Replies: 12 comments 5 replies
-
|
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! |
Beta Was this translation helpful? Give feedback.
-
|
Hi everyone, 1. PvP Quizzes or Team-based Quizzes with ELO Rating System 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 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 4. Seasons and Time-Limited Events 5. Unlockable Content (Leveling Up Through Progress) 6. Virtual Currencies
7. Storylines and Progression Narratives 8. Learning Modes
9. Freedom of Choice (Agency in Learning)
10. Freedom to Fail
11. Fostering Collaboration 12. Gamified Learning Activities
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
|
Beta Was this translation helpful? Give feedback.
-
Points Scoring System — Discussion1. Initial Data StructureWe 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.
2. Categorizing Confidence Levels
For the example payload we will have something as bellow
3. CrossTabulation of confidence Scores and ResultsWe 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.
4. Penalty and Incentives covering all 4 cases & other factors.
5. Compute Confidence ScoreThe individual weight factor can be visualized as below:
To calculate the confidenceScore, use a weighted formula based on how well the user's confidence aligns with correctness: 6. Calculate Total Hint PenaltyDiscourages over-reliance on hints while still allowing their strategic use. Note: hint_penalty should be negative. 7. Calculate Streak BonusEncourages consistent performance by rewarding users for maintaining streaks of correct answers. 8. TimeBonus
In other words:
9. AttemptPenaltyWe want to encourage learning that requires multiple attempts while still applying a mild penalty for repeated tries, so the weighting should be gentle. 10. Final Score11. Increment the Global Points;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)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)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[]
...} |
Beta Was this translation helpful? Give feedback.
-
League System Implementation for VibeI 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. OverviewLeagues group students into tiers. Two implementation styles:
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 Modelconst Student = {
_id: ObjectId,
name: String,
league: String, // 'Bronze', 'Silver', etc.
weeklyPts: Number,
attempts: Number,
totalPts: Number
};Performance Metrics
Ranking & Tie-Breaking
Promotion & Demotion
Automation & Scheduling
Reward Structures
Top-Spot PerksWeekly #1 in each league chooses one perk:
Approach 2: Non-Competitive LeaguesData Modelconst Student = {
_id: ObjectId,
name: String,
totalPts: Number,
league: String // 'Bronze', 'Silver', etc.
};Points Thresholds
Assignment Logic
Reward Structures
Sample CodeCompetitive League: Promotion & Demotionfunction 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 Assignmentfunction 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? |
Beta Was this translation helpful? Give feedback.
-
Bonus Jump Mechanism for Underperforming StudentsStudents 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 & PersistenceBonusJump 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
3.3 Flow
3.4 Mapped Gamification Elements
3.5 Pseudocode |
Beta Was this translation helpful? Give feedback.
-
Generic Design for Badge & Achievements System1. Gamification Metric EntityGamification Metrics are measurable units that form the basis for gamification. {
"_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 EntityUserGameMetrics entity stores the metrics on a per-user-per-metric level. {
"_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. UserGameAchievementsThe 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 RunThe following is an example dry run showcasing the interaction of various entities within the system. Step 1 - Creating MetricsLet's first create a Gamification Metric and call it {
"_id": "68511a120d4fe9ff703a6d8c",
"Name": "points",
"Type": "number",
"units": "points", // this can also be null
"defaultIncrementValue": 0
}Step 2 - Creating Badges or AchievementsLet's create a badge called {
"_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 triggerThe 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
Below is a pseudo-code implementation of the above steps: API Endpoints1. Admin / Developer Perspective (Manage Game Rules)These endpoints are used to define and manage metrics, achievements, and view system-level data. GameMetric API Endpoints
Achievement API Endpoints
User Metrics & Achievements Endpoints
Game Interaction
User Progress
Other / Additional Endpoints (Future Features)
This design provides a clear separation of concerns, making the badge and achievements system easy to maintain and extend. |
Beta Was this translation helpful? Give feedback.
