Funded by the Teaching Development Grant (2015-2016) of the University, this project aims to improve the teaching and learning of Common Core Course through learning analytics (LA) with an emphasis on outcome-based learning (OBL). Common Core Courses (CCCs) are designed to provide significant exposure to ideas, issues, skills, and values, outside of the core discipline of students, through leveraging the expertise across different faculties, whereas learning analytics is the measurement, collection, analysis and reports of data collected in a learning environment with automated means, helping to obtain knowledge about student learning in near real time. Various challenges of CCCs including a huge class size, students’ diverse background, reliance on non-traditional course activities (e.g., group projects, tutorial participation, etc.) all present challenges to both instructors and students. In this project, a learning analytic tool has been developed for instructors and learners to visualize the learning progression. It can serve as a basis for learners to carry out self-monitoring of outcome-linked learning progress, and  help teachers monitor student progress and decide on possible interventions for at-risk students after judicious interpretation of data.

Researchers

Principal Investigator
Dr. Xiao Hu, Faculty of Education, University of Hong Kong

Co-Investigators
Dr. Chi-Un Leon Lei, Technology-Enriched Learning Initiative, University of Hong Kong
Prof. Nancy Law, Faculty of Education, University of Hong Kong
Dr. King-Wa Fu, Journalism and Media Studies Centre, University of Hong Kong
Dr. Gaowei Chen, Faculty of Education, University of Hong Kong

Collaborators
Dr. Wai Ching Paul Wong, Department of Social Work and Social Administration, University of Hong Kong
Dr. Jiangnan Zhu, Faculty of Social Sciences, University of Hong Kong
Dr. Hugo Horta, Faculty of Education, University of Hong Kong

Deliverables

  • Analytic procedures and tools that can be applied to other courses using Moodle
    Predictive models are constructed for predicting student course performances based on their online behaviour. A rudimentary visual analytic tool, in the form of a dashboard, is developed to predict a student’s individual overall performance in a course.
    Documentations of the predictive model and the tool (Click the figure to download):

 

  • A tool (dashboard) developed as a plugin in both Moodle and Open EdX that monitors students’ learning process
    The tool, referred to as the “Learning Analytic Moodle Block”, has been installed in the backend of EDU Moodle of the Faculty of Education, and is available for activation in different courses apart from the participating CCCs in this project. Besides this Moodle block, we also developed a similar tool on Open EdX, the Progress Dashboard, which is the prominent platform for MOOCs of the University.
    The screenshots of the tools can be found in the pdf document below:
    LAK17_LA tool poster_20170126_preprint_modified

 

  • A CCC instructor tutorial kit recording major findings and suggestions made in this project, for the reference of all CCC teachers
    This toolkit consists of two instructional videos, major findings of the project, and suggestions on the best practices of Moodle usage in CCCs.
    CCC instructor tutorial kit(Click the figure to download):   

 

  • Talks related to this project
    “Predicting Student Learning Outcomes in Common Core Courses”. May 2016, Faculty of Education, University of Hong Kong. SoL_Talk_20160526

    “Automated Analysis of Student Contributions and Behaviors” Sep. 2015, Faculty of Education, University of Hong Kong. CITE_Talk_20150923_v3

    “How We Used Moodle: A Sharing Session”. Dec. 2016.  Centre for the Enhancement of Teaching and Learning (CELT), University of Hong Kong. CETL Seminar_MOODLE_20161207

 

  • Publications
    • Hu, X., Ng, J. T. D., Lu, T., & Lei, C-U. (2016). Automating Assessment of Collaborative Writing Quality in Multiple Stages: The Case of Wiki, In Proceedings of the 6th International Conference on Learning Analytics and Knowledge, LAK ’16, April 25 – 29, 2016, Edinburgh, United Kingdom. DOI=10.1145/2883851.2883963.(pdf)
    • Tian, L., Hu, X., Ng, J., Lei, L. (2016). Towards Automatic Assessments of Collaborative Writing on Wiki, Paper presented at the Centre for Information Technology in Education Research Symposium (CITERS), The University of Hong Kong, Hong Kong.
    • Hu, X., Hou, X., Lei, C-U., Yang, C., & Ng, J. (2017). An Outcome-based Dashboard for Moodle and Open EdX, In Proceedings of the 7th International Conference on Learning Analytics and Knowledge, LAK ’17.Hu, X., Cheong, C. W. L., Ding, W., & Woo, M. (2017).(pdf)
    • A Systematic Review of Studies on Predicting Student Learning Outcomes Using Learning Analytics, In Proceedings of the 7th International Conference on Learning Analytics and Knowledge, LAK ’17.(pdf)
    • (in preparation). Participatory Design for a Learning Progress Monitoring Tool.
    • (in preparation). Learning Outcome Prediction and the Contexts.

 

Related Pages

This tool has also been publicized on the E-learning website of the Facutly of Education, in the hope that different parties (e.g., e-learning practitioners, researchers, instructors, students, etc.) take interest in this tool and that more collaboration between the project team and these parties will be anticipated.

http://elearning.edu.hku.hk/2017/01/23/student-learning-analysis/

 

Related Multimedia Resources

We have produced two instructional videos on using this Moodle LA tool, one for the instructors and another for the students:

For instructors:

For students: