Fair Efforts, Fair Points, Fair Penalties

[adityabmv]

This document serves as the initial draft of a structured summary capturing key insights from a two-hour conversation with GPT-o3-mini-high.

The content will be updated in-place in this discussion.

Table of Contents

1. Introduction
2. Core Performance Metrics
3. Bonus Jump Mechanism for Underperforming Students
4. Contribution and Review System
   • 4.1 Question Submission and Genuinity Score
   • 4.2 Peer Review Process and K-Limit
5. Understanding Submissions and Revalidation Process
6. Overall Operational Flow
7. Implementation Considerations
8. Conclusion

---

1. Introduction

The designed points system aims to:

• Reward high performance on quizzes (accuracy, speed, and confidence).
• Encourage genuine contributions (questions and reviews) through bonus incentives.
• Penalize low-quality or wasteful inputs.
• Provide a fair revalidation mechanism for contested penalties.
• Foster a competitive, yet supportive, learning environment.

This system leverages automated quality checks (via LLMs), peer feedback, and dynamic scoring formulas to adjust student points based on both quiz performance and contribution quality.

---

2. Core Performance Metrics

Each quiz attempt or question submission is evaluated using multiple performance metrics. The core metrics include:

• Accuracy: Percentage of correct answers.
• Time Taken: Overall quiz time or per-question response times.
• Precision: (If applicable) Closeness of an answer to the correct answer.
• Streaks: The number of consecutive correct answers.
• Hint Usage: A boolean or counter indicating if hints were used.
• Confidence Rating: Self-reported confidence (e.g., on a 1–5 scale) per question.

Scoring Algorithm Example

A generalized formula for calculating the score for a question might be:

$$\text{score} = \text{basePoints} + (\text{confidenceFactor} \times \text{confidenceScore}) - (\text{hintPenalty} \times \text{hintUsed}) + (\text{streakBonus} \times \text{currentStreak}) - (\text{attemptPenalty} \times (\text{attempts} - 1)) + (\text{timeBonus} \times (\text{idealTime} - \text{timeTaken}))$$

Notes:

• basePoints: Initial points awarded for a correct answer.
• Confidence Multiplier: Rewards accurate self-assessment.
• Time Bonus: Incentivizes quick, yet thoughtful responses.
• Streak Bonus: Rewards consistency and builds competitive momentum.

---

3. Bonus Jump Mechanism for Underperforming Students

Students scoring below the 75th percentile on overall quiz performance are eligible for a “bonus jump” to help them catch up.

Mechanism Details

1. Eligibility:
   Students whose cumulative score falls below the 75th percentile.

2. Bonus Jump Cost:
   When a student opts for a bonus jump, 25% of their existing points is deducted immediately.

3. Exam Performance Bonus:
   After the exam, if a student scores more than 50% (denoted as ( x% )), they receive additional bonus points calculated as:

   $$\text{bonus} = x \times \left(0.8 \times \left(\text{topRankerMarks} - \text{currentStudentMarks}\right)\right)$$
   • x: The exam score percentage (if ( x > 50% )).
   • topRankerMarks: Marks obtained by the highest scoring student.
   • currentStudentMarks: Marks of the student in question.

Outcome

• Students below 75th percentile incur a temporary points deduction.
• Successful exam performance (i.e., ( x > 50% )) results in a bonus that helps close the gap with top performers, balancing risk with reward.

---

4. Contribution and Review System

To encourage quality contributions and genuine reviews, the system supports student-submitted questions and peer reviews. This section details how contributions are scored, reviewed, and rewarded.

4.1 Question Submission and Genuinity Score

Process:

• Submission:
  A student contributes a new quiz question. Each question submission includes:

  • Question Text
  • Contributor ID
  • Metadata: e.g., subject, difficulty level
  • Status: initially set to "pending"
  • LLM Sanity Check: An LLM evaluates the submission for relevance and clarity, assigning an initial quality score.

• Genuinity Score Calculation:
  After the initial LLM check, the question is open for peer reviews. The genuinity score ( G ) is computed as an average rating from the first ( K ) reviews:

  $G = \frac{\sum_{i=1}^{K} R_i}{K}$

  Where $$( R_i )$$ is the rating (e.g., on a 1–5 scale) provided by the $\( i^{th} \)$ reviewer.

• Bonus Award:
  If ( G ) exceeds a predetermined threshold ( T ), the contributor receives bonus points. Additional points can be awarded later based on the question's “success rate” (i.e., how well students perform when answering the question).

4.2 Peer Review Process and K-Limit

Key Aspects:

• Review Submission:
  Students review contributed questions by providing a genuinity rating and feedback.

• K-Limit:
  Only the first ( K ) reviews count towards the bonus and penalty calculations to prevent over-reviewing. Reviews beyond this limit are recorded for transparency but do not impact the bonus calculation.

• Reviewer Bonuses:
  Reviewers whose ratings align well with the aggregated score can earn bonus points. A discrepancy (or consistently outlier ratings) may trigger a penalty to discourage misuse.

---

5. Understanding Submissions and Revalidation Process

In addition to answering quiz questions, students are required to submit an "understanding" text—a plain text or Markdown explanation of what they learned.

Quality Assessment and Penalty

• Automated Evaluation:
  An LLM evaluates the submission and assigns a quality score.
  • Above Threshold: Student earns bonus marks.
  • Below Threshold: A penalty of 30% decrease in points is applied to the student’s overall score.

Revalidation (Contestation) Process

If a student believes the penalty was unjustified, they can contest via a revalidation process:

1. Staking Points:
   The student must stake 50% of their current points to initiate revalidation.

2. Reevaluation:
   The submitted text is re-evaluated using either a secondary LLM or an alternative scoring algorithm. Peer validation can also be incorporated.

3. Outcome and Formula:

   • If Revalidation Is Positive:

     • The student’s staked points (50%) are refunded.
     • The student receives the points for a high-quality submission.
     • Additionally, a bonus of ( k% ) of their current points is awarded.

   • If Revalidation Is Negative:

     • The staked points are forfeited.
     • The original penalty remains in place.

Summary Formula for Revalidation Outcome

• Refund/Bonus Calculation (if approved):

  $$\text{Final Bonus} = \text{Original Bonus} + \left( k\% \times \text{Current Points} \right)$$

• Penalty Remains if Revalidation Fails:
  The 30% penalty stands and the staked points are lost.

---

6. Overall Operational Flow

A. Quiz Attempt and Scoring Flow

1. User takes the quiz.
2. System records:
   • Accuracy
   • Time Taken
   • Confidence, Streaks, Hint Usage, etc.
3. Score Calculation:
   Apply the scoring algorithm and update the student’s cumulative points.
4. Post-Quiz Bonus Jump:
   Students below the 75th percentile can opt for a bonus jump.

   • 25% of points are deducted.

   • Exam score ( x% ) is used to calculate the bonus:

     $$\text{bonus} = x \times \left(0.8 \times \left(\text{topRankerMarks} - \text{currentStudentMarks}\right)\right)$$

B. Question Contribution Flow

1. Submission:
   Student submits a question.
2. Automated LLM Sanity Check:
   The question is preliminarily evaluated.
3. Peer Review:
   First ( K ) reviews are gathered and aggregated to form the genuinity score ( G ).
4. Bonus/Penalty:
   • If ( G \geq T ), bonus points are awarded.
   • Low-quality questions incur penalties.

