Recently, formative feedback in writing instruction has been supported by technologies generally referred to as Automated Writing Evaluation tools. However, such tools are limited in their capacity to explore specific disciplinary genres, and they have shown mixed results in student writing improvement. We explore how technology-enhanced writing interventions can positively affect student attitudes toward and beliefs about writing, both reinforcing content knowledge and increasing student motivation. Using a student-facing text-visualization tool called , we hosted revision workshops for students (n = 30) in an introductory-level statistics course at a large North American University. The tool is designed to be flexible: instructors of various courses can create expectations and predefine topics that are genre-specific. In this way, students are offered non-evaluative formative feedback which redirects them to field-specific strategies. To gauge the usefulness of Write & Audit, we used a previously validated survey instrument designed to measure the construct model of student motivation (Ling et al. 2021). Our results show significant increases in student self-efficacy and beliefs about the importance of content in successful writing. We contextualize these findings with data from three student think-aloud interviews, which demonstrate metacognitive awareness while using the tool. Ultimately, this exploratory study is non-experimental, but it contributes a novel approach to automated formative feedback and confirms the promising potential of Write & Audit.

中文翻译：

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可视化统计写作中的形成性反馈：使用 DocuScope Write & Audit 对学生动机进行探索性研究

最近，写作教学中的形成性反馈得到了通常称为自动写作评估工具的技术的支持。然而，这些工具探索特定学科类型的能力有限，并且在学生写作改进方面的结果好坏参半。我们探讨技术增强的写作干预如何积极影响学生对写作的态度和信念，既强化内容知识又提高学生的积极性。我们使用面向学生的文本可视化工具 ，为北美一所大型大学入门级统计课程的学生（n = 30）举办了复习研讨会。该工具设计灵活：各种课程的教师可以创建期望并预定义特定流派的主题。通过这种方式，学生可以获得非评价性的形成性反馈，从而将他们引导到特定领域的策略。为了衡量“写作和审核”的有用性，我们使用了之前经过验证的调查工具，旨在衡量学生动机的构建模型（Ling 等人，2021）。我们的结果显示，学生的自我效能感和对成功写作中内容重要性的信念显着提高。我们将这些发现与三名学生有声思考访谈的数据结合起来，这些访谈展示了使用该工具时的元认知意识。最终，这项探索性研究是非实验性的，但它为自动形成性反馈提供了一种新颖的方法，并证实了写入和审计的巨大潜力。