School / Department / UnitSupply Chain and Information Management
Funding Scheme/SourceIron Mountain and Research Matching Grant Scheme (RMGS)
Project TitleStudy on Digital Standard for Assessing Students' Learning Performance with Data Analytics
Project Team (HSUHK Staff)Dr Daniel MO (PI)
Dr George HO (Co-PI)
Dr Stephen NG (Co-I)
Dr Jack WU (Co-I)
Project Period2019-09-20 to 2023-01-01 (Completed)
Funding Amount$5,400,000 (in-kind) and $5,400,000 (RMGS)
Other Collaborating PartiesIron Mountain
AbstractThis research initiative aims to evaluate the effectiveness of establishing a digital platform that enables the use of data analytics for assessing students’ learning performance and facilitates the standardization of the assessment process in higher education sector. While different pedagogical assessment methods (e.g., assignments and examinations) are selected to achieve the learning outcomes, their effects on the outcomes, however, have not been systematically estimated and validated yet, much less identifying the contingencies that can strengthen such expected relationships. Teachers and module coordinators are still in a quandary what the most effective assessment methods are and whether the existing design of the assessment methods could reflect the module learning objectives. In addition, the existing learning outcomes are defined qualitatively lacking rigorously validated operationalization.

In our study, we will (1) use an established e-platform to digitize the students’ academic outputs such as assignments, project reports, mid-term test papers and final examination papers, (2) develop a standardized assessment process based on the e-platform to establish a digitized database capturing students’ academic outcomes obtained from various pedagogic assessment methods and to measure their learning outcomes via the platform for teachers’ data analysis, and (3) evaluate the effectiveness of such doing in improving the validity of teachers’ pedagogic assessment methods.

All in all, establishing a digitized platform guided by a standardized protocol to enable data analytics is a significant step toward success, though the associated effort may be huge. By adopting an established e-platform provided by Iron Mountain, we shall be able to define the assessment process and identify the practices of teachers, external examiners, and module coordinators in the course of performance assessment via the digital platform. Second, the platform facilitates teachers/module coordinators to consolidate digitized data collected from different stages of the assessment process so that detailed analytics can be undertaken according to the need of teachers/module coordinators. Having said that, the cost of the platform development is huge. According to Iron Mountain, the market price amounts to HKD 5.4M. The principal investigator of this research, Daniel Y. Mo, conducted studies on systems design (Mo et al., 2009; Mo et al., 2014) and process analytics (Mo et al., 2019) for various supply chain management systems, and he aimed at exploring process analytics for various supply chain management systems, and he aimed at exploring process analytics for e-leaming purpose in this research. The research objectives of this project are as follows:

• To design a digital platform for standardizing the digitalization process of assessment in higher education sector;

• To investigate the decision rules mapping between various student assessment results and learning outcomes; and

• To share the research results in the forms of workshop and case studies.

Selected Publications

D.Y. Mo, Y.M. Tang, E.Y. Wu, V. Tang (2022). “Theoretical model of investigating determinants for a successful Electronic Assessment System (EAS) in higher education”, Education and Information Technologies.
L. Luo, Y. Wang and D.Y. Mo (2022), "Identifying COVID-19 Personal Health Mentions from Tweets Using Masked Attention Model," IEEE Access, Vol. 10, pp. 59068-59077, doi: 10.1109/ACCESS.2022.3179808.
Y. Wang, D.Y. Mo, H.L. Ma (2023), “Perception of time in the online product customization process”, Industrial Management & Data Systems, Vol. 123, No. 2, pp. 369-385.