C. Understanding Submission Flow

1. Submission:
   Student writes an understanding text in plain text/Markdown.
2. Automated Evaluation:
   LLM scores the quality.
3. Immediate Outcome:
   • If quality meets threshold → Award bonus.
   • If quality is low → Apply a 30% penalty.
4. Revalidation (Optional):
   • Student stakes 50% of current points to contest the penalty.
   • Re-evaluation occurs.
   • Positive Outcome: Refund stake, award bonus, and add ( k% ) bonus.
   • Negative Outcome: Forfeit stake; penalty stands.

---

7. Implementation Considerations

A. Quality and Reputation Mechanisms

• LLM-Based Quality Checks:
  Automated models score each submission and review for clarity, relevance, and correctness.

• Reputation Score:
  Each student accumulates a reputation based on:

  • Quiz performance.
  • Quality of contributed questions.
  • Quality of peer reviews.
  • Quality of understanding submissions.

  This reputation may influence how much weight their reviews carry and eligibility for bonus multipliers.

• Feedback Loop:
  Detailed score breakdowns and quality metrics are provided to students for continuous improvement.

---

8. Conclusion

This comprehensive points system integrates multiple dimensions of student performance and engagement:

• Quiz performance is measured using accuracy, time, confidence, streaks, and more.
• Bonus jumps provide opportunities for students below the 75th percentile, with exam performance influencing recovery.
• Contributions and reviews are incentivized via automated LLM checks and peer validation, with a capped (K) number of reviews ensuring quality control.
• Understanding submissions encourage reflective learning and include an automated penalty/revalidation process to maintain high standards.

Together, these components foster a competitive yet fair environment where high performance, genuine contributions, and quality reviews are rewarded, while low-quality inputs incur meaningful penalties. This automated, multi-layered system minimizes the need for manual teacher oversight while ensuring that only relevant and high-quality content drives student progress.

[lasya2007]

Hey! I have a question and a suggestion about the self reported confidence score mechanism. From what I understand, it currently adds to the score when the answer is correct and confidence is high, but doesn’t seem to penalize wrong answers with high confidence.

If that’s the case, wouldn’t it be optimal for students to just enter confidence = 5 on every question? If the answer is right, they gain more; if it’s wrong, there’s no impact, so there’s no incentive to report confidence honestly.

Maybe it could help to penalize high confidence on wrong answers, so students have a reason to be accurate in self-assessment. That way, confidence becomes a meaningful metric.

Thank you!

[adityabmv]

Sure, thanks for pointing that out.

[lasya2007]

Hi everyone,
After exploring some research papers and reflecting on personal experiences, I wanted to suggest a few gamification ideas that could enrich the learning experience. These ideas aim to keep students engaged, motivated, and progressing.

1. PvP Quizzes or Team-based Quizzes with ELO Rating System
ELO is a performance-based rating system, where your score changes depending on the strength of the opponent you face. This can be applied to education via PvP (Player vs Player) challenges, where students can challenge each other to short quizzes (e.g., 3 or 5 questions) and receive an ELO-based rating update. If you beat a stronger opponent, you gain more. If you lose to a weaker one, your rating drops more significantly. This keeps the competition meaningful and dynamic.

We could also explore team-based challenges, where ELO scores shift based on how well a team performs against stronger or weaker teams. It’s fun, interactive, and adds friendly competition without too much pressure.

2. Badges and Achievements
Badges can be awarded for reaching specific milestones, such as performing well in multiple subjects, watching lectures consistently, or submitting quiz questions that pass the "genuineness" threshold.

These can serve as visual markers of effort and engagement, providing encouragement and recognition. However, in my view, these should not influence rankings or scores. A student trying to make a comeback shouldn’t be at a disadvantage because someone else accumulated badges earlier. These should be motivational and celebratory, not a metric for academic performance.

3. Tiered Leaderboards
Instead of a single leaderboard where the top feels too high, we could divide students into tiers based on performance like Bronze, Silver, Gold, and so on. Each tier would have its own leaderboard and rewards for the top students within that group. Promotions and demotions could happen based on performance over time.
This creates reachable, psychologically healthy goals for everyone. Students in lower tiers wouldn’t feel overwhelmed or demotivated by unreachable scores at the top of a single leaderboard. They could still aim to top their current league and gradually move up.

4. Seasons and Time-Limited Events
Just like many games, we can introduce seasons or special event periods. For instance, “Exam Season” could come with extra rewards, unique challenges, or limited-edition badges. It adds urgency and freshness to the learning experience. These limited-time events help break the monotony and offer new opportunities for students to engage, compete, or even collaborate differently every few weeks.

5. Unlockable Content (Leveling Up Through Progress)
We can introduce unlockable content as a reward for progress. For example, after completing a certain module or task, students could gain access to new question sets, special video lectures, bonus case studies or challenges. This mimics a “leveling up” experience, adding a sense of reward and motivation that can help students feel accomplished at each stage of their journey.

6. Virtual Currencies
Another gamification element that can boost engagement is the use of virtual currencies that students earn through learning tasks. These can be accumulated and then exchanged for rewards, which could be:

• Cosmetic rewards like avatars, profile frames, backgrounds, or visual upgrades in their interface,
• Learning unlockables like bonus content, special quizzes, or extra attempts on a quiz,
• Or even tangible rewards, depending on the course setup (e.g., merchandise, coupons, certificates).

7. Storylines and Progression Narratives
Instead of treating learning as a flat series of tasks, we could present it as a progression narrative, say, a journey from beginner to expert. For instance, levels like Novice to Intermediate to Advanced to Scholar, with accompanying visuals and feedback, can make the student’s growth more tangible. It gives a sense of identity and forward movement.

8. Learning Modes
Different students are motivated in different ways. Some enjoy competition, while others thrive in collaborative or self-paced learning environments. Giving students the option to choose between competitive, collaborative, or solo learning modes can make the experience more inclusive and adaptive.
For example, students could choose to:

• Join a team challenge,
• Work alone through personalized tasks, or
• Enter into competitive PvP quizzes.
  This avoids the “one-size-fits-all” issue and makes learning feel more personally aligned.

9. Freedom of Choice (Agency in Learning)
Such freedoms add a sense of ownership and autonomy, which increases intrinsic motivation. Some suggested examples:

• Letting students choose the type of task (essay, group project, individual project etc.),
• Choosing the order in which they complete tasks,
• Voting on specific rules (like mark deductions for non-performing team members),
• Customizing assignment deadlines within a reasonable range,
• Selecting which skills to focus on developing.

10. Freedom to Fail
This concept involves allowing students to fail safely, encouraging experimentation and resilience:

• Let students retake quizzes or tasks,
• Reduce penalties for first-time failures,
• Encourage “learning through failure” instead of punishing it.
  This is slightly controversial and might need moderation, but it can greatly reduce anxiety and build a growth mindset among students who struggle early on.

11. Fostering Collaboration
Learning doesn’t have to be a solo journey. Collaborative projects like team-based assignments, joint quizzes, or discussion-based learning, can build social support and engagement. Gamifying collaboration (example: guilds, shared rewards, group badges, co-op challenges) could further boost involvement.

12. Gamified Learning Activities
These are immersive experiences designed to simulate real-world decision-making, storytelling, or exploration. These are structured like games but built around pedagogical goals.
Some examples could include:

• Story-driven quests, where learners solve subject-specific challenges embedded in a narrative (e.g., solving logic problems to unlock a clue in a mystery storyline),
• Roleplay or simulation-based activities, like taking on different roles in a mock court case, business negotiation, or historical event,
• Scenario-based decision games, where students make choices, reflect on consequences, and learn through iterative feedback.
  These activities help students apply what they’ve learned in context, promoting deeper understanding and critical thinking. They're especially effective for building engagement, collaboration, and long-term retention.

