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Source MappyAI alignments are inferred — not sourced from ECLIPS. Blue cells should be verified before acting on this data.

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Mappy Intelligence

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Visualize curriculum data

Mappy is a tool designed to help educators visualize and analyze curriculum data. It provides multiple visualization options including radar plots, binary heatmaps, and cumulative heatmaps to provide a programmatic lens on assessment design and gain insights into curriculum coverage.

Mappy is fully bootstrapped — it has received no external financial support — and is currently developed by Fiacre Rougieux in his spare time.

Project Team

Project Lead

  • Fiacre Rougieux, UNSW Engineering

Core Team

1) Pedagogy

  • Diana Saragi Turnip, UNSW Medicine
  • Patsie Polly, UNSW Medicine
  • Jon Xiating Cai, UNSW Medicine
  • Priya Khanna Pathak, UNSW Medicine
  • Dhanushi Abeygunawardena, UNSW Science

2) Features

  • Robbie Keswick

3) ECLIPS data

  • Andrew Duncan

Mappy is a curriculum exploration and assurance tool. It helps map evidence, identify gaps, and support decisions about curriculum quality. That is useful. We want to clarify that it is also not enough.

In 2009, Gert Biesta argued that education systems risk "valuing what we measure" rather than "measuring what we value." The point is that measurement can end up defining what quality is. What gets counted starts to look like what counts.

In Mappy, we recognise we operate inside a system of accreditation frameworks, program reviews, and quality assurance cycles which all create demand for evidence. Mappy helps produce it. That is the job. We recognise that the evidence Mappy displays in all our visualisations — CLO coverage, PLO alignment, assessment distribution, progression logic — sits within what Biesta calls the qualification function of education. It says very little about whether a program socialises students into particular ways of knowing uncritically, or whether it creates conditions for genuine independence of thought. Those questions are harder to map. They may not be mappable at all.

So at Mappy, we want to make clear that coverage and alignment are proxies, not values. They can tell you where evidence is weak or missing. They cannot tell you whether the claims being evidenced are worth making. That judgement belongs to you, the people who design, teach, and govern curriculum. Mappy is there to make that judgement better-informed and more defensible, not to replace it.

If you want to read the argument in full:
Biesta, G. Good education in an age of measurement: on the need to reconnect with the question of purpose in education. Educ Asse Eval Acc 21, 33–46 (2009). https://doi.org/10.1007/s11092-008-9064-9

Disclaimer

THIS SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

The information provided by Mappy is for general informational and educational purposes only. Users are solely responsible for verifying all information and using the software at their own risk. The creators and contributors of Mappy make no representations or warranties regarding the accuracy, completeness, or reliability of any content.

© 2026 Mappy Intelligence. All rights reserved.

Mappy Help

Welcome to Mappy, a curriculum visualization and analysis platform designed to help educators map competencies across courses, explore live program structures, and identify coverage patterns.

1. Data Sources

ECLIPS Integration

Mappy connects directly to ECLIPS, the institutional academic graph, giving you live access to program structures, courses, CLOs, PLOs, and assessment data:

  • Search for any program or specialisation using the top search bar
  • Browse by faculty using the treemap on the home screen
  • ECLIPS data includes course relationships, prerequisite chains, and CLO-to-PLO mappings
  • Version history is available where multiple handbook years exist

Custom Data (Excel)

You can also load your own mapping spreadsheets:

  • Select Open File to import an Excel (.xlsx) file
  • Use Pre-filled to start from a template framework
  • Required columns: aspect, course_code
  • Optional columns: term, year
  • Save File to export in Excel, JSON, or CSV formats

2. Visualization Options

  • Radar Charts: Competency distribution with weighted analysis and adaptive scaling.
  • Binary Heatmaps: Presence/absence matrix showing which courses cover which competencies.
  • Cumulative Heatmaps: Progressive coverage across curriculum with adaptive margins.
  • Program Structure: Treemap and hierarchy views of containers, courses, and elective sets drawn from ECLIPS.
  • Assessment Profile: Breakdown of assessment types, weights, and security classifications per course or program.
  • Educational Taxonomies:
    • Miller's Pyramid (Clinical skills)
    • CDIO Framework (Engineering)
    • Dreyfus Model (Skill acquisition)
  • Elective Path: Progression tracking over time.

3. Editing & Custom Mappings

  • Direct Editing: Click any cell in heatmap visualizations to open the editing dialog.
  • Spreadsheet View: Use the sidebar "Edit Data" button for bulk changes and search.
  • Framework Management: Create custom competency templates and standards.
  • New Mapping: Add a custom mapping sheet to any program loaded from ECLIPS.

