Abstract:
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.
Code:
T0165
Principal Project Supervisors:
Keywords provided by authors:
Subjects:
Start Date:
01 Feb 2016
End Date:
31 Jan 2017
Status:
Completed
Result:
In this project, a total 40 undergraduate students in The Education University of Hong Kong from the General Education course called "Technology, Entertainment, and Mathematics" have been sampled for this improved experiment. These students took this free elective of general education course were ranging from Year 2 to Year 4 in their 4-year of study. One of the course requirements was to complete at least one reflective posting on an online discussion forum in the Moodle environment of the university. They were asked to watch a BBC documentary film called "Beautiful Equations" and then posted their reflections in the forum. Each student needed to comment on three self-selected peers, which were extracted in our experiment for analysis. There were more than 200 posts sampled from the forum from the 36 students who had completed the related study module.The results showed that the performance of students might be predicted by the quality of online contribution in the discussion forum through visualizing the text-based discussion. Forum Graph indicates the overview of students' interactivity and clarifies which students as proactive sources within the groups; in such estimation, the teacher can identify the strong tiers of students and address the isolated students who have not frequently connected with others. The topic modeling also allows teachers discover and categorize the potential topics these students were talking among them in the forum. This tool has a great potential if it is being texted more for a large-scale population.
Impact:
The impact on student learning can be seen as the knowledge we could gain through the visual analytical tool. When students are aware of the data collected and analyzed in such way, they could ensure that the teachers would have extra information to learn from their participations in online activities which could not be done before. Through this project, we learned that it is possible to identify the overall participations of the students in the class through the text-based mining techniques and the visualization technology.
Deliverables:
Books / Book Chapters / Journal Articles / Conference Papers
Wong, G. K. W., Li, S. Y. K., & Wong, E. W. Y. (2016). Analyzing academic discussion forum data with topic detection and data visualization. In Proceedings of 2016 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 109-115). New York: IEEE. (No of participants: Around 20)https://repository.eduhk.hk/en/publications/5de62fb0-106a-408a-9400-e051...https://repository.eduhk.hk/en/publications/4d8798b4-3aea-4358-9425-8ecb...
Wong, G. K. W., & Li, S. Y. K. (2016, June). Academic performance prediction using chance discovery from online discussion forums. Paper presented at The 40th IEEE Computer Society International Conference on Computers, Software & Applications (COMPSAC 2016): Connected world: New challenges for data, systems & applications, Sheraton Atlanta Hotel, Georgia, USA. (No of participants: Around 20)https://repository.eduhk.hk/en/publications/f1baacee-4f31-4d9b-8afd-ba24...
Li, S. Y. K., & Wong. G. K. W. (2016). Educational data mining using chance discovery from discussion board. In Y.-T. Wu, M. Chang, B. Li, T.-W. Chan, S. C. Kong, H. C. K. Lin, et al. (Eds.), Conference proceedings of the 20th Global Chinese Conference on Computers in Education 2016 (pp. 712-715). Hong Kong: The Hong Kong Institute of Education. (No of participants: Around 20) https://repository.eduhk.hk/en/publications/6575c76c-afba-463d-9a4d-458d...https://repository.eduhk.hk/en/publications/cbe1e99a-330f-4523-8cec-11a4...
Li, S. Y. K., & Wong. G. K. W. (2016). Educational data mining using chance discovery from discussion board. Paper presented at the 20th Global Chinese Conference on Computers in Education 2016. Hong Kong: The Hong Kong Institute of Education. (No of participants: Around 20)https://repository.eduhk.hk/en/publications/6575c76c-afba-463d-9a4d-458d...
Li, S. Y. K., & Wong, G.K.W.(December, 2016). Visualizing the Asynchronous Discussion Forum Data with Topic Detection. Paper presented at The 9th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia 2016), The Venetian Macao. (No of participants: Around 40) https://repository.eduhk.hk/en/publications/520e2c62-5ff4-46e2-b11e-f725...
Li, Simon Y. K., & Wong, G. K. W. (2016, June). Educational Data Mining using Chance Discovery from Discussion Board. The 20th Global Chinese Conference on Computers in Education 2016 (GCCCE), Hong KongWong, G. K. W. & Li, Simon Y. K. (2016, June). Academic Performance Prediction Using Chance Discovery from Online Discussion Forums. Proceedings of 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC 2016), Atlanta, Georgia.
Financial Year:
2015-16
Type:
TDG