AMEP is a unified AI-powered education platform that transforms fragmented classroom data into actionable intelligence through three integrated pillars:
| Pillar | What It Does | Key Differentiator |
|---|---|---|
| 🎯 Adaptive Mastery Engine | Tracks concept mastery 0-100, generates personalized homework | Uses Deep Knowledge Tracing + Memory Networks (not just simple quizzes) |
| 👁️ Inclusive Engagement System | Captures 100% participation via anonymous polling + implicit signals | Combines explicit + implicit engagement (goes beyond hand-raising) |
| 📊 PBL & Analytics Hub | Manages projects, assesses soft skills, provides unified teacher dashboard | Objective soft-skill rubrics + automated workload reduction |
Proven Results from Research:
- 12.4% improvement in learning outcomes (Paper 4.pdf)
- 20% reduction in time on mastered topics (Paper 4.pdf)
- 25% faster task completion (Paper 6.pdf)
- 3 hours/week saved on lesson planning (Paper 15.pdf)
| Business Requirement | Problem It Addresses | AMEP Solution | Research Source |
|---|---|---|---|
| BR1: Personalized Concept Mastery | Static assessments don't track evolving knowledge | Dynamic mastery scoring engine (0-100) using Deep Knowledge Tracing with LSTM networks that continuously updates based on every interaction | 2105_15106v4.pdf |
| BR2: Adaptive Practice Delivery | One-size-fits-all homework ignores individual gaps | AI algorithm targets tasks slightly above current competency (Zone of Proximal Development) using cognitive load optimization | 6.pdf, 4.pdf |
| BR3: Efficiency of Practice | Students waste time on already-mastered concepts | Memory-aware knowledge tracing identifies mastered vs. weak areas; reduces repetition by 20% | 4.pdf, 2105_15106v4.pdf |
| Business Requirement | Problem It Addresses | AMEP Solution | Research Source |
|---|---|---|---|
| BR4: Inclusive Engagement Capture | 30% of students invisible (quiet, disengaged) | Anonymous polling ensures 100% participation + implicit behavior analytics (login frequency, time-on-task, reattempts) | 8h.pdf, 6.pdf |
| BR5: Objective Soft-Skill Assessment | Teamwork/creativity assessed subjectively | 4-dimension validated framework (TD, TS, TM, TE) with Cronbach α > 0.97; Bayesian prediction models | 11.pdf, 10.pdf |
| BR6: Actionable Teacher Feedback | Teachers get data too late to intervene | Real-time dashboard with class-level engagement index, at-risk alerts, and post-intervention tracking | 62379RAE2024_11.pdf, 13.pdf |
| Business Requirement | Problem It Addresses | AMEP Solution | Research Source |
|---|---|---|---|
| BR7: Workload Reduction | Teachers spend 3+ hours/week on planning | Searchable repository of curriculum-aligned templates; collaborative planning networks | 14.pdf, 15.pdf |
| BR8: Unified Data Reporting | Data fragmented across 5-10 tools | Single dashboard showing Mastery Rate, Adoption Rate, Confidence Score; data drops reduced from 6 to 3/year | 12.pdf, 16.pdf |
| Business Requirement | Problem It Addresses | AMEP Solution | Research Source |
|---|---|---|---|
| BR9: Centralized PBL Workspace | Project management chaotic, milestones missed | 5-stage PBL workflow (Question→Define→Research→Create→Present) with team formation, Gantt charts, artifact submission | 17.pdf, 11.pdf |
Solving: BR1 (Personalized Concept Mastery), BR2 (Adaptive Practice Delivery), BR3 (Efficiency of Practice)
Problem Statement :
"The inability to provide truly personalized instruction at scale... results in uneven student engagement, overlooked learning difficulties, and inefficient use of instructional time."
