Agent 1: Login Monitoring
Active

This agent monitors student login activity and automatically sends notifications when students haven't accessed the course for a specified period. It helps instructors identify at-risk students early and maintain engagement.

Monitoring Settings
How long before a student is considered inactive
Notification Recipients
Send summary reports to all course instructors
Send reminder messages directly to inactive students
Include TAs in notification reports
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {days}, {student_list} as placeholders
Use {student_name}, {course_name}, {days} as placeholders
Agent 2: Assignment Submission Monitoring
Active

This agent monitors assignment deadlines and automatically sends notifications when students miss submission deadlines. It helps instructors track overdue assignments and assists students in staying on top of their coursework.

Monitoring Settings
When to check and send notifications after deadline passes
Which types of assignments to monitor
Notification Recipients
Send summary of missed submissions to instructors
Send reminders to students who missed deadlines
Include TAs in missed submission reports
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {assignment_name}, {due_date}, {student_list}, {total_students}, {submitted_count}, {missing_count} as placeholders
Use {student_name}, {assignment_name}, {course_name}, {due_date} as placeholders
Agent 3: Quiz Performance Monitoring
Active

This agent monitors quiz performance and attempt patterns, sending notifications when students score below threshold or take excessive attempts. It helps instructors identify struggling students and provide timely academic support.

Performance Thresholds
Trigger notification when student scores at or below this percentage
Trigger notification when student exceeds this number of attempts
Which types of quizzes to monitor for performance issues
Notification Recipients
Send performance alerts to instructors
Send study recommendations to struggling students
Include TAs in performance reports
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {quiz_name}, {quiz_date}, {score_threshold}, {attempt_threshold}, {low_score_students}, {high_attempt_students} as placeholders
Use {student_name}, {quiz_name}, {course_name}, {office_hours} as placeholders
Agent 4: Grade Drop Detection
Active

This agent tracks grade progression over time and alerts when students show significant performance declines that may indicate personal issues, increased difficulty, or disengagement.

Detection Thresholds
Minimum percentage drop to trigger alert
Time period to analyze for grade trends
Minimum graded assignments needed for trend analysis
Notification Recipients
Alert instructors about declining students
Send support messages to affected students
Include academic advisors in alerts
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {student_name}, {decline_percentage}, {time_period}, {previous_average}, {current_average}, {affected_assignments} as placeholders
Use {student_name}, {course_name}, {office_hours}, {tutoring_info}, {support_contact} as placeholders
Agent 5: Course Engagement Monitoring
Active

This agent monitors student login frequency, time spent in course, and interaction with course materials to identify students who may be struggling with course engagement or facing technical barriers.

Engagement Thresholds
Minimum time students should spend in course weekly
Days of no course activity before sending alert
How far below class average before triggering alert
Activity Settings
Count weekend logins in weekly totals
Include mobile app activity in calculations
Notification Recipients
Alert about low engagement patterns
Send engagement reminder messages
Alert for possible technical issues
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {low_engagement_students}, {class_average_time}, {below_threshold_count}, {inactive_count} as placeholders
Use {student_name}, {course_name}, {last_login}, {weekly_minutes}, {class_average}, {tech_support}, {academic_support} as placeholders
Agent 6: At-Risk Student Prediction
Active

This agent uses a composite score based on login patterns, assignment completion rates, grade trends, and engagement metrics to identify students at high risk of dropping or failing the course.

Risk Factor Weights
Importance of login frequency and time spent
Importance of grades and assignment completion
Importance of participation and interaction
Risk Level Thresholds
Composite score indicating high drop risk
Composite score indicating moderate drop risk
When to send first intervention alert
Escalation Protocols
For high-risk students after 3 days
For persistent high-risk patterns
Notification Recipients
Alert about at-risk students
Send proactive support messages
Include advisors for high-risk cases
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {high_risk_students}, {medium_risk_students}, {login_score}, {assignment_score}, {engagement_score}, {risk_score} as placeholders
Agent 7: Peer Performance Comparison
Active

This agent compares individual student metrics against class averages and identifies outliers who may need additional support or advanced challenges.

Performance Comparison Settings
How far from class mean before triggering alert
Minimum enrolled students for valid statistical comparison
How often to run peer comparisons
Metrics to Compare
Compare average assignment scores
Compare assignment completion percentages
Compare time spent in course
Compare discussion posts and replies
Compare quiz scores and attempts
Compare login patterns and consistency
Statistical Settings
Remove exceptional performers from baseline
Remove struggling students from baseline
Notification Recipients
Alert about performance outliers
Send support messages to struggling students
Offer advanced challenges to top students
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {below_average_students}, {above_average_students}, {class_average}, {std_deviation}, {median_score} as placeholders
Agent 8: Discussion Forum Participation Monitor
Active

This agent tracks student participation in discussion forums, identifying students who never post, rarely engage, or only make minimal contributions. Active discussion participation often correlates strongly with course success.

