In recent years, e-learning systems (e.g. Moodle) have become more popular in the higher education. The systems enabled many learners to have access to online learning resources such as lecture, tutorial, etc. in a collaborative way. Due to the rapid development of technology, the amount of data stored in online learning systems has been constantly increasing in every subject. Growing interests are observed in improving teaching and learning quality by obtaining meaningful and valuable information through applying educational data mining techniques, known as learning analytics, which is not retrievable otherwise without these techniques. Most of the existing learning analytics methods focus on analyzing the data collected from the systems in order to understand their learning toward the expected outcomes, yet there has been a limited research work but a growing interest in how serendipity, as a by-product of this collaborative online learning, can occur among the students through the participation in Moodle. This project aims to develop a new analytical tool in Moodle for serendipitous findings based on the modified algorithms in text mining which can capture the serendipitous learning of students. The analyzed results would be visualized to both teachers and students by constructing different types of graphical presentations (i.e. concept maps, keygraphs, word cloud and infographics) so that teachers can assess the effectiveness of teaching and innovation of learning respectively through the visualization as the mental models. The research findings can lead to innovative teaching and learning by finding hidden and emerging patterns and linkages in students’ serendipitous learning so as to take the advantage of the available data from the learners for a holistic analysis. The identified results are expected to help both teachers and students plan how to improve teaching and learning respectively with feedbacks from this new tool. Ultimately, this creates a new approach for transformative learning and teaching in higher education by using the advanced mining technology to assess the students’ knowledge discovery process.
|Publication status||Published - 2017|
Chui, H. L. (2017). Teaching development grants final and financial report: Using learning analytics to discover serendipitous learning in Moodle for formative assessment in higher education. Hong Kong: The Education University of Hong Kong.
- Teaching Development Grant (TDG) Report
- TDG project code: T0165
- Period: TDG 2015-2016
- Teaching Development Grant (TDG)
- Educational data mining
- Topic detection
- Serendipitous learning
- Learning analytics
- Big data
- Education, Higher--Computer-assisted instruction
- Educational technology