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.

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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

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3. Approach

Tasks are ranked using a weighted priority scoring system based on:

• Deadline urgency
• Estimated effort required
• Current student workload

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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

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Deadline Scoring

• ≤ 1 day → Score 100
• ≤ 3 days → Score 80
• ≤ 7 days → Score 60
• ≤ 14 days → Score 40

• 14 days → Score 20

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Behaviour

• Tasks due very soon receive the highest priority
• Tasks with longer deadlines receive lower scores
• Ensures students focus on urgent tasks first

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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

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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)

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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.

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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)

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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

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3. Final Workload Score

Workload Score = (0.6 × Task Pressure Score) + (0.4 × Target Grade Score)

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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

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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.

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5.2 Deadline Score

Time Remaining	Score
≤ 1 day	100
≤ 3 days	80
≤ 7 days	60
≤ 14 days	40
> 14 days	20

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5.3 Effort Score (Will be replaced with AI Effort Prediction)

Task Weighting	Score
≤ 10	30
≤ 20	50
≤ 40	70
> 40	90

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5.4 Workload Score

Workload Level	Score
Low	30
Medium	60
High	90

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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

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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

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Result

Task A is prioritised higher than Task B due to a higher overall score.

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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

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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

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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

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10. Outcome

Tasks with higher priority scores will be recommended first, helping students:

• Stay on track
• Reduce stress
• Improve task planning

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11. Dependencies

• AI-based effort prediction feature (external team)
• Task metadata (deadlines, units, etc.)
• Backend API integration

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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.