Specific Challenges:
- ❌ Static assessments don't capture evolving knowledge states
- ❌ One-size-fits-all assignments ignore individual learning gaps
- ❌ Students waste time repeating already-mastered concepts
- ❌ Teachers can't identify which concepts need reinforcement
A real-time mastery scoring system that:
- ✅ Calculates concept mastery scores (0-100) for each student per concept (BR1)
- ✅ Dynamically updates scores based on assessments, practice, and learning progression (BR1)
- ✅ Generates personalized practice assignments targeting knowledge gaps (BR2)
- ✅ Keeps students in their Zone of Proximal Development (ZPD) (BR2)
- ✅ Reduces repetition on mastered concepts while focusing on weak areas (BR3)
Source: Paper 2105_15106v4.pdf - "Knowledge Tracing: A Comprehensive Survey"
We will implement a three-layer hybrid approach:
Layer 1: Bayesian Knowledge Tracing (BKT) for Interpretability Solves BR1: Continuous mastery scoring
P(Ln) = P(Ln|Answer) + (1 − P(Ln|Answer)) × P(T)
P(Cn+1) = P(Ln)(1 − P(S)) + (1 − P(Ln)) × P(G)
Where:
- P(Ln) = Probability of mastery at interaction n (our 0-100 score)
- P(T) = Probability of learning transition
- P(G) = Probability of guessing correctly
- P(S) = Probability of slipping (mistake despite mastery)
Layer 2: Deep Knowledge Tracing (DKT) for Accuracy Solves BR1: Handles complex learning patterns Using LSTM networks to capture complex learning patterns:
ht = tanh(Whs·xt + Whh·ht-1 + bh)
yt = σ(Wyh·ht + by)
Where:
- ht = hidden state representing knowledge
- xt = current learning interaction
- yt = predicted mastery probability
Layer 3: Memory-Aware Knowledge Tracing (DKVMN) for Concept Relationships Solves BR3: Identifies what's mastered vs. what needs work
wt = Softmax(kt·Mk) // Correlation weight
rt = Σ wt(i)·Mv(i) // Read operation - retrieve mastery
Mv(i) = Mv(i) + wt(i)·addt // Write operation - update mastery
Solves BR2: Tasks slightly above current competency (ZPD) Source: Paper 6.pdf - Algorithm 1
# Adaptive Learning with Feedback Loops
Input: Initial knowledge state K0, content difficulty D0,
response time τ, learning rate α, scaling factor γ
For t = 1 to T (epochs):
1. Present learning content Lt with difficulty Dt
2. Record student response Rt and response time τt
3. Update knowledge state: Kt ← β1·Kt-1 + (1-β1)·Rt
4. Update learning objective: Lt ← γ·τt·(1-Kt)
5. Compute bias-corrected knowledge: K̂t ← Kt/(1-β1^t)
6. Update learning parameter: θt ← θt-1 - α·K̂t/√(L̂t+ε)
7. Adjust content difficulty: Dt+1 ← Dt + γ·(K̂t - 0.5)
# If mastery > 0.5, increase difficulty (stay in ZPD)
# If mastery < 0.5, decrease difficulty
Return θt (optimized learning parameters)Solves BR2: Maximizes learning efficiency without overwhelming Source: Paper 6.pdf - Equation 5
L(t) = Σ λi · Di · (1 - ki(t))
Where:
- λi = weight/importance of topic i
- Di = difficulty level of topic i
- ki(t) = student's proficiency in topic i
Goal: Keep L(t) within optimal threshold Lopt
- If L(t) > Lopt → reduce difficulty, add scaffolding
- If L(t) < Lopt → increase challenge to maintain engagement
Solves BR3: Reduces repetition on mastered concepts Source: Paper 4.pdf - Results Section
PRACTICE EFFICIENCY ALGORITHM:
┌─────────────────────────────────────────────────────────┐
│ For each concept C in curriculum: │
│ IF mastery_score(C) > 85%: │
│ → Skip practice (already mastered) │
│ → Move to next concept │
│ ELIF mastery_score(C) > 60%: │
│ → Light review (1-2 questions) │
│ → Focus time on weaker areas │
│ ELSE: │
│ → Focused practice (5-10 questions) │
│ → Provide scaffolding and hints │
└─────────────────────────────────────────────────────────┘
RESEARCH RESULT:
"Learners spent 20% less time on topics they mastered
quickly and received extended practice on topics where
