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

传统教科书旨在将信息从印刷页面传递给学习者，而现代数字教科书提供了学习学习者解释和处理所读信息的机会。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出个性化的建议。怎样才能确定学习者的心理状态？打开一本用过的纸质教科书，答案很清楚：学生们觉得必须通过标注重点段落、标签、问题和笔记来参与课文。尽管学生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什么教育效益。在最好的情况下，重点突出的段落被重读以备考，这种策略远不如自考等其他策略有效。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的免费、同行评审、公开许可的材料，这些材料已被广泛应用于一系列机构，特别是社区大学，因此该技术将超越精英机构，为广大服务不足的学生提供一个潜在强大的学习工具。该项目采用了大数据方法，包括从学习者群体中收集注释，以得出关于单个学习者的推论。该项目将决定如何利用这些数据来模拟认知状态，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，并实施改善学习成果的干预措施。将开发一种工具，对与学生的重点相关的材料进行适当的定时测试。将采用协同过滤方法，利用人口数据为个人推荐特定的段落。所建议的工具将把选定的段落重新编排成复习问题，鼓励积极地重建和阐述知识。该工具的设计和实现将由创新的OpenStax教科书平台中的随机对照研究和协调的实验室研究提供信息。这些研究将解决一些基本的科学问题，这些问题涉及学生为什么要做注释，如何提高他们的注释技能，以及如何优化注释的使用，以指导积极的复习。

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.