These are some ideas I’ve compiled and thought through based on research and conversations. The larger goal is to keep the system motivating, inclusive, and exciting, while still supporting different kinds of learners at their own pace.

Would love to hear your thoughts, and happy to refine these further! Thank you

References

• Buckley, P., Doyle, E., & Doyle, S. (2017). Game On! Students’ Perceptions of Gamified Learning. Journal of Educational Technology & Society, 20(3), 1–10. http://www.jstor.org/stable/26196115
• Darina Dicheva, Christo Dichev, Gennady Agre, & Galia Angelova. (2015). Gamification in Education: A Systematic Mapping Study. Journal of Educational Technology & Society, 18(3), 75–88. http://www.jstor.org/stable/jeductechsoci.18.3.75
• Huang, B., & Hew, K. F. (2021). Using Gamification to Design Courses: Lessons Learned in a Three-year Design-based Study. Educational Technology & Society, 24(1), 44–63. https://www.jstor.org/stable/26977856
• Jen-Wei Chang, & Hung-Yu Wei. (2016). Exploring Engaging Gamification Mechanics in Massive Online Open Courses. Journal of Educational Technology & Society, 19(2), 177–203. http://www.jstor.org/stable/jeductechsoci.19.2.177
• Toda, A. M., Palomino, P. T., Oliveira, W., Rodrigues, L., Klock, A. C. T., Gasparini, I., Cristea, A. I., & Isotani, S. (2019). How to Gamify Learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education. Journal of Educational Technology & Society, 22(3), 47–60. https://www.jstor.org/stable/26896709

[insaneonai]

Thank you @lasya2007 for your valuable contribution! We're carefully reviewing your ideas and work, and will be integrating those that best align with the project's goals.

[insaneonai]

Points Scoring System — Discussion

---

1. Initial Data Structure

We start with a data structure representing a quiz attempt by a user:

{
  "_id": "string",
  "userId": "string" | "ObjectId",
  "QuizId": "string" | "ObjectId",
  "grades": [
    {
      "questionId": "string" | "ObjectId",
      "ConfidenceScore": "number (1 to 5)",
      "result": "boolean (true/false)"
    }
  ],
  "basepoints (best of all attempts)": "number",
  "streaks": "number",
  "timeTaken": "number (seconds)",
  "idealTime": "number (seconds)",
  "attemptCount": "number",
  "hintcount": "number"
}

Example

{
  "_id": "68511a120d4fe9ff703a6d8c",
  "userId": "68511a120d4fe9ff703a6d8a",
  "QuizId": "68511a120d4fe9ff703a6d8b",
  "grades": [
    {"questionId": "q1", "ConfidenceScore": 4, "result": true},
    {"questionId": "q2", "ConfidenceScore": 2, "result": false},
    {"questionId": "q3", "ConfidenceScore": 5, "result": true},
    {"questionId": "q4", "ConfidenceScore": 3, "result": true},
    {"questionId": "q5", "ConfidenceScore": 1, "result": false}
  ],
  "basepoints (best of all attempts)": 85,
  "streaks": 2,
  "timeTaken": 198,
  "idealTime": 150,
  "attemptCount": 1,
  "hintcount": 2
}

GamificationMetrics:

Gamification metrics are measurable elements that form the foundation of the application's gamification system, helping to quantify and drive user engagement.

GamificationMetrics	Description
Points	A reward metric - basis for other gamification tasks

2. Categorizing Confidence Levels

• High confidence: ConfidenceScore ≥ 3

• Low confidence: ConfidenceScore < 3

For the example payload we will have something as bellow

questionId	ConfidenceScore	Confidence Category	result
q1	4	High	true
q2	2	Low	false
q3	5	High	true
q4	3	High	true
q5	1	Low	false

3. CrossTabulation of confidence Scores and Results

We construct a cross-tabulation to capture all four possible combinations of confidence and correctness. This allows us to assign appropriate weights to each case during scoring.

Confidence \ Result	Correct (true)	Wrong (false)	Total
High (≥ 3)	3	0	3
Low (< 3)	0	2	2

4. Penalty and Incentives covering all 4 cases & other factors.

Variable Name	Description	Suggested Value
high_weight	Weight for high confidence answers	2
low_weight	Weight for low confidence answers	1
hint_penalty	Penalty per hint used	-0.5
streak_bonus	Bonus per streak	3
time_weight	Bonus w.r.t time factor	0.1 to 0.3
attempt_penalty	Penalty for no.of attempts	-0.5 to -1

5. Compute Confidence Score

The individual weight factor can be visualized as below:

Confidence \ Result	Correct (true)	Wrong (false)
High (≥ 3)	high_weight	-high_weight
Low (< 3)	low_weight	-low_weight

To calculate the confidenceScore, use a weighted formula based on how well the user's confidence aligns with correctness:

confidenceScore = (count_high_correct × high_weight) - (count_high_wrong × high_weight) + (count_low_correct × low_weight) - (count_low_wrong × low_weight)

6. Calculate Total Hint Penalty

Discourages over-reliance on hints while still allowing their strategic use.

TotalHintPenalty = hint_penalty * hintcount;

Note: hint_penalty should be negative.

7. Calculate Streak Bonus

Encourages consistent performance by rewarding users for maintaining streaks of correct answers.

StreakBonusTotal = streak_bonus * streaks;

8. TimeBonus

timeBonus = (idealTime - timeTaken) × time_weight

• Bonus if the time consumed is relatively smaller than ideal time.
• Penalize if the time consumed is relatively larger than ideal time.

In other words:

• Positive if timeTaken < idealTime (faster)
• Negative if timeTaken > idealTime (slower)

9. AttemptPenalty

We want to encourage learning that requires multiple attempts while still applying a mild penalty for repeated tries, so the weighting should be gentle.

TotalAttemptPenalty = attempt_penalty * (attemptCount - 1)

10. Final Score

TotalScore = basepoints + confidenceScore + TotalHintPenalty + StreakBonusTotal + timeBonus + TotalAttemptPenalty;

11. Increment the Global Points;

Points += TotalScore

One issue with simply updating points as above is users gain huge increases in points just by attempting which we should prevent thus we can:

11.a) (Approach 1)

// Approach 1: Base Points Adjustment + Bonus

// First, get current values
currentPoints = user.points
basePointsOld = user.basePoints
basePointsNew = user.newBasePoints  // New base points after attempt
bonus = calculateBonus(user)  // Confidence, streak, and time bonuses

// If it's the first attempt
if (user.attemptCount == 1) {
    user.points = basePointsOld + bonus
} 
else {
    // If new base points are greater than old
    if (basePointsNew > basePointsOld) {
        improvement = basePointsNew - basePointsOld
    
    // Optional: If improvement is significant enough
    if (improvement >= THRESHOLD) {
        user.points = currentPoints + improvement + bonus
    } else {
        // Apply scaled improvement if below threshold
        scaledImprovement = improvement * SCALING_FACTOR
        user.points = currentPoints + scaledImprovement + bonus
    }
} 
else {
    // No improvement in base points, just add bonus
    user.points = currentPoints + bonus
    }
}

// Update attempt count
user.attemptCount += 1

return user.points

In Approach 1, we calculate the score using the full base points from the first attempt. For subsequent attempts, we adjust the base points based on the user's performance, ensuring that only meaningful improvements are rewarded.

Apprach 1 is a bit complex as it demands the previous attempt baseScore to be maintained in the DB, but rewards actual growth.