they progressed more slowly" - Paper 4.pdf
Source: Paper 6.pdf - "AI-Powered Learning Pathways"
┌─────────────────────────────────────────────────────────────┐
│ AMEP MASTERY ENGINE │
│ Solving: BR1, BR2, BR3 │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Data Layer │───▶│ AI Engine │───▶│ Application │ │
│ │ │ │ │ │ Layer │ │
│ │ • Student │ │ • BKT Model │ │ • Dashboard │ │
│ │ responses │ │ (BR1) │ │ • Progress │ │
│ │ • Time data │ │ • DKT/LSTM │ │ Reports │ │
│ │ • Engagement│ │ (BR1) │ │ • Practice │ │
│ │ metrics │ │ • DKVMN │ │ Generator │ │
│ │ │ │ (BR3) │ │ (BR2) │ │
│ │ │ │ • Adaptive │ │ │ │
│ │ │ │ Algorithm │ │ │ │
│ │ │ │ (BR2) │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────┘
| Existing Solutions | AMEP Advantage | Problem Solved | Source |
|---|---|---|---|
| Simple quiz-based adaptive systems | Uses Deep Knowledge Tracing with LSTM for complex pattern recognition | BR1: Better mastery accuracy | 2105_15106v4.pdf |
| Binary "mastered/not mastered" | Continuous 0-100 scoring with probability distributions | BR1: Granular progress tracking | 2105_15106v4.pdf |
| Fixed difficulty progression | Real-time cognitive load optimization keeps students in ZPD | BR2: Optimal challenge level | 6.pdf |
| Repeat all questions equally | Memory-aware models track what's mastered vs. needs work | BR3: Efficient practice time | 4.pdf |
| No forgetting consideration | DKVMN tracks knowledge decay over time | BR1: Accurate long-term tracking | 2105_15106v4.pdf |
Problem Statement :
"Subjective and delayed assessments... result in uneven student engagement, overlooked learning difficulties" "No learner remains unnoticed or unsupported"
Specific Challenges:
- ❌ Only vocal students participate (quiet students invisible)
- ❌ Teachers rely on subjective observation
- ❌ Engagement data arrives too late for intervention
- ❌ No way to measure implicit engagement (attention, interest)
A comprehensive engagement capture system that:
- ✅ Ensures 100% participation visibility through anonymous polling (BR4)
- ✅ Captures implicit engagement indicators in real-time (BR4)
- ✅ Delivers class-level engagement insights to teachers (BR6)
- ✅ Enables immediate instructional intervention (BR6)
- ✅ Measures post-intervention improvement (BR6)
Solves BR4: 100% participation visibility through anonymous input Source: Paper 8h.pdf - "Impact of Live Polling Quizzes"
Live Polling Quiz (LPQ) Features:
├── Anonymous response collection (no fear of judgment)
├── Real-time result aggregation
├── Instant feedback display
├── Fact-based question support
└── Mobile device compatibility
KEY RESEARCH FINDING:
"By giving every student a chance to respond anonymously,
live polling promotes inclusion and provides quieter
students a voice" - Paper 8h.pdf
"Immediate feedback allows lecturers to gauge student
understanding in real-time and adjust teaching timely"
- Paper 8h.pdf
Solves BR4: Real-time implicit engagement indicators Source: Paper 6.pdf - Section D & Paper 2105_15106v4.pdf
Engagement Metrics Captured (BR4):
┌─────────────────────────────────────────────┐
│ EXPLICIT INDICATORS (from polls) │
├─────────────────────────────────────────────┤
│ • Poll responses (understanding level) │
│ • Question accuracy │
│ • Participation rate │
├─────────────────────────────────────────────┤
│ IMPLICIT INDICATORS (from behavior) │
├─────────────────────────────────────────────┤
│ • Frequency of logins │
│ • Time spent on activities │
│ • Number of interactions with resources │
│ • Response times for quizzes │
│ • Task completion rates │
│ • Reattempts at challenging exercises │
│ • Use of optional resources │
│ • Participation in discussions/peer reviews │
└─────────────────────────────────────────────┘
