The document details the development of McGraw-Hill Education's Learning Analytics Platform (LAP), which utilizes MongoDB for real-time educational event storage and analytics. It outlines the challenges faced in schema design and data flow, emphasizing the platform's need for high performance and atomicity in data processing. The final MongoDB schema and infrastructure are designed to handle a significant volume of educational data efficiently while providing insights to students and educators.

1. MongoDB and the
McGraw-Hill Education
Learning Analytics Platform:
Towards an Open, Scalable,
Streaming Solution for
Education
MongoDB World 2015

2. 2
Outline
• McGraw-Hill Education
• My background
• The MHE Learning Analytics Platform (LAP)
• Standardized educational input events
• MongoDB schema design
• Server infrastructure
• Performance
• Conclusions

3. 3
• Global Company with over 5,000 employees
• Now a Learning Science Company
• All content available digitally by Fall 2015
• Higher Ed system is Connect
• K-12 LMS is Engrade
• Adaptive systems LearnSmart, SmartBook
and ALEKS
McGraw-Hill Education

4. 4
• Global and Marine Seismologist
• Small College Physics Professor
• Oracle Database Administrator
• Head of IT Operations at MIT Sloan School
of Management
• Head of MHE Digital Platform Group’s
Analytics team’s Data Science group
• Systems Engineer on this project
My Background

5. 5
Motivation
 MHE has several digital educational platforms
including Connect for Higher Ed and Engrade for K-
12
 Instrument platforms to send student/educator
events in real time to a central system (LAP)
 Ingest and store education events in data store
(MongoDB)
 Analytics provides “insights” to students/educators
Introduction to LAP

6. 6
Demo Connect Insight for Students (CIS)
Introduction to the LAP

7. 7
Standardized education events (Caliper)
 Utilizes JSON-LD (linked data) format
 Caliper uses Actor - Verb - Object tuple to
form learning events (ex: student – submit –
test)
 Triggered from student/educator activity and
sent to LAP input API
IMS Caliper Format for
Education

8. 8
LAP Architectural Design
LAP
Connect
Caliper
Event
Engrade
Caliper
Event
Other
Caliper
Events

9. 9
LAP Architectural Design
Collection
Collection
Receiver
LAP
Ingestion
API
Connect
Caliper
Event
Engrade
Caliper
Event
Other
Caliper
Events

10. 10
LAP Architectural Design
Connect
Caliper
Event
Collection
Collection
Receiver
Engrade
Caliper
Event
Other
Caliper
Events
LAP
Ingestion
API
Long-term
Storage
SQS

11. 11
LAP Architectural Design
Collection
Collection
Worker
LAP
Ingestion
API
Long-term
Storage
SQS
MongoDB
Data Store
Connect
Caliper
Event
Engrade
Caliper
Event
Other
Caliper
Events

12. 12
LAP Architectural Design
Collection
Collection
Worker
LAP
Ingestion
API
Long-term
Storage
SQS
MongoDB
Data Store
Results/
AnalysisResults/
Analysis
Output
API
Results/
Analysis
Connect
Caliper
Event
Engrade
Caliper
Event
Other
Caliper
Events

13. 13
LAP Architectural Design
Collection
Collection
Worker
LAP
Ingestion
API
Long-term
Storage
SQS
MongoDB
Data Store
Results/
AnalysisResults/
Analysis
Output
API
Results/
Analysis
Results/
AnalysisResults/
Analysis
Connect
Insight for
Students
Engrade
Insight for
Teachers
Future
Insights
Insight
Output
API
Results/
Analysis
Connect
Caliper
Event
Engrade
Caliper
Event
Other
Caliper
Events

14. 14
• JSON-LD input suggested a document store
• MongoDB accessible and well documented
• Provided needed performance and capacity
• Support from MongoDB Inc. (10Gen)
• Six Month Development Support contract
• Dedicated consultants
• Ongoing support contract
Why MongoDB?