11.b) (Approach 2)

// Scenario 2: Simple Bonus Addition

// First, get current values
currentPoints = user.points
bonus = calculateBonus(user)  // Confidence, streak, and time bonuses

// If it's the first attempt
if (user.attemptCount == 1) {
    user.points = user.basePoints + bonus
} 
else {
    // Just add bonus for subsequent attempts
user.points = currentPoints + bonus
}

// Update attempt count
user.attemptCount += 1

return user.points

Approach 2 is relatively simple but Misses Opportunity for Growth Reward.

Note: This markdown outlines the step-by-step process for calculating a student’s Points based solely on their quiz performance.

Thus, the endpoint performing the above logic is hit every time a user attempts and completes a quiz

We also want to maintain a collection named GameElements whose documents are unique for every individual student

{
_id: ObjectId,
userId: ObjectId,
points: number ## Game Element.
badges: badgeIds[]
...}

[Simhadri123]

League System Implementation for Vibe

I am thinking of two approaches to implement a league system in Vibe: a Competitive League model (rankings-based) and a Non-Competitive League model (points-threshold based). Both systems are based on points fairly awarded to students.

---

Overview

Leagues group students into tiers. Two implementation styles:

• Competitive Leagues: Students are ranked within each league by weekly performance and move up or down based on rank and thresholds.
• Non-Competitive Leagues: Students are placed into leagues based on fixed total point thresholds without direct comparisons.

Use Competitive for gamified, head-to-head competition. Use Non-Competitive for goal-oriented progress tracking where every student advances by achieving milestones.

---

Approach 1: Competitive Leagues (Uses a leaderboard)

Data Model

const Student = {
  _id: ObjectId,
  name: String,
  league: String, // 'Bronze', 'Silver', etc.
  weeklyPts: Number,
  attempts: Number,
  totalPts: Number
};

Performance Metrics

• weeklyPts: Points earned in the current week.
• attempts: Number of attempts made in the week.
• totalPts: Cumulative points earned.

Ranking & Tie-Breaking

1. Sort students in each league by:
   • weeklyPts (descending)
   • attempts (ascending)
2. Assign ranks from 1 to LeagueSize.
3. Tie-Blocks: If students have identical metrics, use totalPts as a tie-breaker. If still tied, use additional criteria (e.g., earliest registration).

Promotion & Demotion

League	Promotion Threshold (WeeklyPts)	Demotion Threshold (WeeklyPts)
Bronze	100	20
Silver	200	50
Gold	350	100
Platinum	500	150

• Promotion: Students in the top P ranks who also meet or exceed the promotion threshold for weekly points are promoted.
• Demotion: Any student whose weekly points fall below the demotion threshold is demoted, regardless of rank. League protection can be given for one extra week; if the score does not improve, the student is demoted.

Automation & Scheduling

• Scheduler: Weekly job (e.g., cron at Sunday 23:59 IST).
• Steps: Compute scores → assign leagues → notify students.
• Monitoring: Alerts on league size drift, tie-block frequency.

Reward Structures

League	Badge & Flair	Recognition & Rewards
Bronze	Bronze Badge	Entry level, no rewards
Silver	Silver Badge	+25% bonus on quizzes
Gold	Gold Badge	+50% bonus on quizzes, no penalty on hints (limited)
Platinum	Platinum Trophy	+100% bonus on quizzes, no total penalty in quizzes (limited)

• Badges are awarded upon reaching a league.
• Rewards are refreshed after each league cycle.

Top-Spot Perks

Weekly #1 in each league chooses one perk:

• Challenge Token: Double-points on a selected quiz.
• No penalty Token: No penalty on a selected quiz.
• Interaction: A chance to interact with a professor.

---

Approach 2: Non-Competitive Leagues

Data Model

const Student = {
  _id: ObjectId,
  name: String,
  totalPts: Number,
  league: String // 'Bronze', 'Silver', etc.
};

Points Thresholds

League	TotalPts Range
Bronze	0–500
Silver	501–1500
Gold	1501–3000
Platinum	3001+

Assignment Logic

1. Compute each student’s totalPts.
2. Assign league based on which total point range they fall into.
3. No direct comparison or sorting; progression is goal-based.
4. No demotion: Once a student ascends to a league, they cannot be demoted.

Reward Structures

League	Badge & Flair	Recognition & Rewards
Bronze	Bronze Badge	Entry level, no rewards
Silver	Silver Badge	+25% bonus on quizzes
Gold	Gold Badge	+50% bonus on quizzes, no penalty on hints (limited)
Platinum	Platinum Trophy	+100% bonus on quizzes, no total penalty in quizzes (limited)

• Badges are awarded upon reaching a league.
• Rewards are refreshed after each league cycle.

---

Sample Code

Competitive League: Promotion & Demotion

function assignRanksAndPromotions(students, leagueSize, P, promotionThreshold, demotionThreshold) {
  // Sort by weeklyPts DESC, attempts ASC
  students.sort((a, b) => {
    if (b.weeklyPts !== a.weeklyPts) return b.weeklyPts - a.weeklyPts;
    return a.attempts - b.attempts;
  });

  // Assign ranks
  students.forEach((s, i) => s.rank = i + 1);

  // Promotion: Top P and above threshold
  const promoted = students.filter(
    s => s.rank <= P && s.weeklyPts >= promotionThreshold
  );

  // Demotion: Below demotion threshold
  const demoted = students.filter(
    s => s.weeklyPts < demotionThreshold
  );

  return { promoted, demoted, ranked: students };
}

Non-Competitive League: League Assignment

function assignLeague(student) {
  const pts = student.totalPts;
  if (pts <= 500) return 'Bronze';
  if (pts <= 1500) return 'Silver';
  if (pts <= 3000) return 'Gold';
  return 'Platinum';
}

---

The non-competitive approach is computationally efficient, as league assignments are determined solely by individual user data and do not require comparisons across the user base. In contrast, the competitive approach necessitates processing all users' data to generate leaderboards and determine rankings, resulting in higher computational overhead.

Which approach would you prefer to approve for implementation?

[insaneonai]

@Simhadri123 The competitive model looks good as it promotes fairness by naturally moving top learners to better leagues.

I think it will also be better if we make it harder to earn points as they progress through the leagues.

[NikeshReddy7787]

Bonus Jump Mechanism for Underperforming Students

Students scoring below the 75th percentile on overall quiz performance are eligible for a “bonus jump” to help them catch up. This high‐risk, high‐reward system encourages strategic play and offers a second-chance opportunity.

---

3.1 Data Model & Persistence

BonusJump attempt collection data structure

{
  _id: ObjectId,
  userId: ObjectId,
  optedInAt: ISODate,
  pointsStaked: Number,
  quizIndex: Number,                  // The quiz where bonus jump is taken
  status: "pending" | "passed" | "failed",
  scorePercent: Number | null,
  bonusAwarded: Number | null,
  feedback: String | null,         // E.g., “Improved accuracy by 7%”, etc.