Solves BR4: Ensures no learner remains unnoticed Source: Paper 2105_15106v4.pdf - Section on Incorporating Engagement
# Knowledge and Affect Tracing (KAT) - Sensorless Engagement Model
# Detects "gaming" behaviors that indicate disengagement
Disengagement Behaviors:
1. Quick Guess: Response time < threshold (answering without thinking)
2. Bottom-out Hint: All available hints used (not trying)
3. Many Attempts: More than 3 attempts on single exercise (random clicking)
def detect_disengagement(response_time, hints_used, attempts):
"""
Identifies students who may be disengaged or struggling
Solves BR4: No learner remains unnoticed
"""
gaming_score = 0
if response_time < QUICK_GUESS_THRESHOLD:
gaming_score += 1 # Quick guess detected
if hints_used == MAX_HINTS:
gaming_score += 1 # Bottom-out hint detected
if attempts > 3:
gaming_score += 1 # Many attempts detected
if gaming_score >= 2:
return "AT_RISK" # Flag for teacher attention
elif gaming_score == 1:
return "MONITOR" # Watch closely
else:
return "ENGAGED" # Student is engagedSolves BR6: Actionable, real-time, unbiased feedback Source: Paper 62379RAE2024_11.pdf - "Real-Time Feedback on Teaching Pace"
┌─────────────────────────────────────────────────────────────┐
│ AMEP ENGAGEMENT DASHBOARD (BR6: Actionable Teacher Feedback)│
├─────────────────────────────────────────────────────────────┤
│ CLASS ENGAGEMENT INDEX [87/100] █████████│
│ (Aggregated from explicit + implicit signals) │
├─────────────────────────────────────────────────────────────┤
│ INSTANT POLL: "Do you understand today's concept?" │
│ ████████████████ Yes (72%) │
│ ████████ Partially (20%) │
│ ███ No (8%) ⚠️ Consider re-explaining │
├─────────────────────────────────────────────────────────────┤
│ STUDENT ATTENTION MAP (Implicit Signals): │
│ [●] Engaged [○] Passive [!] At-Risk │
│ ●●●●○●●●●○●●●!●●●●○●●●●●●●●●●!●● │
│ │
│ 2 students flagged for immediate attention │
├─────────────────────────────────────────────────────────────┤
│ POST-INTERVENTION TRACKING (BR6): │
│ Yesterday's intervention on Topic 2.3: │
│ Before: 55% understanding → After: 78% understanding │
│ Improvement: +23% ✅ │
├─────────────────────────────────────────────────────────────┤
│ ACTIONABLE RECOMMENDATIONS: │
│ • 3 students may need 1-on-1 support │
│ • Topic 2.3 average mastery still at 68% - revisit │
│ • Talk time ratio: 70% teacher / 30% student (adjust?) │
└─────────────────────────────────────────────────────────────┘
| Existing Solutions | AMEP Advantage | Problem Solved | Source |
|---|---|---|---|
| Hand-raising only | Anonymous polling ensures 100% participation | BR4: Inclusive capture | 8h.pdf |
| Post-class surveys | Real-time engagement during instruction | BR6: Immediate feedback | 62379RAE2024_11.pdf |
| Subjective teacher observation | AI-powered implicit behavior analysis | BR4: Unbiased detection | 6.pdf |
| Single metric (attendance) | Multi-dimensional engagement scoring | BR4: Comprehensive view | 2105_15106v4.pdf |
| Delayed feedback | Instant class-level insights | BR6: Immediate intervention | 62379RAE2024_11.pdf |
| No intervention tracking | Measures improvement after teacher action | BR6: Post-intervention measurement | 62379RAE2024_11.pdf |
Problem Statement (from PS01):
"Structured design and objective evaluation of multidisciplinary projects, including measurable assessment of collaboration, communication, and problem-solving skills"
Specific Challenges:
- ❌ Soft skills (teamwork, creativity) assessed subjectively
- ❌ No standardized rubrics across teachers
- ❌ Project management is chaotic (missed milestones)
- ❌ Team dynamics issues go undetected
- ❌ No structured artifact submission process
A centralized PBL workspace that:
- ✅ Provides standardized mechanisms for objective soft-skill assessment (BR5)
