution on automated writing feedback that applies to WAT-C's advanced analytics

Related Elements¶

• Feedback — WAT-C gives formative feedback on student writing
• Five-domain TAM survey of WAT-C instructors — the instrument used to evaluate instructors' acceptance of this platform
• Balance human and automated feedback in writing instruction

Examples¶

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Key Sources¶

• Potter, A.; Serhan, Z.; Patne, N. A.; Öncel, P.; Ahmed, I.; Arner, T.; Islam, R.; Roscoe, R. D.; Allen, L. A.; Crossley, S. A.; McNamara, D. S. (2026). Human-AI Collaboration for Qualitative Analysis in Participatory Design: Refining the Writing Analytics Tool. Journal of Educational Data Mining, Volume 18, No 1. https://osf.io/8j9y6/?view_only=3a21ed900475418a8c258ea595cbe53b