}

3.2 Challenges and Design Safeguards

Problem / Exploit	Manifestation	Mitigation Strategy
Panic-driven attempt	Students may join out of fear, with no stratedy and preparation, just to increase their points	• Show a breifing regarding losses (losing of 25% points if you lose) of potential gain/loss based on past scores • Warn the user by displaying the topics that are to be prepared to take up this and get succeeded
Demotivation after failed jump	A flat 25% loss feels punitive, reduces his/her confidence	• Provide partial feedback (“You improved speed by 10%”) • Award a small guaranteed XP “consolation” (or other 2 deg metric)
Chasing bonus jumps instead of learning	Focusing on bonus at cost of core quiz performance (always risking)	• Clearly separate core and bonus scores visually • Limit bonus value to non-dominating level (e.g., 15% cap of total score) • See his history of bonus attempts and warn him his continuous risk of points
Unattainable topRanker benchmark	Bonus seems too large if topRankerMarks far ahead	• Offer alternate benchmarks (median or mean of top K performers in leaderboard) • Introduce tiered bonus multipliers(decided once league based leaderboard is decided)
Confusion about eligibility per quiz	Users unsure why bonus quest showed up or disappeared	• Add tooltip or message: “You are eligible this quiz due to past score < 75 percentile”

---

3.3 Flow

1. After each quiz (e.g., quiz 4), system evaluates cumulative score till that quiz.
2. If score is below the 75th percentile, Bonus Jump unlocks in quiz 5.
3. On opt-in:
   • Deduct 25% of current points.
   • Show potential bonus gain range.
4. Attempt bonus exam during quiz 5.
5. Evaluate bonus exam:
   • If scorePercent > 50%, award bonus using:

     bonus = scorePercent × (0.8 × (avg(topKRankersMarks) − currentStudentMarks))
     

   • If scorePercent ≤ 50%, no bonus; deduction stays.
6. Attempt ends after quiz 5 is submitted.

3.4 Mapped Gamification Elements

1. Quest Initiation

   • “Bonus Jump: your comeback quest” shown only to eligible users
   • Element: Quest

2. Opt‑In Screen

   • Shows current points, “Stake 25% to enter”
   • Elements:
     • Loss Aversion
     • Time-Limited Opportunity
     • Risk vs Reward Calculation

3. Attempt Bonus Exam

   • Timed bonus quiz during regular quiz window
   • Only available during that quiz index

4. Feedback & Transparency

   • Show score % immediately
   • Show success/failure and awarded bonus if applicable
   • Element: Feedback & Transparency

5. Progress Booster

   • Update points
   • Celebrate recovery or show motivation for retry
   • Element: Progress Boosters

---

3.5 Pseudocode

// CONFIGURATION
PERCENTILE_THRESHOLD = 75
STAKE_RATIO = 0.25
PASS_THRESHOLD = 50
BONUS_FACTOR = 0.8
TERM_LIMIT = 3
FAILURE_WARNING_THRESHOLD = 2        // Warn after 2 failures


// Determine eligibility for quizIndex (e.g., quiz 5)
function isEligible(user, quizIndex):
  if user.bonusJumpsThisTerm >= TERM_LIMIT:
    return (false, "Term limit reached")

  score = cumulativeScoreTill(user.id, quizIndex - 1)
  rank = percentileRank(score)
  if rank >= PERCENTILE_THRESHOLD:
    return (false, "Score above threshold")

  attempts = loadUserBonusHistory(user.id)
  failedAttempts = filter(attempts, a => a.status == "failed")
  
  return (true, failedAttempts.length >= FAILURE_WARNING_THRESHOLD ? "warning" : null)


// On Opt-In
function optInBonusJump(user, quizIndex):
  (ok, reason) = isEligible(user, quizIndex)
  assert ok, reason
  stake = floor(user.points × STAKE_RATIO)
  user.points -= stake
  user.bonusJumpsThisTerm += 1
  save(user)

  attempt = {
    userId: user.id,
    optedInAt: now,
    pointsStaked: stake,
    quizIndex: quizIndex,
    status: "pending"
  }
  saveBonusAttempt(attempt)
  return attempt._id

// After Attempt Submission
function submitBonusResult(attemptId, scorePercent):
  attempt = loadBonusAttempt(attemptId)
  assert attempt.status == "pending"

  bonus = 0
  passed = scorePercent > PASS_THRESHOLD
  if passed:
    avgTopK = averageTopKUsersPoints(5)
    userPoints = loadUserPoints(attempt.userId)
    gap = avgTopK - userPoints
    bonus = floor(scorePercent × (BONUS_FACTOR × gap))

  attempt.status = passed ? "passed" : "failed"
  attempt.scorePercent = scorePercent
  attempt.bonusAwarded = bonus
  saveBonusAttempt(attempt)

  user = loadUser(attempt.userId)
  user.points += bonus
  save(user)

  return {
    status: attempt.status,
    bonusAwarded: bonus,
    totalPoints: user.points
  }

---

[insaneonai]

Generic Design for Badge & Achievements System

---

1. Gamification Metric Entity

Gamification Metrics are measurable units that form the basis for gamification.
We define a collection named GamificationMetrics to create and Store metrics.

{
  "_id": "ObjectId",
  "Name": "String",
  "Type": "Enum",
  "units": "string",
  "defaultIncrementValue": "double"
}

Example

{
  "_id": "68511a120d4fe9ff703a6d8c",
  "Name": "points",
  "Type": "number",
  "units": "points", // this can also be null
  "defaultIncrementValue": 0
}

2. Achievements Entity:

Achievements are game elements that are awarded upon reaching a certain threhold defined over a Gamification metric.

{
"_id": "ObjectId",
"Name": "string",
"Trigger": "enum" // weather metric or a streak
"Metric": "ObjectId | null" // The metric that this achievement corresponds to.
"MetricTotal": "double | null" // the threshold value above which the Achievement is unlocked.
"StreakCount": "int | null", // the streak threshold value above which the Achievement is unlocked.
"BadgeUrl": "string" // image url for the badge.
}

Example

{
"_id": "68511a120d4fe9ff703a6d8c",
"Name": "Points Master",
"Trigger": "metric" // weather metric or a streak trigger
"Metric": "68511a120d4fe9ff703a6d8c" // The metric that this achievement corresponds to.
"MetricTotal": 1000 // the threshold value above which the Achievement is unlocked.
"StreakCount": int, // the streak threshold value above which the Achievement is unlocked.
"BadgeUrl": "examplebadge.jpeg" // image url for the badge.
}

3. UserGameMetrics Entity

UserGameMetrics entity stores the metrics on a per-user-per-metric level.
This Entity stores the actual values of the user's game elements i.e metrices.

{
"_id": "ObjectId",
"userId": "ObjectId",
"metricId": "ObjectId",
"value": "double",
"lastUpdated": "TimeStamp",
"createdAt": "TimeStamp"
}

Example

{
  "_id": "665a7e9fb4f3f0b7c8e5c321",
  "userId": "665a7e55b4f3f0b7c8e5c310",
  "metricId": "665a7e88b4f3f0b7c8e5c318",
  "value": 72.5,
  "lastUpdated": "2025-06-18T14:30:00Z",
  "createdAt": "2025-06-10T09:00:00Z"
}

4. UserGameAchievements

The UserGameAchievements entity stores the achievements unlocked by a user.

{
    "_id": "ObjectId",
    "userId": "ObjectId",
    "achievements": [{
    "id": "ObjectId",
    "unlockedOn": "TimeStamp"
    }, 
    ...]
}

{
  "_id": "64d2f79e5e4b6a001f8c1234",
  "userId": "64d2f7595e4b6a001f8b4567",
  "achievements": [
    {
      "id": "64d2f7995e4b6a001f8c0001",
      "unlockedOn": "2025-06-15T14:30:00Z"
    },
    {
      "id": "64d2f7a05e4b6a001f8c0002",
      "unlockedOn": "2025-06-17T09:45:00Z"
    },
    {
      "id": "64d2f7a85e4b6a001f8c0003",
      "unlockedOn": "2025-06-18T12:00:00Z"
    }
  ]
}

4. Example Dry Run

The following is an example dry run showcasing the interaction of various entities within the system.

Step 1 - Creating Metrics

Let's first create a Gamification Metric and call it points
This action is done by admin or the instructor.