- ✅ Includes peer-review inputs for multidimensional evaluation (BR5)
- ✅ Offers progress visualization dashboards (BR5)
- ✅ Supports team formation and role assignment (BR9)
- ✅ Tracks tasks and milestones with Gantt charts (BR9)
- ✅ Enables structured artifact submission (BR9)
Solves BR9: Centralized workspace for project execution Source: Paper 17.pdf - "Tackle Implementation Challenges in PBL"
PBL Learning Process (BR9: Streamlined Project Execution):
┌──────────────────────────────────────────────────────────┐
│ Stage 1: QUESTIONING │
│ • Inquiry-based approach to generate project ideas │
│ • SWOT analysis tools │
│ • Consumer insight interview templates │
│ AMEP Feature: Guided brainstorming templates │
├──────────────────────────────────────────────────────────┤
│ Stage 2: DEFINE │
│ • Project persona creation │
│ • Needs statement generator │
│ • SMART goal setting wizard │
│ • Role & responsibility designation │
│ AMEP Feature: Team formation tool (BR9) │
├──────────────────────────────────────────────────────────┤
│ Stage 3: RESEARCH │
│ • Resource library integration │
│ • Citation management │
│ • Knowledge sharing space │
│ AMEP Feature: Collaborative document editing │
├──────────────────────────────────────────────────────────┤
│ Stage 4: CREATE & IMPROVE │
│ • Prototyping workspace │
│ • Version control for artifacts │
│ • Peer feedback collection │
│ AMEP Feature: Milestone tracking with Gantt chart (BR9) │
├──────────────────────────────────────────────────────────┤
│ Stage 5: PRESENT & EVALUATE │
│ • Presentation upload │
│ • Multi-stakeholder evaluation │
│ • Reflection journal │
│ AMEP Feature: Artifact submission portal (BR9) │
└──────────────────────────────────────────────────────────┘
Solves BR5: Standardized, objective soft-skill assessment Source: Paper 11.pdf - "Team Effectiveness in PBL Settings"
4-DIMENSIONAL TEAM EFFECTIVENESS MODEL (BR5):
┌────────────────────────────────────────────────────────────┐
│ Validated Framework with Cronbach α = 0.972 - 0.980 │
│ (High reliability = objective, consistent measurement) │
├────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌─────────────────┐ │
│ │ TEAM DYNAMICS │◀───────▶│ TEAM STRUCTURE │ │
│ │ (TD) │ r=0.93 │ (TS) │ │
│ │ │ │ │ │
│ │ • Communication │ │ • Clear roles │ │
│ │ • Mutual support│ │ • Task scheduling│ │
│ │ • Trust building│ │ • Decision-making│ │
│ │ • Active │ │ • Conflict │ │
│ │ listening │ │ resolution │ │
│ └────────┬────────┘ └────────┬────────┘ │
│ │ r=0.91 │ r=0.92 │
│ ▼ ▼ │
│ ┌─────────────────┐ ┌─────────────────┐ │
│ │ TEAM MOTIVATION │◀───────▶│ TEAM EXCELLENCE │ │
│ │ (TM) │ r=0.82 │ (TE) │ │
│ │ │ │ │ │
│ │ • Clear purpose │ │ • Growth mindset│ │
│ │ • SMART goals │ │ • Quality work │ │
│ │ • Passion & │ │ • Self-monitoring│ │
│ │ dedication │ │ • Reflective │ │
│ │ • Synergy │ │ practice │ │
│ └─────────────────┘ └─────────────────┘ │
│ │
│ "All dimensions positively and significantly correlated │
│ at 95% confidence level" - Paper 11.pdf │
└────────────────────────────────────────────────────────────┘
Solves BR5: Peer-review inputs for soft skill assessment Source: Paper 11.pdf
PEER REVIEW RUBRIC (5-point Likert Scale):
┌─────────────────────────────────────────────────────────────┐
│ Rate your teammate on each dimension (1=Strongly Disagree │
│ to 5=Strongly Agree): │
├─────────────────────────────────────────────────────────────┤
│ TEAM DYNAMICS: │
│ □ "This teammate communicates openly and clearly" │
│ □ "This teammate actively listens to others' ideas" │
│ □ "This teammate supports other team members" │
├─────────────────────────────────────────────────────────────┤
│ TEAM STRUCTURE: │
│ □ "This teammate completes assigned tasks on time" │
│ □ "This teammate takes responsibility for their role" │