Participation Thresholds
Expected minimum discussion contributions
Minimum word count to qualify as substantive post
Time period before alerting about zero participation
Quality Engagement Settings
Monitor if students reply to peers, not just post
Identify students who initiate discussions
Notification Recipients
Alert about low participation patterns
Encourage non-participating students
Include TAs in participation reports
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {zero_participation_students}, {low_participation_students}, {required_posts}, {average_posts}, {meeting_requirement} as placeholders
Use {student_name}, {course_name}, {your_posts}, {expected_posts}, {active_topics} as placeholders
Agent 9: Content Consumption Pattern Analyzer
Active

This agent analyzes which course materials students actually access (videos, readings, slides) and identifies students who skip important content or show superficial engagement patterns like rapid clicking without actual consumption.

Content Access Settings
Minimum percentage of required content that should be accessed
Minimum percentage of video that should be watched
Minimum time to consider content actually read vs. skipped
Superficial Engagement Detection
Flag students clicking through without engaging
Identify excessive fast-forwarding behavior
Priority Content Tracking
Which content items to monitor most closely
Notification Recipients
Alert about content skipping patterns
Remind students to review missed content
Alert if content consistently skipped
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {low_content_students}, {total_materials}, {average_access}, {skipped_content}, {rapid_click_count}, {low_video_count} as placeholders
Use {student_name}, {course_name}, {missed_content_list}, {accessed_count}, {total_count}, {average_time}, {videos_watched}, {total_videos}, {content_links} as placeholders
Agent 10: Early Warning Deadline Reminder
Active

This agent proactively reminds students about upcoming deadlines before they're due, with escalating reminders for students who haven't started assignments. Prevention is better than cure for deadline management.

Reminder Schedule
When to send the initial deadline reminder
When to send the follow-up reminder
Last chance reminder before deadline
Smart Reminder Logic
Skip reminders if student already submitted
Send more frequent reminders if not started
Combine reminders for same-day deadlines
Send reminders at appropriate local times
Notification Channels
Send via email
Send via Canvas inbox
Send text messages for urgent deadlines
Notification Recipients
Send deadline reminders to students
Alert instructors about unstarted work
Include parents for critical deadlines
Message Templates
Use {student_name}, {course_name}, {assignment_name}, {due_date}, {due_time}, {days_remaining}, {assignment_link} as placeholders
Use {student_name}, {course_name}, {assignment_name}, {due_date}, {due_time}, {hours_remaining}, {office_hours}, {assignment_link} as placeholders
Agent 11: Consistent Struggle Pattern Detector
Active

This agent identifies students who consistently struggle with specific topics, question types, or assignment categories by analyzing error patterns across multiple assessments. It enables targeted intervention and personalized support.

Pattern Detection Settings
How many repeated struggles before triggering alert
Score threshold for identifying topic struggles
How deep to analyze performance patterns
Analytics Categories
Track struggles with specific course topics
Identify struggles with multiple choice, essays, etc.
Map to course learning outcomes
Track analytical, computational, conceptual skills
Automated Intervention Suggestions
Recommend specific materials for weak topics
Link to additional practice for weak areas
Notification Recipients
Alert about consistent struggle patterns
Provide personalized learning recommendations
Connect students with specialized tutoring
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {student_name}, {struggle_topics}, {topic_name}, {topic_average}, {attempt_count}, {pattern_type}, {resource_links} as placeholders
Use {student_name}, {course_name}, {struggle_areas}, {topic_name}, {current_average}, {class_average}, {recommended_materials}, {practice_problems}, {office_hours}, {tutoring_info} as placeholders
Agent 12: Sudden Disengagement Alert
Active

This agent detects dramatic behavioral changes in previously engaged students - sudden drops in login frequency, participation, or assignment completion. Rapid disengagement often signals personal crises requiring immediate intervention.

Baseline Establishment
Period to establish student's normal engagement pattern
Only track students who were initially engaged
Disengagement Detection Thresholds
Percentage drop in activity to trigger alert
Time period to detect sudden change
Complete absence triggers immediate alert
Multi-Factor Disengagement Indicators
Monitor sudden decrease in logins
Monitor sudden stop in discussions
Monitor missed assignments after consistent completion
Monitor reduction in time spent per session
Priority Escalation Protocol
Notify instructor within 1 hour of detection
Alert student support services for severe cases
Notification Recipients
Immediate alert to teaching staff
Alert student's academic advisor
Send caring check-in message
Message Templates
Use {instructor_name}, {course_name}, {section_name}, {student_name}, {baseline_engagement}, {current_engagement}, {change_percentage}, {detection_window}, {login_comparison}, {participation_comparison}, {assignment_comparison}, {last_activity_date} as placeholders
Use {student_name}, {course_name}, {instructor_email}, {office_hours}, {support_services}, {instructor_name} as placeholders