{
  "_id": "68511a120d4fe9ff703a6d8c",
  "Name": "points",
  "Type": "number",
  "units": "points", // this can also be null
  "defaultIncrementValue": 0
}

Step 2 - Creating Badges or Achievements

Let's create a badge called points master which gets unlocked on 1000 points;
This action is done by admin or the instructor.

{
"_id": "68511a120d4fe9ff703a6d8b",
"Name": "Points Master",
"Trigger": "metric" // weather metric or a streak trigger
"Metric": "68511a120d4fe9ff703a6d8c" // The metric that this achievement corresponds to.
"MetricTotal": 1000 // the threshold value above which the Achievement is unlocked.
"StreakCount": int, // the streak threshold value above which the Achievement is unlocked.
"BadgeUrl": "examplebadge.jpeg" // image url for the badge.
}

Step 3 - User interaction & Event trigger

The user performs an interaction, e.,g attendinga quiz, which rewards them points, and upon receiving the point, we send Event API a request with the following payload:

{
"userId": "68511a120d4fe9ff703a6d8d",
"metricId": "68511a120d4fe9ff703a6d8c"
"value": 1100
}

Step 4 - The Event API

• The event API first updates the UserGameMetrics with an $inc operator over the value with matching userId and metricId.

• It then fetches those achievements with the same metricId and value (Achievements collection), less than or equal to value (UserGameMetrics collection).

• We use the $addToSet operator to add Achievements unlocked by the user to the UserGameAchievements collection while preventing duplicates.

Below is a pseudo-code implementation of the above steps:

function handleEvent(userId, metricId, incrementBy):

    # Step 1: Update the UserGameMetrics
    updatedMetric = db.UserGameMetrics.findOneAndUpdate(
        { userId: userId, metricId: metricId },
        {
            $inc: { value: incrementBy },
            $set: { lastUpdated: currentTimestamp() }
        },
        { upsert: true, returnDocument: "after" }
    )

    newValue = updatedMetric.value

    # Step 2: Fetch eligible achievements
    eligibleAchievements = db.Achievements.find(
        {
            Trigger: "metric",
            Metric: metricId,
            MetricTotal: { $lte: newValue }
        }
    )

    if eligibleAchievements.isEmpty():
    return {
        message: "Metric updated, no new achievements unlocked.",
        metricsUpdated: [{ metricId: metricId, newValue: newValue }],
        achievementsUnlocked: []
    }

    # Step 3: Prepare list of achievement objects (with timestamp)
    unlockedAchievements = [
        { id: achievement._id, unlockedOn: currentTimestamp() }
        for achievement in eligibleAchievements
    ]

    # Step 4: Add new achievements to UserGameAchievements using $addToSet + $each
    db.UserGameAchievements.updateOne(
        { userId: userId },
        {
            $addToSet: {
                achievements: {
                    $each: unlockedAchievements
                }
            }
        },
        { upsert: true }
    )

    return {
        message: "Metric updated, no new achievements unlocked.",
        metricsUpdated: [{ metricId: metricId, newValue: newValue }],
        achievementsUnlocked: eligibleAchievements
    }

API Endpoints

1. Admin / Developer Perspective (Manage Game Rules)

These endpoints are used to define and manage metrics, achievements, and view system-level data.

GameMetric API Endpoints

Method	Endpoint	Purpose
POST	/admin/metrics	Create a new GameMetric (e.g., login_days, quizzes_done)
GET	/admin/metrics	List all metrics
GET	/admin/metrics/:id	Get details of a single metric
PUT	/admin/metrics/:id	Update a metric
DELETE	/admin/metrics/:id	Delete a metric

Achievement API Endpoints

Method	Endpoint	Purpose
POST	/admin/achievements	Create a new Achievement
GET	/admin/achievements	List all achievements
GET	/admin/achievements/:id	Get achievement details
PUT	/admin/achievements/:id	Update an achievement
DELETE	/admin/achievements/:id	Delete an achievement

User Metrics & Achievements Endpoints

Method	Endpoint	Purpose
GET	/admin/users/:userId/metrics	View all tracked metrics for a user
GET	/admin/users/:userId/achievements	View all achievements earned by a user

Game Interaction

Method	Endpoint	Purpose
POST	/events	Trigger an event (e.g., login, quiz_completed) that updates user metrics and checks for achievements

User Progress

Method	Endpoint	Purpose
GET	/users/:userId/metrics	Get user's current metric progress
GET	/users/:userId/achievements	Get all achievements earned by the user
GET	/users/:userId/badges	Get badge images or unlocked rewards
GET	/users/:userId/progress	Full gamification profile (metrics + achievements)

Other / Additional Endpoints (Future Features)

Method	Endpoint	Purpose
GET	/leaderboard/:metric	View top users by a specific metric
GET	/achievements/available	List all achievements and user eligibility

This design provides a clear separation of concerns, making the badge and achievements system easy to maintain and extend.

[kapihllkumar]

PvP Quiz Duel System (ELO-Based)

Duel Structure

Challenge Types

• Speed Round: 3 questions, fastest correct answer wins.
• Accuracy Mode: 5 questions, highest score wins (ties decided by fastest total time).

Rules

• Time Limit: 15 seconds per question (auto-submit on timeout).

---

Matchmaking

Direct Challenge (Player-to-Player)

• Players can challenge any online user from their league or one tier above/below.
• Receiver can accept or decline.
• Max 3 pending challenges at a time.

Random Matchmaking

• Player clicks “Quick Duel” and selects:
  • Mode (Speed / Accuracy)
  • Topic
• Match found using tiered ELO algorithm.

---

Rematch Option

• After a match ends, both players are given the option to request a rematch.
• A rematch will only start if both players accept.
• The same duel mode (Speed/Accuracy) and topic can be reused, or players may choose new ones.
• ELO and streaks are affected as in a regular match.

---

Question Generation

• Questions are generated by an LLM based on the chosen topic.
• Includes multiple choice options and the correct answer.

---

Win/Loss (Δ)

If You Win:

Δ = BaseGain + (ELODifference × 0.1) + (Streak × 1.5)

If You Lose:

Δ = BaseLoss + (ELODifference × 0.05)

Where:

• BaseGain / BaseLoss = configurable constants
• ELODifference = |OpponentELO - YourELO|
• Streak = current win streak

---

Matching Algorithm

Tiered Pool

• Players are grouped by leagues (e.g., Bronze, Silver, Gold).

