A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education
- Allow teachers/senior management to quickly access vital teaching and learning data from sources all in one place;
- Provide relational information to create snapshots of teaching and learning performance for teaching and learning quality improvement;
- Provide the interface for users to analyze different time periods and trends to make future predictions on teaching and learning changes;
- Expand capabilities for deeper insights and more robust analysis of teaching and learning performance;
- Develop visualizations and forward-looking business intelligence from big teaching and learning data;
- Offer “what-if” scenarios to inform practical decisions based on more comprehensive analysis.
2. Literature Review
3. Benchmark of International Practices
4. Methodology and System Design
4.1. Data Source
- Student Management under the Banner ERP System;
- Learning Management System (Moodle);
- Data from Institutional Surveys (for example, Term-End Course Teaching and Learning Evaluation (CTLE) Survey raw data, First Year Student Learning Experience Survey data, Final Year Student Learning Experience Survey data, Employers Survey data, and Alumni Survey, etc.);
- Data of professional development activities.
4.2. Scope and Multidimensional Data Model
4.3. Data Warehouse Architecture
- Determine system requirements and obtain appropriate values from program leaders and senior management; acquire a diverse set of user requirements from various academic departments in order to obtain the necessary data and reports; identify data sources such as owner, availability, constraints, and quality;
- Design and build a high-performing multi-dimensional model in the database, according to the data from the user requirements in step 1, i.e., SQL Server in this study, according to the user requirements collected from step 1;
- Collaborate with data source owners and IT teams to create database schema and the ETL program for extracting, transforming, and loading source data into the data warehouse system;
- Design the data marts and OLAP cubes in the data warehouse and perform complex measurements and calculations based on user requirements;
- Develop a dynamic and interactive user interface in Power BI for end-users to explore data via mobile, PC, or other applications (for example, Excel, Power BI);
- Launch the System Integration Testing (SIT) and User Acceptance Test (UAT) of the developed solution with end-users and deploy it to the Power BI Cloud;
- Review the developed solution and user feedback and continue the dashboard development.
4.4. User Interface
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Li, Q.; Duffy, P.; Zhang, Z. A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education. Systems 2022, 10, 96. https://doi.org/10.3390/systems10040096
Li Q, Duffy P, Zhang Z. A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education. Systems. 2022; 10(4):96. https://doi.org/10.3390/systems10040096Chicago/Turabian Style
Li, Qingyun, Peter Duffy, and Zhongyang Zhang. 2022. "A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education" Systems 10, no. 4: 96. https://doi.org/10.3390/systems10040096