MMI Survey Data Analysis

Methodology

Data Sources

This study draws on two separate rounds of teacher feedback surveys conducted at Marymount International School Rome during the 2024–2025 academic year:

January 2025
- Dataset: PowerSchool Data Dashboard Insights – Team Feedback
- Collected during professional development sessions, focusing on collective team observations and needs.
May–June 2025
- Dataset: PowerSchool Data Dashboard Survey – Individual Feedback
- Collected via individual teacher surveys toward the end of the school year to capture personal perspectives on dashboard functionality.

Across both surveys, teachers from all divisions (ECC, Elementary, and Secondary) provided qualitative feedback on desired data dashboard features.

Data Preparation and Cleaning

The original datasets were cleaned in Excel to produce two analysis-ready files:

january_cleaned.xlsx
mayjune_cleaned.xlsx

Cleaning steps included:

Standardizing column names (e.g., consolidating multiple suggestion fields into a single Dashboard Feature Wishes column).
Removing empty rows and irrelevant entries.
Ensuring consistent labels for school divisions (e.g., combining ECC + Elementary).

These cleaned files were then imported into RStudio and merged into a combined dataset of 39 unique teacher feedback entries.

Text Analysis and Thematic Coding

The qualitative responses in the Dashboard Feature Wishes column were analyzed using:

Word frequency analysis, with stop words and common filler terms removed, followed by lemmatization to combine variants (e.g., “students”, “student’s” → “student”).
N-gram analysis, to explore common 3- to 5-word phrases, although ultimately single words provided clearer actionable insights.

Themes were then identified based on recurring keywords, resulting in six major categories:

Progress Tracking
Grade-Level Comparison
Behavior & Attendance
Assessment Clarity
Individual Insights
Dashboard Accessibility / Design/Layout

Both automatic keyword tagging and manual review of quotes were used to ensure all feedback was categorized, culminating in a complete thematic dataset.

Visualization and Reporting

The analysis produced several visual representations:

Bar charts showing most frequently mentioned themes and words.
Word clouds highlighting key vocabulary across all teachers and by division.
A grouped bar chart comparing themes between ECC & Elementary vs. Secondary teachers.

All visuals were generated in RStudio using packages such as dplyr, tidytext, ggplot2, and wordcloud, ensuring reproducibility.

Findings and Insights

Analysis of teacher dashboard feature wishes across both data collections (January team-based feedback and May–June individual surveys) revealed consistent priorities:

Progress tracking and grade-level comparisons were the most frequently cited needs, emphasizing teachers’ desire for clear views of student growth over time and benchmarks across cohorts.
Behavioral and attendance data were also strongly requested, underscoring interest in holistic student profiles.
In the elementary sections, teachers emphasized class-level summaries, behavioral incidents, and simple assessment visuals, while secondary teachers focused more on trends, cohorts, and longitudinal data.
The combination of cleaned datasets (39 total entries after standardization) provided a robust snapshot of teacher needs, confirmed by both word frequency plots and direct thematic coding of individual quotes.

These findings support the design of a more flexible and comprehensive data dashboard that integrates academic progress, behavioral trends, and comparative analytics in a user-friendly format.

Figures

Figure 1: Most Requested Dashboard Features by Theme (whole school)

Most Requested Features

Footnote:
This bar chart displays the total mentions of each thematic dashboard feature category based on teacher responses from ECC through Secondary, collected in January and June 2025. “Progress Tracking” and “Grade-Level Comparison” were the most frequently cited needs, followed by features related to behavior, assessment clarity, and individual student insights.

Figure 2: Word Clouds of Most Frequent Words by School Section

Side by Side Wordcloud

Footnote:
These side-by-side word clouds visualize the most frequently used words in teacher responses, split by school section. The left cloud represents ECC & Elementary teachers, highlighting terms like “class”, “behavior”, and “assessment”. The right cloud shows Secondary teachers’ priorities, emphasizing “grade”, “trend”, “cohort”, and “progress”. Larger words appear more frequently in the data.

Figure 3: Overall Word Cloud for Most Frequent Words (whole school)

Overall Wordcloud

Footnote:
This word cloud illustrates the most common words across all teacher responses from ECC through Secondary. Prominent terms include “grade”, “class”, “behavior”, “progress”, and “information”, reinforcing the thematic needs identified in the bar charts.

Figure 4: Top 25 Words Mentioned in Dashboard Feature Wishes

Top 25 Words

Footnote:
This bar chart shows the 25 most frequently occurring individual words in teacher comments, based on cleaned and lemmatized text from both surveys. It highlights the prevalence of terms related to assessment and progress tracking, as well as behavior and documentation.

Figure 5: Comparison of Thematic Feature Requests by School Division

Theme by Division

Footnote:
This grouped bar chart compares the frequency of each major dashboard feature theme between ECC & Elementary (orange) and Secondary (blue) teachers. It reveals stronger emphasis on progress tracking and grade-level comparisons across both divisions, with Secondary teachers showing slightly higher interest in cohort analytics and trend data.

Figure 6: Top Dashboard Feature Wishes by Frequency (alternative breakdown)

Top Dashboard Wishes

Footnote:
This alternative bar chart representation highlights the relative frequency of key words like “grade”, “class”, “behavior”, “progress”, and “attendance” in teacher feature requests, reinforcing the main areas where dashboard improvements are most desired.