Range Expansion

• Initial range: ±100 ELO
• Expands by ±50 ELO every 5 seconds

Pseudocode

function findOpponent(user, mode, topic) {
  const eloRange = 100 + (queueTime / 5) * 50;
  return Players.inTier(user.tier)
    .filter(p => 
      Math.abs(p.elo - user.elo) <= eloRange &&
      (topic ? p.selectedTopics.includes(topic) : true)
    )
    .sortByEloCloseness();
}

Data Interfaces

Profile

{
  "userId": "string",
  "elo": "number",
  "league": "string", 
  "preferredTopics": ["string"],
  "pvpStats": {
    "wins": "number",
    "losses": "number",
    "currentStreak": "number",
    "highestStreak": "number",
    "winRates": {
      "speed": "number", 
      "accuracy": "number"  
    }
  },
  "matchHistory": [{
    "opponentId": "string",
    "outcome": "string",      
    "Topic": "string",
    "eloChange": "number",
    "Type": "string",       
    "questions": ["string"]
  }]
}

PVP Match

{
  "PVPId": "string",
  "type": "string",             
  "players": ["string", "string"],
  "topic": "string",
  "questions": ["string"],
  "results": {
    "winner": "string",
    "eloChanges": ["number", "number"],
  }
}

Questions

{
  "id": "string",
  "text": "string",
  "options": ["string"],
  "correctIndex": "number",
  "topic": "string",
  "difficulty": "number",     
  "stats": {
    "uses": "number",
    "accuracy": "number"      
  }
}

Shop System - Can Purchase from Shop

Items

Item Name	Description	Effect	Cooldown
New Player Shield	No ELO loss for first 3 duels; ELO gain allowed	ELO loss protection for 3 matches	None
ELO Freeze	No ELO loss for 3 hours	Protects ELO for 3 hours	1 day
Boost (3 hrs)	+10% Δ for 3 hours	10% bonus to ELO gain	1 day
Double Points	2× Δ on next win	Doubles ELO gain for next win	1 day
Streak Shield	Prevents streak break on next loss	Streak continues even after a loss	12 hours

{
  "shopItems": [{
    "id": "ObjectId",
    "name": "ELO Freeze",
    "icon": "img.png",
    "cost": 100,
    "description": "Protects you from losing ELO for 3 hours.",
    "cooldown": "24h"
  }]
}

[insaneonai]

Need for a Secondary Metric (Currency)

To ensure that points are perceived as valuable resources—particularly because they are redeemable for score—there is a need for a secondary in-app currency. This secondary currency can be used by users to perform other gamified actions within the application, such as participating in duels.

This Also Demands a Scoring System for the Secondary Currency that needs to be developed.

[insaneonai]

Other Smaller Gamification Elements

Heart or HP deduction: Players lose hearts (HP) for each incorrect attempt during quiz sessions. They can either spend in-app currency or points to purchase more hearts, or wait for them to regenerate over time.

Random Rewards: Occasionally, random rewards appear during the questionnaire. These can be earned by answering the associated question correctly.

Purchasing time: When required, a student can spend their in-app currency or points to earn time during quizzes or test submissions.

[kapihllkumar]

Question of the Day Feature

We can also implement a “Question of the Day” feature, where a quiz question automatically pops up for all users each day.

[Simhadri123]

Daily Streaks: Giving some bonus multiplier to the points earned for each consecutive day of activity—e.g. 2x multiplier for 7-day streak, 4x for 14-day streaks so that students are rewarded progressively more for maintaining a daily habit. The streak will be incremented only if they earn some points that day.

**Badges Collections & Meta‑Badges:**Group badges into collections, award a special “meta-badge” when a full set is completed. These meta badge obtained users will get some extra points or a score multiplier for a limited time

**Levels & Progress Bars:**Convert points into “experience” and show a progress bar toward the next level (e.g. Level 1 → Level 2).Unlock content or get some rewaards at each level.

[NikeshReddy7787]

Merit System — Behavioral side of system

The merit system acts as a behavior-level score that complements quiz performance and gamified Points. While those reflects skill, merit reflects integrity, responsibility, and engagement consistency.

Merit starts at Base: 100 and fluctuates based on your platform interactions. Falling below thresholds may limit user privileges, while high merit can unlock extra features

---

Merit Deduction Triggers

Quiz Participation

• Skipping quiz midway: Deducts −2 Merit
  Students leaving midway without submission show a lack of commitment, possibly gaming the system to avoid a low score.

• Excessive hint usage: Deducts −1 Merit
  Reliance on hints every time shows poor study discipline. Encourages learning over spoon-feeding.

---

Question Submission

• ≥3 low-quality/rejected submissions in a week: Deducts −3 Merit
  Repeated rejected questions indicate spammy or careless contributions. Merit loss discourages flooding the system with low-value content.

---

Peer Review Process

• Outlier or biased reviews: Deducts −1 Merit per review
  Reviews far outside the consensus (e.g., trolling with 1-stars, rating friends unfairly) damage the credibility of the Genuinity score (G). This deduction upholds review trust.

• Failing to review after opting in: Deducts −1 Merit
  Users who claim review slots but don’t complete them disrupt the peer pipeline and block others from contributing.

---

Understanding Submissions

• LLM-rated poor explanations: Deducts −3 Merit
  If explanation quality is below a threshold, it triggers not only a 30% score penalty but also a behavior flag. Merit loss identifies ignoring reflection.

---

Revalidation Misuse

• Invalid revalidation content (non-sense appeal): Deducts −2 Merit
  If a user stakes points but submits nonsense or copy-paste content to abuse/challenge the system, it creates load on LLMs/reviewers. The penalty ensures that only genuine revalidations are made.

---

Engagement Decay

• Inactive for > a quiz cycle: Deducts −1 Merit per inactive cycle
  Ensures merit reflects active users. Inactivity leads to a slow decay in merit to encourage consistent participation.

---

Merit Deduction Table

Context / Section	Trigger / Condition	Merit Deduction
Quiz Participation	Skipping quiz midway or abandoning early	−2 Merit
	Excessive hint usage (> threshold)	−1 Merit
Question Submission	≥3 rejected submissions/week	−3 Merit
Peer Review Process	Review is outlier vs. average (biased or trolling)	−1 Merit per review
	Failing to review after opting in	−1 Merit
Understanding Submission	Below LLM threshold (in addition to 30% score penalty)	−3 Merit
Revalidation Misuse	Contesting with invalid content (false revalidation attempt)	−2 Merit
Engagement Decay	Inactive >7 days (no quizzes, submissions, etc.)	−1 Merit/week

---

Merit Recovery Triggers

While users may lose merit for negative behaviors, the system also offers structured and fair recovery paths to regain it. These ensure students who improve over time can fully re-engage with platform features.

Each action below is designed to reflect responsible behavior, not just system usage.

---

Merit Recovery Table

Recovery Method	Condition	Merit Gain	Why It Helps
Complete a Quiz Responsibly	Quiz completed with ≥40% score	+3 Merit	Demonstrates honest engagement and basic comprehension
Write a Good Understanding	LLM rates the explanation ≥ threshold	+5 Merit	Sincerity
Complete Remedial Module	Full completion of foundational or remedial content	+4 to +5 Merit	Recovery-specific pathways for those in low merit zones (e.g., < 50 merit)
Consistent Activity	Logging activity for a whole quiz cycle	+2 Merit	Shows sustained effort and platform discipline
Daily Challenge Completion	Win or complete a challenge like "QOTD", speed task, etc.	+1 Merit	Encourages platform habit and touchpoints with varied content
Streak	Observe his streak componenet and award	+3 Merit	Once he gets the streak of fortnite award him +3 merit points (Award once per K cycles)
Validated Report / Flag	Reported invalid question or biased review and it was verified	+1 to +2 Merit	Promotes peer accountability and platform hygiene
Seek Peer Support (Upvotes)	User requests feedback and receives ≥3 valid peer upvotes on reflection/answer	+2 Merit (capped weekly)	Crowd-aided trust recovery

---

Threshold-Based Merit Restrictions (Tentative)

To maintain system integrity, certain actions or benefits can be locked behind minimum merit requirements:

Merit Score	Restricted Features
< 90 Merit	Cannot submit new questions or peer reviews
< 80 Merit	Daily bonus multipliers disabled
< 70 Merit	PvP Battles and Group Challenges restricted
< 60 Merit	Cannot contest penalties or access special event rewards
< 50 Merit	Account enters review lock — must rebuild merit via foundational quizzes

These thresholds act as mild behavioral gating and help distinguish high-engagement learners from low-integrity users.

