eLearning Analytics

eLearning Analytics

  • eLearning Analytics
    ART/190CP
    20150127 - 20160726
    12921

    Ms Christina Chan
    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
    Dr Vincent LAU Mr Patrick WU Ms Adele WANG Dr Michelle Dan HONG Dr YunZhao LU Mr Asa CHAN Ms Kary HO Ms Belinda CHONG Mr Yi YANG Miss Rui WANG Mr Yee Lim CHAN Ms Amy LAM
    Education Bureau

    There exist many learning activities in the Cloud environment, which could be found in schools’ Learning Management Systems, content providers’ websites, or activities designers’ websites. Collection of these types of data is crucial in order to come up with a comprehensive profile of students’ learning behavior. Therefore, a standard interface needs to be defined so that data exchange among these entities will be simple and efficient. With the collected data, further analysis can be done. Based on the nature of the activities, two types of analyses can be conducted. First, quiz-based analysis, which uses data mining and analysis technique to evaluate the performance on exercises or quizzes, can identify a student’s weakness. A new set of questions or a recommendation on supplemental material targeting the student's weakness can be generated automatically. This will greatly enhance the personalized learning experience for a student. Second, context-based analysis, which uses text mining and analysis technique to identify the use of colloquial Chinese in students' writing in discussions and compositions. Suggestions will be made to correct these common errors without much teachers’ intervention.