Personalized Online Adaptive Learning System

个性化在线自适应学习系统

• 批准号: 20H01719
• 负责人: クロス ジェフリーS
• 金额: $ 5.49万
• 依托单位: Tokyo Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2020
• 资助国家: 日本
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20H01719/
• 关键词: online learning; personalized learning; AI; dashboard; edtech; machiine learning; metacognition; LMS; software; オンライン学習; 個人学習; 学習管理システム; 仮想現実; 人工知能; メタ認知; ラーニング分析; eラーニング; 機械学習; 教育技術; 個人化された学習

The Personalized Online Adaptive Learning System (POALS) is a web-based add-on to a learning management system (LMS) developed to help learners succeed when taking online learning courses. It is made up of three components: 1) the Metacognitive Tutor to equip students with metacognitive skills needed for autonomous learning crucial to online learning, 2) the Adaptive Engine to help students manage the cognitive strain of having metacognitive tutoring alongside domain knowledge learning, and the 3) Analytics Dashboard based on student responses on the Metacognitive Tutor to give feedback to teachers on where learner interventions might be needed. In 2021, a variation of POALS' Metacognitive Tutor was added to AI Kaku, an AI-assisted writing tool also created at the PI's laboratory. In 2022, the focus had been on adaptive learning through capacity-building in gamification and virtual reality. Key takeaways from this research include which metacognitive skills can be learned independently and which ones require nudging from teachers, which algorithms can work best for knowledge tracing while considering metacognitive measures, and how text data from learner metacognitive reflections can be used for learning analytics. Research achievements for 2022 include one peer review journal paper published and 9 conference presentations for both domestic and international conferences.

个性化的在线自适应学习系统（POALS）是基于Web的学习管理系统（LMS）的附加组件，以帮助学习者在参加在线学习课程时取得成功。它由三个组成部分组成：1）为学生提供对在线学习至关重要的自主学习所需的元认知技能，2）自适应引擎，可帮助学生管理元认知辅导与域知识学习以及3的分析仪表板的认知障碍，以使学生的响应能力依赖于元研究，以使学生对教师进行教师的贡献，从而使教师提供了教师的养生员，以提供教师。在2021年，POALS的元认知导师的变体被添加到Ai Kaku，这是AI辅助写作工具在PI的实验室也创建的。在2022年，通过游戏化和虚拟现实中的能力建设来进行自适应学习。这项研究的关键要点包括可以独立学习哪些元认知技能，哪些需要从教师那里汲取灵感，哪些算法可以在考虑元认知措施的同时最适合知识追踪，以及如何将来自学习者元认知反思的文本数据用于学习分析。 2022年的研究成就包括一份发表的同行评审杂志，以及为国内和国际会议的9次会议演讲。

项目成果

Learning analytics dashboard prototype for implicit feedback from metacognitive prompt responses

学习分析仪表板原型，用于从元认知提示响应中获取隐式反馈

• 发表时间: 2021
• 作者: Carlon;May Kristine Jonson and Cross;Jeffrey Scott
• 通讯作者: Jeffrey Scott

Japanese Elementary Schools Playful Programming Curriculum Considerations: Readiness, Limitations and Teacher Training

日本小学趣味编程课程考虑因素：准备情况、局限性和教师培训

• 发表时间: 2021
• 作者: Gougeon;Luc and Cross;Jeffrey Scott
• 通讯作者: Jeffrey Scott

Predicting Guesses and Slips Through Question Encoding with Complexity Hints

通过复杂性提示预测问题编码的猜测和失误

• 发表时间: 2022
• 作者: May Kristine Jonson Carlon;Sangwhan Moon;Naoaki Okazaki;Jeffrey S. Cross
• 通讯作者: Jeffrey S. Cross

Open response prompts in an online metacognitive tutor

在在线元认知导师中打开回答提示

• 发表时间: 2021
• 作者: Carlon;May Kristine Jonson and Cross;Jeffrey Scott
• 通讯作者: Jeffrey Scott

Global Learning at Scale

大规模的全球学习

• 发表时间: 2020
• 作者: David A. Joyner;May Kristine Jonson Carlon;Jeffrey Scott Cross;Eduardo Corpo;Rocael Hernandez Rizzardini;Oscar Rodas;Dhawal Shah;Manoel Cortes-Mendez;Thomas Staubitz;Jose; Ruirez-Valiente
• 通讯作者: Jose; Ruirez-Valiente