| name | education-expert |
| version | 1.0.0 |
| description | Expert-level education technology, learning management systems, and ed-tech platforms |
| category | domains |
| tags | ["education","edtech","lms","e-learning","assessment"] |
| allowed-tools | ["Read","Write","Edit"] |
Education Expert
Expert guidance for education technology, learning management systems, online learning platforms, and educational software development.
Core Concepts
Educational Technology
- Learning Management Systems (LMS)
- Student Information Systems (SIS)
- Assessment and evaluation tools
- Adaptive learning platforms
- Virtual classrooms
- Content management
Standards
- SCORM (Sharable Content Object Reference Model)
- xAPI (Experience API / Tin Can API)
- LTI (Learning Tools Interoperability)
- QTI (Question and Test Interoperability)
- Accessibility (WCAG, Section 508)
Key Features
- Course management
- Grade tracking
- Student analytics
- Content delivery
- Collaborative tools
- Assessment engines
LMS Core Implementation
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
from enum import Enum
class EnrollmentStatus(Enum):
ACTIVE = "active"
COMPLETED = "completed"
DROPPED = "dropped"
PENDING = "pending"
@dataclass
class Course:
course_id: str
title: str
description: str
instructor_id: str
start_date: datetime
end_date: datetime
credits: int
capacity: int
syllabus_url: str
prerequisites: List[str]
@dataclass
class Student:
student_id: str
first_name: str
last_name: str
email: str
enrolled_date: datetime
grade_level: str
gpa: float
@dataclass
class Enrollment:
enrollment_id: str
student_id: str
course_id: str
enrollment_date: datetime
status: EnrollmentStatus
final_grade: Optional[float]
class LMSPlatform:
():
.db = db
():
course = .db.get_course(course_id)
current_enrollment = .db.count_enrollments(course_id)
current_enrollment >= course.capacity:
Exception()
course.prerequisites:
completed = .get_completed_courses(student_id)
(prereq completed prereq course.prerequisites):
Exception()
enrollment = Enrollment(
enrollment_id=generate_id(),
student_id=student_id,
course_id=course_id,
enrollment_date=datetime.now(),
status=EnrollmentStatus.ACTIVE,
final_grade=
)
.db.save_enrollment(enrollment)
():
enrollments = .db.get_student_enrollments(student_id)
transcript = []
enrollment enrollments:
enrollment.status == EnrollmentStatus.COMPLETED:
course = .db.get_course(enrollment.course_id)
transcript.append({
: course.course_id,
: course.title,
: course.credits,
: enrollment.final_grade,
: .get_term(enrollment.enrollment_date)
})
transcript
():
transcript = .get_student_transcript(student_id)
total_points =
total_credits =
record transcript:
record[] :
total_points += record[] * record[]
total_credits += record[]
total_points / total_credits total_credits >
Assessment Engine
from abc import ABC, abstractmethod
class Question(ABC):
"""Base question class"""
def __init__(self, question_id, text, points):
self.question_id = question_id
self.text = text
self.points = points
@abstractmethod
def check_answer(self, student_answer):
pass
class MultipleChoiceQuestion(Question):
"""Multiple choice question"""
def __init__(self, question_id, text, points, choices, correct_answer):
super().__init__(question_id, text, points)
self.choices = choices
self.correct_answer = correct_answer
def check_answer(self, student_answer):
return student_answer == self.correct_answer
class Essay Question(Question):
"""Essay question requiring manual grading"""
def __init__(self, question_id, text, points, rubric):
super().__init__(question_id, text, points)
self.rubric = rubric
def check_answer(self, student_answer):
:
():
total_points =
earned_points =
results = []
question assessment.questions:
total_points += question.points
student_answer = student_answers.get(question.question_id)
(question, MultipleChoiceQuestion):
is_correct = question.check_answer(student_answer)
points_earned = question.points is_correct
earned_points += points_earned
results.append({
: question.question_id,
: is_correct,
: points_earned,
: is_correct
})
:
results.append({
: question.question_id,
: ,
: student_answer
})
score_percentage = (earned_points / total_points) * total_points >
{
: total_points,
: earned_points,
: score_percentage,
: results
}
():
scores = [s[] s all_submissions]
{
: (scores) / (scores),
: (scores)[(scores) // ],
: (scores),
: (scores),
: .calculate_std_dev(scores)
}
Learning Analytics
class LearningAnalytics:
"""Student learning analytics"""
def get_student_engagement(self, student_id, course_id):
"""Calculate student engagement metrics"""
activities = self.db.get_student_activities(student_id, course_id)
metrics = {
'login_frequency': self.calculate_login_frequency(activities),
'content_completion_rate': self.calculate_completion_rate(activities),
'assessment_participation': self.calculate_participation(activities),
'forum_posts': len([a for a in activities if a['type'] == 'forum_post']),
'time_on_platform': sum(a['duration'] for a in activities)
}
return metrics
def predict_student_risk(self, student_id, course_id):
"""Predict at-risk students"""
engagement = self.get_student_engagement(student_id, course_id)
grades = self.get_recent_grades(student_id, course_id)
risk_score = 0
if engagement['login_frequency'] < 2:
risk_score += 25
if engagement[] < :
risk_score +=
engagement[] < :
risk_score +=
grades (grades) / (grades) < :
risk_score +=
risk_level = risk_score >= risk_score >=
{
: student_id,
: risk_score,
: risk_level,
: .generate_interventions(risk_score, engagement)
}
():
current_skills = .assess_skills(student_id)
gap_analysis = .identify_skill_gaps(current_skills, learning_goals)
recommended_courses = .match_courses_to_goals(gap_analysis)
{
: student_id,
: current_skills,
: learning_goals,
: gap_analysis,
: recommended_courses,
: .estimate_completion_time(recommended_courses)
}
xAPI (Tin Can API) Integration
import json
from datetime import datetime
class xAPIStatement:
"""xAPI statement builder"""
@staticmethod
def create_statement(actor, verb, object_data, result=None):
"""Create xAPI statement"""
statement = {
'actor': {
'objectType': 'Agent',
'name': actor['name'],
'mbox': f"mailto:{actor['email']}"
},
'verb': {
'id': verb['id'],
'display': {'en-US': verb['display']}
},
'object': {
'id': object_data['id'],
'definition': {
'name': {'en-US': object_data['name']},
'description': {'en-US': object_data.get('description', '')}
}
},
'timestamp': datetime.utcnow().isoformat()
}
if result:
statement['result'] = result
return statement
@staticmethod
def completed_course(student, course, score):
xAPIStatement.create_statement(
actor={: student[], : student[]},
verb={
: ,
:
},
object_data={
: ,
: course[]
},
result={
: {
: score / ,
: score,
: ,
:
},
: ,
: score >=
}
)
Best Practices
- Implement accessibility standards (WCAG)
- Support mobile learning
- Enable offline access
- Provide real-time feedback
- Use adaptive learning algorithms
- Protect student data privacy (FERPA)
- Enable collaborative learning
- Provide detailed analytics
- Support multiple content formats
- Implement plagiarism detection
- Enable parent/guardian access
- Support multilingual content
Anti-Patterns
❌ Poor mobile experience
❌ No accessibility features
❌ Ignoring data privacy
❌ No analytics or reporting
❌ Inflexible assessment tools
❌ No offline capabilities
❌ Poor user experience
Resources
