Knowledge-Aware Learning Analytics Infrastructure to Support Smart Education and Learning

支持智慧教育和学习的知识感知学习分析基础设施

• 批准号: 20H01722
• 负责人: Flanagan Brendan
• 金额: $ 11.48万
• 依托单位: Kyoto University
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20H01722/
• 关键词: Knowledge Map; Knowledge Extraction; Learning Analytics; Educational Data Mining; Smart Learning Systems; Knowledge map; Smart learning systems; Knowledge extraction; Learning analytics; Educational Informatics

Inital development in the previous year lead to the implementation and evaluation of several research sub-topics, and results were also disseminated in journals and international conferences, inlcuding a new knowledge tracing model based on the latest transformer deep learning model. The later is based on a BERT style transformer and outperformed the state-of-the-art deep knowledge tracing models at the time of presentation. Other research was also conducted to examine the explainability of more classic knowledge tracing models, such as: BKT by analyzing the internal parameters of the model and how they relate with the types of quizzes being recommended. An explainable group formation method was also proposed by applying a genetic algorithm to the creation of groups for study tasks based on the students current knowledge state as estimated by the knowledge map platform. A reading recommendation system was also designed based on the knowledge map platform preliminary evaluation was conducted in a school. The design of the system was presented as a poster paper at the leading conference on learning analytics, LAK.

去年的初步开发导致了几个研究子课题的实施和评估，结果也在期刊和国际会议上传播，包括基于最新Transformer深度学习模型的新知识追踪模型。后者基于BERT风格的Transformer，在演示时优于最先进的深度知识跟踪模型。其他研究也进行了检查更经典的知识追踪模型的可解释性，如：BKT通过分析模型的内部参数，以及它们如何与被推荐的测验类型相关。一个可解释的组形成方法也提出了通过应用遗传算法创建组的学习任务的基础上，学生当前的知识状态估计的知识地图平台。设计了基于知识地图平台的阅读推荐系统，并在某学校进行了初步评价。该系统的设计在学习分析的领先会议LAK上作为海报论文提出。

项目成果

A framework to foster analysis skill for self-directed activities in data-rich environment

培养数据丰富环境中自主活动分析技能的框架

• DOI: 10.1186/s41039-021-00170-y
• 发表时间: 2021
• 期刊: Research and Practice in Technology Enhanced Learning
• 影响因子: 3.2
• 作者: 石黒直隆;松村秀一;寺井洋平;本郷一美;Yuanyuan YANG; Rwitajit MAJUMDAR; Huiyong LI; Gokhan Akapanar; Brendan FLANAGAN; Hiroaki OGATA
• 通讯作者: Yuanyuan YANG; Rwitajit MAJUMDAR; Huiyong LI; Gokhan Akapanar; Brendan FLANAGAN; Hiroaki OGATA

BEKT: Deep Knowledge Tracing with Bidirectional Encoder Representations from Transformers

BEKT：使用 Transformer 的双向编码器表示进行深度知识追踪

• 发表时间: 2021
• 期刊: ICCE 2021
• 作者: Zejie Tian;Guangcong Zheng;Brendan Flanagan;Jiazhi Mi;and Hiroaki Ogata
• 通讯作者: and Hiroaki Ogata

Identifying Student Engagement and Performance from Reading Behaviors in Open eBook Assessment

从开放电子书评估中的阅读行为中识别学生的参与度和表现

• 发表时间: 2020
• 期刊: 28th International Conference on Computers in Education (ICCE2020)
• 作者: Brendan Flanagan;Rwitajit Majumdar;Kensuke Takii;Patrick Ocheja;Mei-Rong Alice Chen and Hiroaki Ogata
• 通讯作者: Mei-Rong Alice Chen and Hiroaki Ogata

How Does The Quality of Students’ Highlights Affect Their Learning Performance in e-Book Reading

学生精彩片段的质量如何影响他们电子书阅读的学习表现

• 发表时间: 2020
• 期刊: 28th International Conference on Computers in Education (ICCE2020)
• 作者: Albert Yang;Irene Y.L. Chen;Brendan Flanagan and Hiroaki Ogata
• 通讯作者: Brendan Flanagan and Hiroaki Ogata

LA Platform in Junior High School: Trends of Usage and Student Performance

初中 LA 平台：使用趋势和学生表现

• 发表时间: 2020
• 期刊: Companion Proceedings of the 10th International Conference on Learning Analytics and Knowledge
• 作者: Majumdar Rwitajit;Kuromiya Hiroyuki;Komura Kiriko;Flanagan Brendan;Ogata Hiroaki
• 通讯作者: Ogata Hiroaki