Define Key Performance Metrics

Before you begin collecting data, it’s essential to clarify what performance metrics are necessary for the analysis. These could include:

• Grades: Average scores, pass/fail rates, and trends in student performance over time.
• Graduation Rates: The percentage of students completing the program successfully.
• Retention Rates: Percentage of students who continue in the program year after year.
• Student Satisfaction: Results from student surveys, feedback forms, or course evaluations.
• Instructor Evaluations: Ratings or feedback from students regarding the quality of instructors.
• Completion Time: Average time taken to complete the program.
• Post-graduation Success: Employment rates or career advancement data of graduates.

2. Gather Historical Data

Once you have defined the metrics, you will need to access the relevant datasets. These may come from different sources, including:

a. Internal Systems and Databases

• Student Information System (SIS): This will contain data such as grades, graduation rates, retention rates, and student demographics.
• Learning Management System (LMS): Data regarding course completions, student engagement, and instructor evaluations can typically be found here.
• Survey Tools: Use historical data from any internal survey tools or feedback platforms used to collect student satisfaction data and instructor evaluations.

b. External Data Sources

• Accreditation Reports: If available, reports from accrediting bodies may provide data on program effectiveness from a broader educational perspective.
• Industry Benchmarks: Gather external benchmarks or comparison data from similar educational programs, if available.

3. Organize Data by Cohorts and Time Periods

Organize the data based on cohorts (e.g., student groups based on enrollment year or program start date) and time periods (e.g., monthly, quarterly, or annually). This will allow you to track trends and identify any changes over time.

Key Steps:

• Cohort Grouping: Group the data by program cohorts, such as a specific batch of students who started in a particular month or year. This helps track performance trends over time and identify patterns.
• Time Frames: Set the time periods (e.g., by semester, annual reports) for which you want to analyze performance. This will allow you to compare data across different times for trend analysis.

4. Data Cleaning and Validation

Ensure that the data is clean, accurate, and complete. Address any gaps or inconsistencies before beginning analysis.

Data Cleaning Steps:

• Remove Duplicate Entries: Ensure that each student’s data appears only once in the dataset.
• Check for Missing Data: Address missing or incomplete entries. For example, if a student’s grade is missing, determine if it can be filled in based on other sources, or decide how to handle those gaps.
• Data Validation: Cross-check the data with other sources (e.g., student records or surveys) to verify accuracy.

5. Format Data for Reporting

Prepare the data in an easily interpretable format that can be easily shared with stakeholders and used for analysis.

Recommended Formats:

• Excel or CSV Files: This is the most common format for organizing and analyzing program data. It’s easily accessible and compatible with various data analysis tools.
• Google Sheets: For collaborative efforts, Google Sheets provides a cloud-based solution with real-time updates and easy sharing.
• Visualization Tools: Consider integrating the data into tools like Power BI, Tableau, or Google Data Studio to create interactive dashboards for stakeholders.

6. Create Data Submission Templates

To ensure uniformity and consistency, create submission templates that employees can use when submitting their data. These templates should include:

• Column Headers: Define clear, standardized column headers for each metric (e.g., “Student ID,” “Program Completion Date,” “Final Grade,” etc.).
• Required Fields: Make sure that fields related to key metrics are clearly marked as required (e.g., grades, program completion status).
• Instructions: Provide clear guidelines on how to enter the data, how to deal with missing information, and any common mistakes to avoid.

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Example of a Submission Template (Excel/Google Sheets Format)

Student ID	Cohort	Program Start Date	Final Grade	Graduation Status	Completion Time (Months)	Satisfaction Rating	Instructor Evaluation	Employment Status (Post-Graduation)
1001	Cohort 2023A	01/2023	A	Graduated	6	4.5/5	4.7/5	Employed (Software Developer)
1002	Cohort 2023A	01/2023	B+	Graduated	7	4.0/5	4.4/5	Employed (Data Analyst)
1003	Cohort 2023B	06/2023	C	Not Graduated	8	3.8/5	4.0/5	Unemployed

This template ensures that all necessary data points are captured and standardized across all employee submissions.

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7. Set Up Submission Deadlines and Instructions

Establish clear deadlines and instructions for when and how employees should submit their data. Consider the following points:

• Submission Timeline: Set a reasonable deadline for employees to submit their data, ensuring there is enough time to clean and analyze it before the final report is prepared.
• Submission Platform: Define the platform or repository where data will be uploaded (e.g., Google Drive, SharePoint, internal portal).
• Guidelines: Provide employees with detailed instructions on how to submit their data, including the format, file naming conventions, and any other important considerations.

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8. Monitor Submissions and Follow-Up

Ensure that the data collection process runs smoothly by monitoring submissions and following up with employees who have not yet submitted their data.

• Tracking Submissions: Keep track of which employees have submitted their data and which have not. A simple tracking sheet in Google Sheets or Excel can be used for this purpose.
• Reminders: Send out automated or manual reminders as the submission deadline approaches to ensure all required data is collected on time.

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9. Final Review and Aggregation

Once all the data has been submitted, conduct a final review to ensure it is complete and accurate. Aggregate the data from all employees into a master dataset for analysis.

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10. Share Findings and Recommendations

Once the data is aggregated, the final performance reports and insights should be shared with internal stakeholders (e.g., program managers, curriculum evaluators). These findings should be summarized in an easy-to-read report, ready for presentation or further analysis.