Merge pull request #178 from rashi-agrawal29/feature/task_prioritisation-recommender

Design Document for Task prioritization recommender

Author: SteveDala

src/content/docs/Products/OnTrack/Projects/Task Prioritization Recommender/task_prioritisation_recommender.md

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+---
+title: Task Prioritization Recommender Feature
+---
+
+## 1. Introduction
+
+The Task Prioritization feature is designed to help students identify which tasks to focus on next
+within OnTrack. The system evaluates multiple factors to generate a priority score for each task,
+enabling students to manage their workload more effectively and reduce the risk of missed deadlines.
+
+---
+
+## 2. Purpose
+
+The purpose of this feature is to:
+
+- Recommend tasks based on urgency and workload
+- Support better time management
+- Integrate with AI-based effort prediction for smarter prioritisation
+
+---
+
+## 3. Approach
+
+Tasks are ranked using a **weighted priority scoring system** based on:
+
+- Deadline urgency
+- Estimated effort required
+- Current student workload
+
+---
+
+## 4. Prioritization Logic
+
+### 4.1 Deadline Urgency
+
+Tasks with closer due dates receive higher priority. The system calculates urgency based on the
+number of days remaining until the task due date.
+
+#### Deadline Calculation Logic
+
+1. Retrieve the task due date from the task definition
+2. Calculate the number of days remaining:
+
+   **days_left = due_date − current_date**
+   - The current date is obtained using `Time.zone.today` to ensure timezone consistency
+   - If no due date is available, the score defaults to **0**
+
+---
+
+#### Deadline Scoring
+
+- ≤ 1 day → Score 100
+- ≤ 3 days → Score 80
+- ≤ 7 days → Score 60
+- ≤ 14 days → Score 40
+- > 14 days → Score 20
+
+---
+
+#### Behaviour
+
+- Tasks due very soon receive the highest priority
+- Tasks with longer deadlines receive lower scores
+- Ensures students focus on urgent tasks first
+
+---
+
+### 4.2 Estimated Effort
+
+Tasks requiring more effort are prioritised earlier to allow sufficient time for completion.
+
+Currently, effort is approximated using **task weighting** as a temporary measure.  
+This will be replaced by the AI-based effort prediction feature in future.
+
+#### Effort Scoring (Current Implementation)
+
+- Weighting ≤ 10 → Score 30
+- Weighting ≤ 20 → Score 50
+- Weighting ≤ 40 → Score 70
+- Weighting > 40 → Score 90
+
+---
+
+### 4.3 Workload
+
+Tasks are prioritised higher when a student has multiple competing tasks across their enrolled
+units.
+
+Workload is determined by a combination of:
+
+- **Number of incomplete tasks** across all active units (task pressure)
+- **Student target grade** (academic ambition)
+
+---
+
+#### Target Grade Values
+
+Each project (unit) has a target grade represented numerically:
+
+- 0 → Pass
+- 1 → Credit
+- 2 → Distinction
+- 3 → High Distinction
+
+The workload calculation uses the **average target grade** across all enrolled units to reflect the
+student’s overall academic goal.
+
+---
+
+#### Workload Calculation Logic
+
+**1. Task Pressure Score (0–100)**  
+Based on the number of incomplete tasks:
+
+- 0–4 tasks → Score 30 (Low)
+- 5–9 tasks → Score 60 (Medium)
+- 10+ tasks → Score 90 (High)
+
+---
+
+**2. Target Grade Score (0–100)**  
+Based on the student’s average target grade:
+
+- High Distinction (3) → Score 90
+- Distinction (2) → Score 75
+- Credit (1) → Score 60
+- Pass (0) → Score 40
+
+---
+
+**3. Final Workload Score**
+
+**Workload Score = (0.6 × Task Pressure Score) + (0.4 × Target Grade Score)**
+
+---
+
+#### Behaviour
+
+- Students with more incomplete tasks receive higher workload scores
+- Students aiming for higher grades receive higher prioritisation sensitivity
+- Ensures personalised recommendations based on both workload and ambition
+
+---
+
+## 5. Scoring Model
+
+### 5.1 Priority Score Formula
+
+**Priority Score = (0.5 × Deadline Score) + (0.3 × Effort Score) + (0.2 × Workload Score)**
+
+Each factor is converted into a score between **0–100**.
+
+---
+
+### 5.2 Deadline Score
+
+| Time Remaining | Score |
+| -------------- | ----- |
+| ≤ 1 day        | 100   |
+| ≤ 3 days       | 80    |
+| ≤ 7 days       | 60    |
+| ≤ 14 days      | 40    |
+| > 14 days      | 20    |
+
+---
+
+### 5.3 Effort Score (Will be replaced with AI Effort Prediction)
+
+| Task Weighting | Score |
+| -------------- | ----- |
+| ≤ 10           | 30    |
+| ≤ 20           | 50    |
+| ≤ 40           | 70    |
+| > 40           | 90    |
+
+---
+
+### 5.4 Workload Score
+
+| Workload Level | Score |
+| -------------- | ----- |
+| Low            | 30    |
+| Medium         | 60    |
+| High           | 90    |
+
+---
+
+## 6. Example Calculation
+
+### Task A
+
+- Due in 2 days → Deadline Score = 80
+- Effort = 8 hours → Effort Score = 70
+- Workload = Medium → Workload Score = 60  
+  Priority Score = (0.5 × 80) + (0.3 × 70) + (0.2 × 60) = 40 + 21 + 12 = 73
+
+---
+
+### Task B
+
+- Due in 10 days → Deadline Score = 40
+- Effort = 2 hours → Effort Score = 30
+- Workload = Low → Workload Score = 30
+
+Priority Score = (0.5 × 40) + (0.3 × 30) + (0.2 × 30) = 20 + 9 + 6 = 35
+
+---
+
+### Result
+
+Task A is prioritised higher than Task B due to a higher overall score.
+
+---
+
+## 7. System Flow
+
+1. Retrieve all tasks for the student
+2. Calculate:
+   - Deadline score
+   - Effort score (from AI prediction)
+   - Workload score
+3. Compute total priority score
+4. Rank tasks in descending order
+5. Recommend highest priority tasks to the user
+
+---
+
+## 8. Integration
+
+- Effort scores will be derived from the **AI-Based Effort Prediction feature**
+- The system will consume predicted effort (in hours) and map it to scoring ranges
+- Designed to integrate seamlessly with backend APIs
+
+---
+
+## 9. Design Rationale
+
+- **Deadline urgency (50%)**  
+  Highest weight to reduce missed submissions
+
+- **Effort (30%)**  
+  Encourages early start on complex tasks
+
+- **Workload (20%)**  
+  Balances tasks across multiple units using task pressure and academic ambition
+
+---
+
+## 10. Outcome
+
+Tasks with higher priority scores will be recommended first, helping students:
+
+- Stay on track
+- Reduce stress
+- Improve task planning
+
+---
+
+## 11. Dependencies
+
+- AI-based effort prediction feature (external team)
+- Task metadata (deadlines, units, etc.)
+- Backend API integration
+
+---
+
+## 12. Conclusion
+
+The Task Prioritization feature enhances OnTrack by providing a structured and intelligent way to
+manage student tasks. By combining urgency, effort, and workload, the system delivers meaningful
+recommendations that improve productivity and academic outcomes.