│ □ "This teammate helps resolve conflicts constructively" │
├─────────────────────────────────────────────────────────────┤
│ TEAM MOTIVATION: │
│ □ "This teammate shows enthusiasm for the project" │
│ □ "This teammate contributes innovative ideas" │
│ □ "This teammate stays focused on team goals" │
├─────────────────────────────────────────────────────────────┤
│ TEAM EXCELLENCE: │
│ □ "This teammate produces high-quality work" │
│ □ "This teammate reflects on and improves their approach" │
│ □ "This teammate helps the team exceed expectations" │
└─────────────────────────────────────────────────────────────┘
Solves BR5: Progress visualization for soft skills Source: Paper 11.pdf, 10.pdf
SOFT SKILLS PROGRESS DASHBOARD (BR5):
┌─────────────────────────────────────────────────────────────┐
│ TEAM ALPHA - Soft Skills Assessment │
├─────────────────────────────────────────────────────────────┤
│ TEAM DYNAMICS (TD): ████████████████ 4.2/5.0 │
│ TEAM STRUCTURE (TS): ██████████████ 3.8/5.0 │
│ TEAM MOTIVATION (TM): ████████████████████ 4.5/5.0 │
│ TEAM EXCELLENCE (TE): ████████████████ 4.0/5.0 │
├─────────────────────────────────────────────────────────────┤
│ INDIVIDUAL SCORES: │
│ Student A: Communication ████████████████ 4.3 │
│ Student B: Leadership ██████████████████ 4.6 │
│ Student C: Creativity ████████████ 3.5 ⚠️ Needs Growth │
│ Student D: Collaboration ██████████████████████ 4.8 │
├─────────────────────────────────────────────────────────────┤
│ TREND: Week 1 → Week 4 │
│ TD: 3.2 → 3.8 → 4.0 → 4.2 📈 Improving │
│ TS: 3.0 → 3.2 → 3.5 → 3.8 📈 Improving │
└─────────────────────────────────────────────────────────────┘
Solves BR5: Identifies teams at risk of underperformance Source: Paper 10.pdf - "Teamwork Performance Prediction Using Soft Skills"
PREDICTION MODELS (BR5 Enhancement):
┌───────────────────────────────────────────────────────────┐
│ TSS Model (Technological Savvy Skills) │
│ • Programming/Technical skills │
│ • Logical skills │
│ • Creativity skills │
│ │
│ SSM Model (Soft Skills Model) │
│ • Leadership skills │
│ • Communication skills │
│ • Logical skills │
├───────────────────────────────────────────────────────────┤
│ Bayesian Classification for Prediction: │
│ P(Sk|x) = P(Sk) × P(x|Sk) / P(x) │
│ │
│ RESEARCH FINDING: │
│ "Team members with good leadership and communication │
│ skills can maximize the project team's soft skills" │
│ - Paper 10.pdf │
│ │
│ Precision/Recall: >70% for identifying high performers │
└───────────────────────────────────────────────────────────┘
| Existing Solutions | AMEP Advantage | Problem Solved | Source |
|---|---|---|---|
| Generic project management | Education-specific 5-stage PBL workflow | BR9: Structured execution | 17.pdf |
| Subjective peer reviews | Validated 4-dimension framework (α > 0.97) | BR5: Objective assessment | 11.pdf |
| No progress tracking | Soft skills trend visualization over time | BR5: Progress dashboards | 11.pdf |
| Manual milestone tracking | Automated Gantt charts | BR9: Task management | 17.pdf |
| No artifact management | Structured submission portal with version control | BR9: Artifact submission | 17.pdf |
| No early warning | Bayesian prediction identifies at-risk teams | BR5: Proactive intervention | 10.pdf |
Problem Statement :
"Increasing teacher workload... fragmented faculty task management" "Eliminating dependency on fragmented tools and reports"
Specific Challenges:
- ❌ Teachers spend 3+ hours/week on lesson planning
- ❌ Data scattered across 5-10 different tools
- ❌ 6 data drops per year create excessive workload
- ❌ No consolidated view of key metrics
- ❌ Individual planning duplicates effort
A teacher productivity system that:
- ✅ Provides searchable repository of curriculum-aligned templates (BR7)