15. 15
Standardized education events (Caliper)
 Caliper (JSON-LD) produced by triggers in
the Connect Oracle database
 Triggered from student/educator activity and
sent to LAP input API
 LAP then verifies input, transforms into
MongoDB schema, calculates aggregates,
and sends to data visualizations
Data Flow Through the LAP

16. 16
Data Flow Through the LAP
Standardized education events (Caliper
examples)
1. Assessment Created
2. Assessment Attempt Started
3. Assessment Attempt Submitted
4. Assessment Attempt Graded
An assessment is an on-line homework assignment, quiz or test
associated with a McGraw-Hill digital textbook.

17. {
"@context": " http://purl.imsglobal.org/ctx/caliper/v1/AssessmentEvent",
"@type" : "AssessmentEvent",
"@id" : "mhe-caliper:connect-000/eventId/ea7db7cf-2ed9-43a3-b9d4-1472265157c5",
"generatedAtTime" : "2012-11-01T08:00:01",
"verb" : "created",
"actor" : {
"@id" : "mhe-caliper:connect-000/actor/MjYwMTM4Nzc3Nw==",
"@type" : "instructor"
},
"startedAtTime" : "2012-11-01T08:00:00",
"object" : {
"@id" : "mhe-caliper:connect-000/assessment/MjYwMTM4Nzc4Mg==",
"@type": "assessment",
"category" : "homework",
"origin" : "ASSESSMENT",
"topics" : ["addition", "subtraction"],
"maxSubmissionsAllowed" : 3,
"maxTimeAllowed" : 10800,
"maxOutcomePossible" : 100.0,
"startDate" : "2012-11-01T04:00:00",
"dueDate" : "2012-11-05T08:00:00",
"assessmentName" : "Sample Assignment Zero",
"noFeedback" : false,
"ALEDisplayName" : "Critical Missions",
"attemptDeductions" : false,
"lateSubmissionDeduction" : true,
"studyAttempts" : true,
"forceSubmission" : true
},
"learningContext" : {
"enrolledIn" : "section",
"course" : "mhe-caliper:connect-000/course/MjYwMTM4Nzc4NQ==",
"section" : "mhe-caliper:connect-000/section/MjYwMDU1MTc0Ng==",
"courseName" : "Intro to Algebra",
"sectionName" : "MWF 10-11",
"timeZone" : "America/New_York"
}
}
Assessment Created
Caliper Event

18. Assessment Attempt Started
Caliper Event
{
"@context" : "http://purl.imsglobal.org/ctx/caliper/v1/AssessmentEvent",
"@type" : "AssessmentEvent",
"@id" : "mhe-caliper:connect-000/eventId/87231361-6c9c-4ef6-8ea3-49e39e78eb4d",
"generatedAtTime" : "2012-11-01T08:00:01",
"verb" : "started",
"actor" : {
"@id" : "mhe-caliper:connect-000/actor/MjYwMTM4Nzc4MQ==",
"@type" : "student"
},
"startedAtTime" : "2012-11-04T11:00:00",
"object" : {
"@id" : "mhe-caliper:connect-000/assessment/MjYwMTM4Nzc4Mg==",
"@type": "assessment"
},
"learningContext" : {
"enrolledIn" : "section",
"course" : "mhe-caliper:connect-000/course/MjYwMTM4Nzc4NQ==",
"section" : "mhe-caliper:connect-000/section/MjYwMDU1MTc0Ng==",
"courseName" : "Intro to Algebra",
"sectionName" : "MWF 10-11",
"timeZone" : "America/New_York"
},
"attemptCount" : 1
}

