NCS-FO: Collaborative Research: Operationalizing Students' Textbooks Annotations to Improve Comprehension and Long-Term Retention

虽然传统的教科书旨在将信息从印刷页面传输给学习者，但当代数字教科书为学习者提供了解释和处理阅读信息的机会。通过更好地了解学习者的心理状态，教科书可以为进一步的学习和复习提供个性化的建议。如何确定学习者的心理状态？打开一本用过的印刷教科书，答案很清楚：学生们觉得有必要通过用亮点、标签、问题和注释关键段落来参与文本。尽管学生们在阅读时自发地想要注释，但这种形式的互动在过去几乎没有带来教育效益。最好的情况是，突出的段落被重读以备考试，这种策略远不如其他策略（如自测）有效。该项目将开发一种新的方法：根据注释自动评估学生的知识水平，将突出显示的段落转换为适当的学习问题，并为每个学生提供适时的个性化复习。由于该项目基于OpenStax提供的免费、同行评审、公开许可的材料，这些材料已被一系列机构广泛采用，特别是社区大学，该技术将超越精英机构，为广大服务水平低下的学生提供一个潜在的强大学习工具。该项目采用了一个巨大的-一种数据方法，涉及从学习者群体中收集注释，以推断个体学习者。该项目将确定如何利用这些数据来建模认知状态，使团队能够推断学生对事实和概念的理解深度，预测随后的测试表现，并进行干预以改善学习成果。将开发一个工具，管理适当的时间测验有关的材料，学生的亮点。将采用一种协作过滤方法，利用人口数据为个人提供具体的段落供审查。拟议的工具将把选定的段落改写成复习问题，鼓励积极重建和阐述知识。该工具的设计和实施将由创新的OpenStax教科书平台内的随机对照研究和协调的实验室研究提供信息。这些研究将解决基本的科学问题，有关为什么学生注释，如何提高他们的注释技能，以及优化使用注释指导主动审查的技术。

While traditional textbooks are designed to transmit information from the printed page to the learner, contemporary digital textbooks offer the opportunity to study learners as they interpret and process information being read. With a better understanding of a learner's state of mind, textbooks can make personalized recommendations for further study and review. How can the learner's state of mind be determined? Open a used printed textbook and the answer is clear: students feel compelled to engage with their texts by annotating key passages with highlights, tags, questions, and notes. Despite students' spontaneous desire to annotate as they read, this form of interaction has reaped few educational benefits in the past. At best, highlighted passages are re-read to study for exams, a strategy not nearly as effective as other strategies such as self-quizzing. This project will develop a new methodology that: assesses student knowledge level automatically based on annotations, transforms highlighted passages into appropriate study questions, and provides each student with well-timed, personalized review. Because the project is based on free, peer-reviewed, openly licensed materials from OpenStax that have been widely adopted at a range of institutions, particularly community colleges, the technology will reach beyond elite institutions to provide a broad spectrum of underserved students with access to a potentially powerful learning tool.This project adopts a big-data approach that involves collecting annotations from a population of learners to draw inferences about individual learners. The project will determine how to exploit these data to model cognitive state, enabling the team to infer students' depth of understanding of facts and concepts, predict subsequent test performance, and perform interventions that improve learning outcomes. A tool will be developed that administers appropriately timed quizzes on material related to a student's highlights. A collaborative-filtering methodology will be employed that leverages population data to suggest specific passages for an individual to review. The proposed tool will reformulate selected passages into review questions that encourage the active reconstruction and elaboration of knowledge. The design and implementation of the tool will be informed by both randomized controlled studies within the innovative OpenStax textbook platform and coordinated laboratory studies. These studies will address basic scientific questions pertaining to why students annotate, how to improve their annotation skills, and techniques to optimize the use of annotations for guiding active review.