A GitHub-Based Learning Analytics System for Analysing Entrepreneurial Learning Behaviour in Software Product Development GitHub-Based Learning Analytics Dashboard
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jayantkhat-debug/FounderGPT

FounderGPT X — AI Operating System for Founders

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Experimentation Intensity Score

50.2 / 100

A single, comparable figure that combines engagement, regularity, issue refinement and integration into one view of a repository's development activity — calculated fresh relative to every other repository currently in the database, so scores stay meaningful as more repositories are added.

00.51EngagementCommit frequency and active-day ratio: how much sustained iterative effort is recorded.0.00RegularityCommit interval variability and longest inactivity gap: how evenly spaced activity is.1.00Issue RefinementIssue closure rate, comments per issue and resolution time: problem follow-through.N/AIntegrationPR merge rate, reviewed-PR ratio and cycle time: structured collaboration and review.N/A
How is this calculated, and why did I get this score?
Why you got this score
This score of 50.2/100 is the average of 2 applicable dimensions: Engagement (0.00), Regularity (1.00). Issue Refinement is N/A because this repository has no recorded issues; Integration is N/A because this repository has no recorded pull requests.
How it's calculated
Each dimension is min–max normalised against every repository currently in the database (reverse-coded where a lower raw value is the stronger pattern), then the applicable dimensions are averaged and scaled to 0–100.
What it represents
One comparable figure for how much iterative, structured development activity a repository shows — not code quality or project success.
How to interpret it
It's relative to whatever repositories are currently stored, so treat it as a within-this-dataset ranking aid, not a universal benchmark.
How to use it
Compare repositories side by side, and check the four dimension scores and metric cards below for the evidence behind the number. Full explanation →

Commit activity

Feeds into: Engagement & Regularity

Commit frequency
0.08 / week
Active-day ratio
0.6%
Commit interval variability
N/A
Longest inactivity gap
0.0 days

Issue activity

Feeds into: Issue Refinement

Total issues
0
Issue closure rate
N/A
Mean comments per issue
N/A
Mean issue resolution time
N/A

Pull request activity

Feeds into: Integration

Total pull requests
0
Merge rate
N/A
Reviewed PR ratio
N/A
Mean PR cycle time
N/A

Commit activity over time

Learning Quality Indicator

100.0%

Share of classified repository text — commit messages, issues, pull requests and reviews — that reads as substantive learning-oriented language (problem identification, experimentation, reflection or refinement) rather than routine text.

How is this calculated, and why did I get this score?
Why you got this score
Of the 2 pieces of repository text analysed, 2 (100.0%) were classified as substantive learning-oriented language, most often 'refinement' (2); the remaining 0 were classified as routine text.
How it's calculated
Every classified text snippet is assigned one of five categories by a TF-IDF + classifier: Problem Identification, Experimentation, Reflection, Refinement, or None. The indicator is the percentage that fell into one of the four substantive categories.
What it represents
An estimate of how much repository text reflects learning-oriented language vs. routine/administrative text.
How to interpret it
The current classifier (Linear SVC) was trained on 100 labelled examples and measured 49.6% accuracy in cross-validation. Treat classifications as exploratory suggestions, not certainties.
How to use it
As a starting point for which artefacts might be worth reading directly — not proof that learning occurred. Full explanation →
Refinement: 22Total
Problem Identification
0% (0)
Experimentation
0% (0)
Reflection
0% (0)
Refinement
100% (2)
None
0% (0)