電子學習數據分析

電子學習數據分析

  • 電子學習數據分析
    ART/190CP
    20150127 - 20160726
    12921

    陳國嫻博士
    There are five major deliverables in this project 1. Learning Data Collection i. Define an open interface to be connected to third party LMS and publishers’ server ii. Information Visualization software tool a) presentation of data in the form of learning dashboards which provide overview learning data through data visualization tools b) Teachers may be able to select the types of data to be presented 2. Quiz-based Learning Data Analysis Engine i. Models definition based on EDB Learning data a) Models should be able to identify the areas of weakness for the students b) Models constructed based on more than 5000 training questions (including questions data and student profile data) ii. Learning analysis engine implemented in software and applied in primary school mathematics subject evaluation iii. Automatic feedback capability - new set of questions targeting the weakness can be generated automatically a) Identify areas of weakness of students, and to provide students with tailored learning pathways, or assessment materials (90% of the identifications is in agreement with school teachers) b) 90% of the auto generated set of questions are accepted by school teachers, which will be validated in the school trial. 3. Context based Text Mining/Text Analysis Software Engine This focuses mainly on primary school students' Chinese writing i. Develop text mining algorithm specific for identifying Colloquial Cantonese (software) ii. The algorithm will be able to identify 80% of the common errors in the writings of primary school students 4. Learning Analytics Server i. Connection to third party LMS, publisher sites (at least two sites) based on the open interface ii. With text mining/text analysis engine and learning data analysis engines built in iii. Provide instant suggestions on the correction on common errors with accuracy of 80% 5. EDB School trial of learning analytics server i. With at least 5 schools ii. Report generation
    劉文健博士 吴志剛先生 王雁晨女士 洪丹博士 蘆運照博士 陳文財先生 何嘉穎女士 莊秋萍女士 楊逸先生 王蕊小姐 陳宜廉先生 林愛華女士
    教育局

    在雲端學習環境中,不同的網站提供了形形色色的學習活動。這些活動數據,對分析學生的學習行為的全面性尤其重要。因此,有需要去定義一個標準的接口,令到網站之間的數據交換更簡單和快捷。 收集數據後,可作進一步的分析。根據活動的性質,兩種類型的分析可以進行。首先,以測驗為基礎的分析。它採用數據挖掘和分析技術來評估學生在練習或測驗中的表現,去識別個別學生的弱點。一組新的問題或補充材料的建議,針對著學生的弱點,可以自動產生。這將大大提高個人化的學習經驗。 第二,基於文章內容的分析。它利用文本挖掘和分析技術,針對學生在論壇或文章的寫作,辨別口語化的中文用詞,即時指出學生的錯誤並作出修改建議,不需教師太多的干預。