---

[Simhadri123]

Badges Collections & Meta‑Badges for Vibe

Vibe rewards students for their learning journey through themed badge collections. Each collection has individual badges and a powerful Meta-Badge unlocked by earning all badges in that set. Meta-Badges grant special perks for a limited time.

---

How It Works

• Badge: Earned by meeting a specific achievement or milestone.
• Meta-Badge: Awarded when all badges in a collection are unlocked. Grants a unique, time-limited perk.
• Perks: Temporary boosts (e.g., bonus points, multipliers) or instant rewards.

---

1. Quiz Master Collection

For students who excel at quizzes.

Badge	Criteria	Perk
Quick Solver	Complete a quiz within 5 minutes (min. 80% score, unique quizzes only)	—
Accuracy Ace	Score 90%+ in 3 different quizzes (unique quizzes, no repeats)	—
Quiz Enthusiast	Attempt 10 quizzes (unique quizzes, min. 5 questions each)	—
Meta-Badge	Unlock all above → Grand Quizmaster	+15% quiz points for 48 hrs

Meta-Badge Perk:
Grand Quizmaster gives +15% quiz points for 48 hours.

---

2. Video Voyager Collection

For students who engage with video content.

Badge	Criteria	Perk
First Watch	Watch your first course video (at least 80% watched, no skipping)	—
Binge Learner	Watch 5 videos in one day (at least 80% of each, no background tab)	—
Completionist	Watch 90%+ of videos in a course (each video at least 80% watched, no skipping or fast-forwarding)	—
Meta-Badge	Unlock all above → Video Virtuoso	+10% total points for 48 hrs

Meta-Badge Perk:
Video Virtuoso gives +10% total points for 48 hours.

---

3. Hardworker Collection

For students who dedicate time and effort.

Badge	Criteria	Perk
Late-Nighter	Study after 10 PM for 3 days (active engagement required)	—
Work Horse	Spend 15 hours on platform in a week (active time only, idle time excluded)	—
Marathoner	Spend 40 hours on platform in two weeks (active time only, idle time excluded)	—
Meta-Badge	Unlock all above → Marathon Master	Bonus 300 points

Meta-Badge Perk:
Marathon Master grants an instant bonus of 300 points.

---

4. Consistency Champion Collection

For students who build strong learning habits.

Badge	Criteria	Perk
Routine Starter	Complete 1 activity for 5 consecutive days (unique activities, timezone locked)	—
Habit Maker	Complete 1 activity for 15 consecutive days (unique activities, timezone locked)	—
Consistency Champ	Complete 1 activity for 30 consecutive days (unique activities, timezone locked)	—
Meta-Badge	Unlock all above → Streak Champion	1.4× points for 48 hrs

Meta-Badge Perk:
Streak Champion multiplies all points by 1.4× for 48 hours.

---

5. Course Completion Collection

For students who finish courses.

Badge	Criteria	Perk
First Finish	Complete your first course (course must be at least 5 hours of content)	—
Course Collector	Complete 3 different courses (unique, not repeated, min. 5 hours each)	—
Course Marathoner	Complete 5 different courses (unique, not repeated, min. 5 hours each)	—
Meta-Badge	Unlock all above → Course Conqueror	Bonus 500 points

Meta-Badge Perk:
Course Conqueror grants an instant bonus of 500 points.

---

6. Challenge Conqueror Collection

For students who take on tough challenges.

Badge	Criteria	Perk
Risk Taker	Attempt 4 hard-level quizzes (system-labeled only, unique quizzes)	—
Challenge Completer	Successfully pass 6 challenges/quizzes labeled "hard" (system-labeled, unique, min. 70% score)	—
No Hints Pro	Complete 10 hard-level quizzes without using hints (system-labeled, unique quizzes)	—
Meta-Badge	Unlock all above → Ultimate Challenger	+20% quiz points for 48 hrs

Meta-Badge Perk:
Ultimate Challenger gives +20% quiz points for 48 hours.

---

7. Meta-Badge Perk Reactivation

• Meta-Badges are permanent honors.
• Perks are time-limited and can be reactivated after a cooldown by completing a special challenge for each collection.
• Cooldown: After a perk expires, you must wait 7 days before attempting the reactivation challenge.

Reactivation Rules

• Only new, unique activities count toward reactivation (no repeats).
• All reactivation challenges are monitored for genuine engagement.
• System clock manipulation or automation will void progress.

Reactivation Conditions by Collection

Collection	Reactivation Challenge	Cooldown
Quiz Master	Score 90%+ in 2 unique quizzes within 1 days	7 days
Video Voyager	Watch 5 new course videos in a single day (no repeats)	7 days
Hardworker	Spend 10 active hours on the platform in 5 days	7 days
Consistency Champion	Complete 1 unique activity daily for 7 consecutive days	7 days
Course Completion	Complete 1 new course (not previously completed)	7 days
Challenge Conqueror	Pass 2 new hard-level quizzes without hints	7 days

• After the cooldown, completing the reactivation challenge will grant the perk again for its original duration.

[kapihllkumar]

PvP Quiz Duel System - End Points

1. API Endpoints

A. Direct Duel Endpoints

Endpoint	Method	Description	Request Body	Response
/duels/direct/challenge	POST	Challenge specific player	{ mode: "speed"|"accuracy", opponentId: string, topic?: string }	{ duelId: string, status: "pending" }
/duels/direct/pending	GET	List pending direct challenges	None	{ challenges: DirectChallenge[] }
/duels/direct/accept/:challengeId	POST	Accept direct challenge	None	{ duelId: string, questions: [] }
/duels/direct/decline/:challengeId	POST	Decline direct challenge	None	{ message: "Challenge declined" }

B. Random Duel Endpoints

Endpoint	Method	Description	Request Body	Response
/duels/random/queue	POST	Enter random matchmaking queue	{ mode: "speed"|"accuracy", topic?: string }	{ queueId: string, position: number }
/duels/random/status	GET	Check queue status	None	{ status: "waiting"|"matched", duelId?: string }
/duels/random/cancel	POST	Leave matchmaking queue	None	{ success: boolean }

C. Common Duel Endpoints

Endpoint	Method	Description	Request Body	Response
/duels/submit/:duelId	POST	Submit answers	{ answers: Answer[] }	{ winner: string, eloChanges: number[] }
/duels/rematch/:duelId	POST	Request rematch	None	{ status: "pending"|"accepted"|"rejected" }
/duels/history	GET	Match history	None	{ matches: PvPMatch[] }
/duels/leaderboard	GET	Leaderboard	?league=bronze|silver|gold|platinum	{ players: LeaderboardEntry[] }

D. Shop Endpoints

Endpoint	Method	Description	Request Body	Response
/shop/items	GET	Available items	None	{ items: ShopItem[] }
/shop/purchase/:itemId	POST	Buy item	None	{ success: boolean }
/shop/inventory	GET	User's items	None	{ items: UserItem[] }

E. Badges Endpoints

Endpoint	Method	Description	Request Body	Response
/badges	GET	All badges	None	{ unlocked: Badge[], locked: Badge[] }
/badges/:badgeId	GET	Badge details	None	{ badge: Badge, progress?: number }

2. Badges System

PvP Duel Badges

Badge Name	Criteria
Duel Novice	Complete first PvP duel
Speed Demon	Win 5 Speed duels in a row
Accuracy Master	100% accuracy in 5 Accuracy duels
Perfect Streak	Win 10 duels consecutively
ELO Grinder	Gain 500+ ELO in a week
Comeback King	Win from 2+ question deficit
Tactician	Win without using hints
Rival Slayer	Beat same opponent 3x straight
Tournament Champ	Win weekly tournament