-
PvP Quiz Duel System (ELO-Based)Duel StructureChallenge Types
Rules
MatchmakingDirect Challenge (Player-to-Player)
Random Matchmaking
Rematch Option
Question Generation
Win/Loss (Δ)If You Win:Δ = BaseGain + (ELODifference × 0.1) + (Streak × 1.5) If You Lose:Δ = BaseLoss + (ELODifference × 0.05) Where:
Matching AlgorithmTiered Pool
Range Expansion
Pseudocodefunction 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 InterfacesProfile{
"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 ShopItems
{
"shopItems": [{
"id": "ObjectId",
"name": "ELO Freeze",
"icon": "img.png",
"cost": 100,
"description": "Protects you from losing ELO for 3 hours.",
"cooldown": "24h"
}]
} |
Beta Was this translation helpful? Give feedback.
-
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. |
Beta Was this translation helpful? Give feedback.
-
Other Smaller Gamification ElementsHeart 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. |
Beta Was this translation helpful? Give feedback.
-
Merit System — Behavioral side of systemThe 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 TriggersQuiz Participation
Question Submission
Peer Review Process
Understanding Submissions
Revalidation Misuse
Engagement Decay
Merit Deduction Table
Merit Recovery TriggersWhile 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
Threshold-Based Merit Restrictions (Tentative)To maintain system integrity, certain actions or benefits can be locked behind minimum merit requirements:
These thresholds act as mild behavioral gating and help distinguish high-engagement learners from low-integrity users. |
Beta Was this translation helpful? Give feedback.
-
Badges Collections & Meta‑Badges for VibeVibe 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
1. Quiz Master CollectionFor students who excel at quizzes.
Meta-Badge Perk: 2. Video Voyager CollectionFor students who engage with video content.
Meta-Badge Perk: 3. Hardworker CollectionFor students who dedicate time and effort.
Meta-Badge Perk: 4. Consistency Champion CollectionFor students who build strong learning habits.
Meta-Badge Perk: 5. Course Completion CollectionFor students who finish courses.
Meta-Badge Perk: 6. Challenge Conqueror CollectionFor students who take on tough challenges.
Meta-Badge Perk: 7. Meta-Badge Perk Reactivation
Reactivation Rules
Reactivation Conditions by Collection
|
Beta Was this translation helpful? Give feedback.
-
PvP Quiz Duel System - End Points1. API EndpointsA. Direct Duel Endpoints
B. Random Duel Endpoints
C. Common Duel Endpoints
D. Shop Endpoints
E. Badges Endpoints
2. Badges SystemPvP Duel Badges
|
Beta Was this translation helpful? Give feedback.
Uh oh!
There was an error while loading. Please reload this page.
Uh oh!
There was an error while loading. Please reload this page.
-
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
The designed points system aims to:
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:
Scoring Algorithm Example
A generalized formula for calculating the score for a question might be:
Notes:
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
Eligibility:
Students whose cumulative score falls below the 75th percentile.
Bonus Jump Cost:
When a student opts for a bonus jump, 25% of their existing points is deducted immediately.
Exam Performance Bonus:
After the exam, if a student scores more than 50% (denoted as ( x% )), they receive additional bonus points calculated as:
Outcome
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:
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:
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
An LLM evaluates the submission and assigns a quality score.
Revalidation (Contestation) Process
If a student believes the penalty was unjustified, they can contest via a revalidation process:
Staking Points:
The student must stake 50% of their current points to initiate revalidation.
Reevaluation:
The submitted text is re-evaluated using either a secondary LLM or an alternative scoring algorithm. Peer validation can also be incorporated.
Outcome and Formula:
If Revalidation Is Positive:
If Revalidation Is Negative:
Summary Formula for Revalidation Outcome
Refund/Bonus Calculation (if approved):
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
Apply the scoring algorithm and update the student’s cumulative points.
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:
B. Question Contribution Flow
Student submits a question.
The question is preliminarily evaluated.
First ( K ) reviews are gathered and aggregated to form the genuinity score ( G ).
C. Understanding Submission Flow
Student writes an understanding text in plain text/Markdown.
LLM scores the quality.
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:
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:
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.
Beta Was this translation helpful? Give feedback.
All reactions