- ✅ Offers ready-to-use project briefs and assessment frameworks (BR7)
- ✅ Consolidates Mastery Rate, Adoption Rate, Confidence Score in one view (BR8)
- ✅ Eliminates fragmented tools through single platform (BR8)
- ✅ Reduces data drops from 6 to 3 per year (BR7, BR8)
Solves BR7: Ready-to-use content reduces planning time Source: Paper 14.pdf, 15.pdf
TEMPLATE REPOSITORY (BR7: Workload Reduction):
┌─────────────────────────────────────────────────────────────┐
│ 🔍 Search Templates: [____________________] [Search] │
│ │
│ Filter by: [Grade Level ▼] [Subject ▼] [Project Type ▼] │
├─────────────────────────────────────────────────────────────┤
│ CURRICULUM-ALIGNED PROJECT TEMPLATES: │
│ │
│ 📁 Science - Grade 7 │
│ ├── Ecosystem Investigation Project │
│ │ • Learning objectives pre-mapped │
│ │ • Assessment rubric included │
│ │ • Estimated time: 3 weeks │
│ │ • Soft skills targeted: Collaboration, Research │
│ ├── Climate Change Data Analysis │
│ └── Renewable Energy Design Challenge │
│ │
│ 📁 Math - Grade 8 │
│ ├── Statistics in Sports Project │
│ ├── Geometry Architecture Challenge │
│ └── Financial Literacy Simulation │
│ │
│ 📁 English - Grade 9 │
│ ├── Journalism & Media Literacy │
│ ├── Podcast Creation Project │
│ └── Persuasive Campaign Design │
└─────────────────────────────────────────────────────────────┘
RESEARCH SUPPORT:
"Where work plans included appropriate and stimulating
curriculum content, challenging questions, key vocabulary,
engaging activities and resource ideas, this led to an
overall reduction in teacher workload around planning"
- Paper 14.pdf (Meads Teaching School report)
"Centrally developed unit plans would be welcomed by teachers
and save them approximately three hours a week"
- Paper 15.pdf
Solves BR7: Shared resources eliminate duplicate effort Source: Paper 14.pdf - "Workload Challenge Research Projects"
COLLABORATIVE PLANNING MODEL (BR7):
┌─────────────────────────────────────────────────────────────┐
│ TRANSFORM TRUST MODEL (from research): │
│ │
│ School A Teachers ──┐ │
│ School B Teachers ──┼──▶ Year Group Planning Hub │
│ School C Teachers ──┘ │ │
│ ▼ │
│ ┌────────────────────┐ │
│ │ SHARED OUTPUTS: │ │
│ ├────────────────────┤ │
│ │ • Lesson plans │ │
│ │ • Resource materials│ │
│ │ • Assessment items │ │
│ │ • Best practices │ │
│ └────────────────────┘ │
│ │
│ RESEARCH RESULTS: │
│ ✓ "Improved teacher subject knowledge" │
│ ✓ "Production of high quality planning" │
│ ✓ "Reduction in teacher workload around planning" │
│ - Paper 14.pdf (Transform Trust Teaching School Alliance) │
└─────────────────────────────────────────────────────────────┘
Solves BR8: Consolidated view of institutional metrics Source: Paper 12.pdf, 13.pdf, 16.pdf
UNIFIED ANALYTICS DASHBOARD (BR8: Consolidated Reporting):
┌─────────────────────────────────────────────────────────────┐
│ AMEP ANALYTICS HUB │
│ Eliminating dependency on fragmented tools (BR8) │
├─────────────┬─────────────┬─────────────────────────────────┤
│ MASTERY │ TEACHER │ ADMINISTRATIVE │
│ RATE │ ADOPTION │ CONFIDENCE SCORE │
│ │ RATE │ │
│ ████ 78% │ ████ 92% │ ████ 94% │
│ (class avg) │ (platform │ (data completeness │
│ │ usage) │ & reliability) │
├─────────────┴─────────────┴─────────────────────────────────┤
│ DATA COLLECTION STATUS: │
│ │
│ BEFORE AMEP: AFTER AMEP: │
│ • 6 data drops/year • 3 data drops/year (50% reduction) │
│ • Multiple entry pts • Single entry, multiple uses │
│ • Fragmented reports • Unified dashboard │
│ • Manual calculation • Automated analytics │
│ │
│ "Data could be entered once and then used multiple times │
│ at class, school and MAT levels" - Paper 14.pdf │
├─────────────────────────────────────────────────────────────┤
│ CONCEPT MASTERY HEATMAP (from BR1 data): │
│ Topic 1: ████████████████████ 92% │
