Need: The DRE is looking to improve efficiency and eliminate siloing for the analysis and reporting of the MOE's tests and assessments.
Purpose: To develop tools to automate data cleaning and analysis and to centralize storage of data and reports for the MOE's Palau English Reading Assessment (PERA).
Developer: Ms. Yaxuan (Rebecca) Li, TCinGC 2021 student consultant.
Local Partner: Mr. Keizy Shiro, DRE Assessment Specialist.
Status: Just started on June 3 2021.
Next: Expected to end about August 10 2021.
In general the MOE has issues with data siloing and lack of formal processes for data storage and analysis. This results in duplication of effort, long process times, and inability to audit and verify reports. While efficiency studies have not been conducted, in at least two individual cases, process times has been reduced from several days to two hours after implementing centralized storage and some automation.
Using the Division of Research and Evaluation (DRE) as an example, the graphic below illustrates the current state of data analysis, and shows the role of the silo in the process.
The desired workflow is illustrated in the graphic below. It sets up a centralized area with automation tools and central storage. The tools handle cleaning, uploads to storage, downloads from storage, analysis, and report generation. The desired workflow places automation tools at certain points in the process to take care of repetetive tasks, store data, move data where needed, and eliminate silos by provide access to the latest and cleanest data to everyone (obviously security will be implemented to allow only authorized access).
Note that DRE is missing from the graphic. In the current scenario, data ended up with Keizy at DRE because he was assigned to analyse the report. Hence the data is in Keizy's silo in the DRE. In the desired scenario, the data goes to the most appropriate place for storage. The owner of the data, BCI, can have access, and so can other people who need to perform analysis on the data or perform and audit of a report.
Please note that this is a more lower scale treatment of the situation that was addressed by the "Data Analysis Stack 2020" project. That project looked at the whole process from data source to the report. It was not adopted after its completion. So this project can help out while DRE assesses its options regarding its data analysis process.
|Ms. Yaxuan (Rebecca) Li is the developer for this project. Ms. Li is a rising junior at Carnegie Mellon University majoring in Information Systems and a minor in Business Analytics and Optimization and Human Computer Interaction. She is from Beijing, China and loves traveling around the world. She is able to speak four different languages, including Mandarin, English, Japanese, and French. She is interested in developing innovative ideas and talking with creative intellectuals. Ms. Li's Resume.|
|Mr. Keizy U. Shiro is the local counterpart to Ms. Li on the project. Mr. Shiro is originally from the beautiful state of Ngaraard, situated to the north-east of Babeldaob Island in the Republic of Palau. Mr. Shiro has over 18 years of professional work experience, which began with his employment career with Palau High School as a classroom teacher in 2000. He last served as Palau High School Registrar from 2004 to 2007. Mr. Shiro is currently working as the Testing Coordinator at the Division of Research and Evaluation within Palau’s Ministry of Education.|
The Palau English Reading Assessment (PERA) workflow was selected to the target of the project. The two will work to create and establish a PERA workflow that is more automated and more centralized. The automation improves efficiency. The centralization reduces duplication of effort and eliminates data and process silos.
The basic tasks of the workflow are: clean the data, store the data, grab the data from storage, analyse the data, and generate reports. The project will build tools to automate these tasks as much as possible and to facilitate the workflow, ie., transferring the data between storage and users and tools.
Weeks 1 and 2: Orientation, Context Review and Analysis, Project Selection.
Week 3: Research and finalize how the project will be done.
Week 4: Complete the first version of the cleaning and upload tools.
July 9, 2021 0800-0800: MVP Meeting on Zoom
Weeks 5-7: Complete the basic data cleaning tool
Week 8: Refine code, generate the graphs for each table
Week 9: Generate the graphs for each table
Week 10: Wrap up the project