Remember to use Save File after editing to persist your changes.

4. Advanced Features

  • Export: Download all plots as images or generate PDF reports.
  • Settings: Customize theme, colors, and analysis thresholds via the status bar.
  • Analysis: Weighted analysis toggles to factor in assessment weightings.

© 2026 Mappy Intelligence. All rights reserved.

Key terms used throughout Mappy, explained for new users.

Visualizations

Binary Heatmap
A grid that shows yes or no: whether each course covers each competency. A filled cell means the course addresses that competency; an empty cell means it doesn't.
Cumulative Heatmap
Like the binary heatmap, but each row shows the running total of coverage up to that point in the curriculum, useful for spotting when competencies are first introduced and how they build up.
Radar Chart
A spider-web chart where each axis is a competency. The shape shows how evenly coverage is distributed: a balanced polygon means broad coverage; spikes indicate over-emphasis in a few areas.
Elective Path
A timeline visualization showing how competencies accumulate as a student moves through a sequence of elective choices.
Assessment Profile
A breakdown of assessment types and their weightings across a course or program, e.g., how much of the grade comes from exams vs. assignments.

Data & Mapping

Competency
A skill, knowledge area, or learning outcome being mapped. Each column in your spreadsheet typically represents one competency.
Framework
A structured set of competencies used as a mapping template, e.g., Bloom's Taxonomy or the UN Sustainable Development Goals.
PLO (Program Learning Outcome)
A high-level goal for the entire degree program: what graduates should know or be able to do upon completion.
CLO / SLO (Course / Subject Learning Outcome)
Specific learning goals for a single course. CLOs map upward to PLOs to show how courses contribute to program-level outcomes.
I / D / A (Introduce · Develop · Assess)
Three levels of engagement with a competency. Introduce = first exposure; Develop = practice and deepen; Assess = formally evaluate.
Aspect
A data dimension in Mappy's file format, essentially a column header representing a competency or outcome category.
Mapping
A single sheet linking one set of elements (e.g., courses) to one set of competencies. A workbook can contain multiple mappings for different frameworks.

Assessments & Program Structure

Secured (assessment type)
An assessment classified as requiring controlled conditions based on a structured decision tree (covering supervision level, permitted materials, and environment). Typical examples include invigilated exams and timed online tests under lockdown. Whether a given task qualifies as Secured depends on the decision tree: contact Mappy for more information on the decision tree.
Assessment Weight
The percentage of the final grade contributed by a single assessment task. Weights across all tasks in a course should sum to 100%.
Weighted Analysis
An analysis mode that factors in assessment weights rather than treating all tasks equally: a 60% exam counts more than a 10% quiz.
UoC (Units of Credit)
The credit-point value of a course, indicating its relative size and workload (e.g., most courses are 6 UoC; a thesis may be 24 UoC).
Container
A structural grouping in a program, e.g., a major, specialisation, or prescribed elective list. Containers can nest inside each other.
Elective
A course selected from a defined set of options (rather than a fixed requirement). Mappy can visualize how different elective combinations affect competency coverage.
Threshold
A minimum coverage level used in analysis, e.g., a competency is considered "met" only if it appears in at least 2 courses or reaches a set weighted total.

Educational Frameworks & Taxonomies

Bloom's Taxonomy
A six-level cognitive framework: Remember → Understand → Apply → Analyze → Evaluate → Create. Widely used to classify the depth of learning outcomes.
Miller's Pyramid
A four-level framework for clinical skills: Knows → Knows How → Shows How → Does. Used in health and medical education to gauge practical competence.
CDIO Framework
An engineering education framework standing for Conceive, Design, Implement, Operate, mapping how students engage with the full product lifecycle.
Dreyfus Model
A five-stage skill acquisition model: Novice → Advanced Beginner → Competent → Proficient → Expert. Useful for tracking professional skill development.
Krathwohl's Affective Domain
A taxonomy for attitudinal and emotional learning: Receiving → Responding → Valuing → Organizing → Characterizing. Complements Bloom's cognitive domain.
Fink's Taxonomy
Six interrelated dimensions of significant learning: Foundational Knowledge, Application, Integration, Human Dimension, Caring, and Learning How to Learn.

© 2026 Mappy Intelligence. All rights reserved.

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Example Template

Load a sample curriculum map with pre-populated data

Open File

Select a local Excel file from your computer

Walkthrough

Take a quick tour of the features

Program Browser

Select a program to load from the cloud

New Mapping

Source items will be loaded automatically.
Target items will be loaded automatically.

Export Report (Beta)

Impact Analysis

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