Topic Development to Support Revision Feedback

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI:10.1145/3303772.3303816

Jovita M. Vytasek, Alexandra Patzak, P. Winne

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引用次数: 3

Abstract

Revision is important but challenging for novice writers, particularly in post-secondary education where opportunities for personalized feedback are limited. Inexperienced writers typically overlook revision; when they do revise, they focus on surface errors rather than global revisions that enhance meaning and coherence. Writing analytics can automate personalized prompts to guide revision. We use topic modelling LDA as grounds for an analytic to scaffold holistic revision at paragraph and essay levels. The analytic visualizes topic distribution and generates three types of prompts: Introduction, Paragraph and Conclusion. Feedback encourages revisions focusing on sequencing topics, expanding underdeveloped ideas, and making holistic revisions to improve clarity and coherence of paragraphs. Model feedback was evaluated using undergraduate student essays on various topics scored by human evaluators. Model accuracy was strong for all types of feedback. This opens new branches of research to explore generating personalized feedback at paragraph and essay levels.

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主题开发以支持修订反馈

修改很重要，但对新手作家来说很有挑战性，特别是在高等教育中，个性化反馈的机会有限。缺乏经验的作者通常会忽略修改;当他们修改时，他们关注的是表面的错误，而不是增强意义和连贯性的整体修改。编写分析可以自动化个性化提示来指导修订。我们使用主题建模LDA作为分析的基础，以支撑段落和文章级别的整体修订。该分析将主题分布可视化，并生成三种类型的提示:引言、段落和结论。反馈鼓励对主题进行排序，扩展不发达的想法，并进行整体修改以提高段落的清晰度和连贯性。模型反馈使用由人类评估者评分的本科生关于各种主题的论文进行评估。对于所有类型的反馈，模型的准确性都很强。这开辟了新的研究分支，探索在段落和文章级别生成个性化反馈。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 9th International Conference on Learning Analytics & Knowledge

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