│ Topic 2: ████████████████ 80% │
│ Topic 3: ██████████ 55% ⚠️ Needs Review │
│ Topic 4: ████████████████████ 95% │
├─────────────────────────────────────────────────────────────┤
│ ENGAGEMENT TRENDS (from BR4/BR6 data): │
│ Week 1: ████████████ 72% │
│ Week 2: ██████████████ 78% │
│ Week 3: ████████████████ 85% 📈 Improving │
├─────────────────────────────────────────────────────────────┤
│ PBL PROJECT STATUS (from BR9 data): │
│ Active Projects: 12 | On Track: 9 | At Risk: 3 │
└─────────────────────────────────────────────────────────────┘
Solves BR7 & BR8: Reduced data drops, single entry Source: Paper 16.pdf - "Teacher Workload and Target Setting"
DATA ENTRY REDUCTION STRATEGY:
┌─────────────────────────────────────────────────────────────┐
│ BEFORE AMEP: │
│ • 6 data drops per year (every 6-7 weeks) │
│ • Teachers enter same data in multiple systems │
│ • Manual report generation for each stakeholder │
│ • "Half of teachers reported no good practice being │
│ actioned in relation to target setting" - Paper 16.pdf │
├─────────────────────────────────────────────────────────────┤
│ AFTER AMEP: │
│ • 3 data drops per year (50% reduction) │
│ • "Enter once, use everywhere" architecture │
│ • Auto-generated reports for all stakeholders │
│ │
│ RESEARCH SUPPORT: │
│ "More than one third of all participants (24 of 60) said │
│ that there were fewer data drops to complete, most commonly │
│ reducing from six or four to three" │
│ - Paper 16.pdf │
│ │
│ "The extension of time between data drops allowed for a │
│ clear indication of the level of progress being made by │
│ pupils as a result of interventions" │
│ - Paper 16.pdf │
└─────────────────────────────────────────────────────────────┘
| Existing Solutions | AMEP Advantage | Problem Solved | Source |
|---|---|---|---|
| Multiple disconnected tools | Single unified dashboard | BR8: Unified reporting | 12.pdf |
| Manual report generation | Automated real-time analytics | BR8: Consolidated metrics | 13.pdf |
| Individual lesson planning | Collaborative networks + shared templates | BR7: Reduced planning time | 14.pdf |
| 6 data drops/year | 3 data drops (50% reduction) | BR7: Less data entry | 16.pdf |
| Generic templates | Curriculum-aligned, ready-to-use content | BR7: Quality + speed | 15.pdf |
| Separate metrics systems | Mastery Rate + Adoption Rate + Confidence Score in one view | BR8: Complete picture | 12.pdf |
| BR# | Requirement | AMEP Solution | Key Tech | Research |
|---|---|---|---|---|
| BR1 | Personalized Concept Mastery (0-100 scoring) | Hybrid KT: BKT + DKT (LSTM) + DKVMN | TensorFlow/PyTorch | 2105_15106v4.pdf |
| BR2 | Adaptive Practice Delivery (ZPD targeting) | Cognitive load algorithm + difficulty adjustment | Reinforcement learning | 6.pdf |
| BR3 | Efficiency of Practice (reduce repetition) | Memory-aware tracking identifies mastered vs. weak | DKVMN | 4.pdf |
| BR4 | Inclusive Engagement Capture (100% visibility) | Anonymous polling + implicit behavior analytics | WebSockets + ML | 8h.pdf, 6.pdf |
| BR5 | Objective Soft-Skill Assessment | 4-dimension framework (TD, TS, TM, TE) + peer review | Statistical validation | 11.pdf, 10.pdf |
| BR6 | Actionable Teacher Feedback (real-time) | Live dashboard with engagement index + alerts | Real-time analytics | 62379RAE2024_11.pdf |
| BR7 | Workload Reduction (templates, less data entry) | Template repository + 3 data drops (from 6) | Content management | 14.pdf, 15.pdf, 16.pdf |
| BR8 | Unified Data Reporting (consolidated metrics) | Single dashboard: Mastery, Adoption, Confidence | Data integration | 12.pdf, 13.pdf |
| BR9 | Centralized PBL Workspace | 5-stage workflow + team tools + artifact submission | Project management | 17.pdf |
checked by peer-reviewed research papers*