Abstract:
This project aims to develop a learning analytics platform to enhance the capacity of teachers in promoting students’ reflective engagement in higher education sector. The advocacy of students’ reflective learning and the trend of digital classrooms using various learning management systems (LMSs) and social network platforms (SNPs) place new demands on teachers for transforming pedagogical practices. Paralleled to it is a gigantic amount of accessible learning data that are produced and captured in the LMSs and SNPs across formal and informal learning spaces. These student-generated data footprints document what is actually happening in the learning process. It provides opportunities for teachers to examine the data through learning analytics.
This project, adopting the design-based approach, will conduct a three-year research on adaptive teaching practices in digital classrooms by co-designing and co-developing a learning analytics platform with teachers that supports students’ reflective engagement. The research foci are:
To develop a learning analytics platform conducive to data-oriented decision-making;
To evaluate the impact of a learning analytics platform on facilitating reflective engagement of students in the learning process;
To evaluate the impact of a learning analytics platform onteachers’ pedagogical decision making.
Code:
05091
Principal Project Supervisors:
Start Date:
01 Jul 2014
End Date:
31 Dec 2017
Status:
Completed
Result:
This project has conducted a three-year research on adaptive pedagogical practices by co-designing and co-developing the learning analytics platform with teachers from EdUHK, HKBU, CUHK and LUHK. Evaluations from both students and teachers indicated a positive perception towards the learning analytics platform in enhancing their learning and teaching in LMSs. The major achievements are as follows:
A learning analytics platform has been developed conducive to data-oriented decision-making. This learning analytics platform has scaled up to different subjects and universities to transform learning and teaching in higher education.
The impact of the learning platform on facilitating reflective engagement of students in the learning process has been evaluated. Both students and teachers generally agreed that the learning analytics platform enabled them to gain a better understanding of students’ learning process with useful information from a whole class of students and stimulated the development of their individual intellectual ideas.
The impact of the learning platform on teachers’ pedagogical decision-making has been evaluated. Teachers positively perceived the learning analytics platform for identifying students’ learning progress including their strengths and inadequacies so that predicting learning patterns and improving learning environments can be achieved.
Students’ learning progression patterns have been extracted so that recommendations can be made on how to improve the design of the learning activities and materials through progression pattern comparison using artificial neural network.
Impact:
The significance of this project for enhancing the quality of learning and teaching in Higher Education Institutes lies in three aspects. Firstly, teachers will benefit from using the learning analytics platform to gain a better understanding of students’ learning process, and identify learning issues to predict learning patterns and make corresponding pedagogical decision-making, hence optimizing the learning environment. Secondly, by focusing on students’ learning needs and learning trails, the adoption of the learning analytics platform will help teachers to move away from summative assessment to formative assessment; students will be able to respond to the results of analytics, which enable them to see the learning progress of his/her learning peers, thus evidence-based improvement can be achieved. Thirdly, the learning analytics platform can be scaled up to different subjects and universities to transform teaching and learning in higher education.
In conclusion, the findings provided evidence-based support for the significance of this learning analytics platform in enhancing the quality of learning and teaching in higher education institutes. This has undoubtedly encouraged the future development of learning analytics platforms for students to track the learning progress of their learning peers and for teachers to move away from summative assessment to formative assessment.
Deliverables:
Books / Book Chapters / Journal Articles / Conference Papers
Kong, S. C., Li, P., & Song, Y. (2017). Evaluating a bilingual text-mining system with a taxonomy of key words and hierarchical visualization for understanding learner-generated text. Journal of Educational Computing Research, 0/(0), 1-27. https://doi.org/10.1177/0735633117707991.
Kwong, T., Wong, E., & Yue, K. (2017). Bringing abstract academic integrity and ethical concepts into real-life situations. Technology, Knowledge and Learning, 22, 353-368. https://doi.org/10.1007/s10758-017-9315-2
Poon, L.K.M., Kong, S.C., Yau, T.S.H., Wong, M., & Ling, M.H. (2017). Learning analytics for monitoring students’ participation online: Visualizing navigational patterns on learning management system. In S. K. Cheung, L.-F. Kwok, W. W. Ma, L.-K. Lee, & H. Yang (Eds.), Lecture Notes in Computer Science: Vol. 10309. Blended Learning: New Challenges and Innovative Practices (pp. 166-176). Cham: Springer. (https://repository.eduhk.hk/en/publications/learning-analytics-for-monitoring-students-participation-online-v-6)
Poon, L. K. M., Kong, S. C., Wong, M., & Yau, T. S. H. (2017, July). Mining sequential patterns of students’ access on learning management system. In International Conference on Data Mining and Big Data (pp. 191-198). Cham: Springer. (https://repository.eduhk.hk/en/publications/mining-sequential-patterns-of-students-access-on-learning-managem-6)
Wong, E., Kwong, T., Cheung, S., Ng, G., Chiu, R., & Zhong, E. (2016). Application of Augmented Reality Mobile Learning Trails to Develop Students’ Academic Integrity. In e-Learning excellence awards – An anthology of case histories 2016 (pp. 217-232). Remenyi, D. (Ed.). United Kingdom: Academic Conferences and Publishing International Limited.
Kong, S.C., & Li, P. (2016, May). Implementing a bilingual text-mining system with hierarchical visualization to reflect the changes of learners’ understanding of academic integrity. In Y.-T. Wu, M. Chang, B. Li, T.-W. Chan, S. C. Kong, H.-C.-K. Lin, H.-C. Chu, M. Jan, M.-H. Lee, Y. Dong, K. H. Tse, T. L. Wong & P. Li (Eds.), Conference Proceedings of the 20th Global Chinese Conference on Computers in Education 2016 (pp. 708-711). Hong Kong: The Hong Kong Institute of Education. (https://repository.eduhk.hk/en/publications/implementing-a-bilingual-text-mining-system-with-hierarchical-vis-6)
Li, P., Kong, S.C., Wong, T.L., & Guo, C. (2015, November). Enhanced bilingual text analysis for BYOD with hierarchical visualization. In H. Ogata, W. Q. Chen, S. C. Kong & F. Qiu (Eds.), Proceedings of the 23rd International Conference on Computers in Education ICCE 2015 (pp. 407-412). Hangzhou, China: Asia-Pacific Society for Computers in Education. (https://repository.eduhk.hk/en/publications/enhanced-bilingual-text-analysis-for-byod-with-hierarchical-visua-6)
Li, P., & Kong, S.C. (2015, May). Contribution-oriented user relation visualization for discussion forums. In X. Gu, Y.-T. Wu &. B. Chang (Eds.), Proceedings of the 19th Global Chinese Conference on Computers in Education (pp. 844-847). Taipei: Global Chinese Society for Computers in Education. (https://repository.eduhk.hk/en/publications/contribution-oriented-user-relation-visualization-for-discussion--3)
Li, P., & Kong, S. C. (2014, November). Detailed user relation visualization on Moodle. In C.-C. Liu, H. Ogata, S. C. Kong, & A. Kashihara (Eds.), In Proceedings of the 22nd International Conference on Computers in Education, ICCE 2014 (pp. 921-926). Japan: Asia-Pacific Society for Computers in Education. (https://repository.eduhk.hk/en/publications/detailed-user-relation-visualization-on-moodle-6)
Type:
UGC T&L related project