AI & DATA SCIENCE / STUDENT EXPERIENCE
Welcome Week insight
Making successive survey cycles comparable so service teams could investigate change over time.
How the use case emerged
Annual student feedback offered a view of digital services, but changing question wording and file structures made direct comparison unreliable. The task was to create a repeatable and explainable view of overlapping themes.
Making a fair comparison
I used Python to inspect each export, map changing question labels onto genuinely shared themes and harmonise response scales across three annual cycles (2023/24, 2024/25 and 2025/26). Missing and “not applicable” answers stayed separate from scored responses. Where a consistent overall question was unavailable, I labelled the combined theme score as a proxy rather than a direct satisfaction measure.
Compare themes that can genuinely be aligned across cycles.
Show coverage and variation, not only averages.
Use associations to guide investigation, not claim causation.
A comparable theme needs three checks
What changed
The repeatable analysis made coverage, year-on-year direction and potentially important themes easier to discuss with service owners. It supported prioritisation questions rather than a claim that a particular intervention had already improved experience. Correlation can suggest where to look next, but does not prove that changing one theme will improve the overall experience.
Evidence boundary: The analysis used real survey exports. This page includes no individual responses, small-group breakdowns, source files or internal paths.
EXPLAINABLE AI & DATA / READ THE METHOD
Read the evidence, understand the method.
A guide to the charts and case evidence on this page. Exhibit captions remain the authority for data origin and the claims each visual supports.
Follow the reasoning.
- SourceRead the exhibit’s evidence label
- RepresentInspect units, categories and scales
- CompareDistinguish observed results from demonstrations
- ReviewCheck limitations before taking action
Explore the building blocks.
Number of concepts in this guide, not performance or usage measures.
Explore the explanation matrix · 14 concepts
14 concepts shown
On small screens, scroll the table sideways. “Learning context” explains a related idea; it does not mean that method runs on this page. Deep learning is the standard term for “deep ML”.
| Technical term | Technical description / definition | In plain English | How this page uses it |
|---|---|---|---|
| Visual encodingVisualisation | Mapping values or categories to marks, position, length, colour and labels.Primary reference ↗ | Make the comparison visible, with a scale and a clear label. | Published analysis Read axis labels, denominators and captions before comparing exhibits. |
| Synthetic dataAI/Data Concepts | Generated records used to represent scenarios rather than directly report observed individuals or events.Primary reference ↗ | A practice dataset. Useful for demonstration, not proof of workplace impact. | Demonstration Exhibits labelled synthetic or reconstructed illustrate methods; they are not disclosed workplace records. |
| Descriptive analyticsAI/Data Concepts | Summaries of observed data using counts, rates, distributions or comparisons.Primary reference ↗ | Tell us what the records show; this alone does not predict next week. | Published analysis Counts and trend summaries describe the stated records and period, not future performance. |
| Residuals and prediction errorEvaluation & XAI | A residual compares an observed value with its fitted or predicted value. Error metrics summarise those discrepancies.Primary reference ↗ | How far did the estimate miss, and where does it miss most? | Learning context Prediction exhibits can compare observed and estimated values; a smooth chart alone is not validation. |
| Confusion matrix, precision and recallEvaluation & XAI | A confusion matrix counts predicted versus actual classes. Precision measures positive-result reliability; recall measures positive-case coverage.Primary reference ↗ | Which alarms were right, which were false, and which cases were missed? | Learning context Where a confusion chart is shown, inspect false alarms and missed cases as well as correct predictions. |
| Data science pipelineData Science Pipeline | An ordered, reproducible sequence for acquiring, validating, preparing, analysing and evaluating data.Primary reference ↗ | A chain from a question and source to an answer someone can check. | Published analysis Case studies connect the problem, evidence, preparation, method, evaluation and decision. |
| Explanation versus causal proofEvaluation & XAI | A model explanation describes selected behaviour or contributions; it does not establish causal effects or full reliability.Primary reference ↗ | A reason for this output is useful, but it is not proof that the model is right. | Responsible use A persuasive visual does not prove causation or transferability. |
| Data minimisationResponsible AI | Limit collected, disclosed and retained information to what is needed for the stated purpose.Primary reference ↗ | Use the smallest appropriate amount of information. | Responsible use Restricted source records stay private; approved aggregates and labelled reconstructions support public learning. |
| pandasPython Package | A Python library for labelled tabular data manipulation and analysis.Primary reference ↗ | A programmable workbench for cleaning and comparing tables. | Learning context A common Python option for tabular preparation; its inclusion is not a claim that this browser runs pandas. |
| MatplotlibPython Package | A Python plotting library for static, animated and interactive visualisations.Primary reference ↗ | Turn numerical work into labelled figures that can be inspected and shared. | Learning context A common way to prepare Python figures. This page displays published assets rather than launching Python plotting sessions. |
| Machine learning (ML)AI Capability | Methods that estimate patterns or parameters from examples to support inference on other inputs.Primary reference ↗ | Learn a pattern from examples, then check whether it helps with new ones. | Learning context ML learns from examples. Read the page-specific methods below to see whether any model runs here. |
| Deep learningAI Capability | Machine learning using neural networks with multiple representation-learning layers.Primary reference ↗ | A layered model learns useful representations; more layers do not guarantee a better answer. | Learning context Deep learning is an AI capability, not a label for every chart. No deep model is implied by this explanation panel. |
| Natural language processing (NLP)AI Capability | Computational methods for representing, analysing or generating human language.Primary reference ↗ | Help a computer work with words, while remembering that context matters. | Learning context Text search, translation, thematic classification and generation are different language tasks; their implementation is identified separately. |
| Sentiment analysisAI Capability | A language-analysis task that estimates an expressed attitude, often as polarity or sentiment categories.Primary reference ↗ | Estimate the tone of words—not the private feelings or personality of their author. | Learning context Sentiment estimates expressed tone. It must not be confused with topic classification or a diagnosis of a person. |
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