19. Assessment Attempt Submitted
Caliper Event
{
"@context" : "http://purl.imsglobal.org/ctx/caliper/v1/AssessmentEvent",
"@type" : "AssessmentEvent",
"@id" : "mhe-caliper:connect-000/eventId/b1db77ea-44a7-4f99-a819-e8b7e142f457",
"generatedAtTime" : "2012-11-01T08:00:01",
"verb" : "submitted",
"actor" : {
"@id" : "mhe-caliper:connect-000/actor/MjYwMTM4Nzc4MQ==",
"@type" : "student"
},
"startedAtTime" : "2012-11-04T11:45:01",
"object" : {
"@id" : "mhe-caliper:connect-000/assessment/MjYwMTM4Nzc4Mg==",
"@type" : "assessment",
"maxSubmissionsAllowed" : 3,
"maxTimeAllowed" : 10800,
"dueDate" : "2012-11-08T12:00:00"
},
"learningContext" : {
"enrolledIn" : "section",
"course" : "mhe-caliper:connect-000/course/MjYwMTM4Nzc4NQ==",
"section" : "mhe-caliper:connect-000/section/MjYwMDU1MTc0Ng==",
"courseName" : "Intro to Algebra",
"sectionName" : "MWF 10-11",
"timeZone" : "America/New_York",
},
"attemptCount" : 1,
"timeTaken" : 1800
}

20. Assessment Attempt Graded
Caliper Event
{
"@context" : "http://mheducation.com/mhe-caliper/v1/OutcomeEvent",
"@type" : "OutcomeEvent",
"@id" : "mhe-caliper:connect-000/eventId/d7c28248-8e14-495e-9a5c-bb1cc1e0882d",
"generatedAtTime" : "2012-11-01T08:00:01",
"verb" : "graded",
"actor" : {
"@id" : "mhe-caliper:connect-000/actor/MjYwMTM4Nzc3Nw==",
"@type" : "instructor"
},
"startedAtTime" : "2012-11-04T19:00:00",
"object" : {
"@id" : "mhe-caliper:connect-000/assessment/MjYwMTM4Nzc4Mg==",
"@type": "assessment",
"student" : "mhe-caliper:connect-000/actor/MjYwMTM4Nzc4MQ=="
},
"learningContext" : {
"enrolledIn" : "section",
"course" : "mhe-caliper:connect-000/course/MjYwMTM4Nzc4NQ==",
"section" : "mhe-caliper:connect-000/section/MjYwMDU1MTc0Ng==",
"courseName" : "Intro to Algebra",
"sectionName" : "MWF 10-11",
"timeZone" : "America/New_York"
},
"attemptCount" : 1,
"outcome" : .85,
"percentDeducted" : 5
}

21. 21
Constraints on developing schema
 Several learning activities require multiple
Caliper events
• Example: student starts, submits, and is
graded to complete a quiz
 No guarantee that external applications will
send events in chronological order
 May receive duplicate events
Data Flow Through the LAP

22. 22
MongoDB Schema – Version
0.1
V0.1
 2 schema model (student and
class)
Class Collection describes the
class, section and assignments
Student Collection
• Assessment array updated when
attempt is complete
• All events for an activity
• Attempts for each activity in a sub-array

23. 23
V0.1
 Problems
• Too embedded
• Difficult to update a student doc
• Query-logic-update
MongoDB Schema

24. 24
MongoDB Schema Version 2
• Remove nested arrays
• Move attempts doc up to the top level

25. V0.2
 Problems
• Still have query-logic-update
• Difficult to do atomically and maintain
deterministic state
25
MongoDB Schema Version 2

26. 26
MongoDB Schema Version 3
{}
{}
• Remove arrays altogether
• Replace arrays with assessment and attempt docs,
each of which contains several sub-docs

27. V0.3
 Atomic updates now much easier
 Save raw Caliper event in event collection
 Only update student collection if all required events
are in event collection
27
MongoDB Schema Version 0.3

28. 28
Query Utilization
• 3 basic queries to build visualization for CIS
• All student docs for current class
• All student docs for current student
• Class doc for current class
• All queries are on indexed parameters
• Student doc _id = class_id:student_id
• Class doc _id = class_id

29. 29
Infrastructure
• All servers and storage is in AWS
• Backups done using EBS snapshots
• DB size estimated to grow about 500 [GB/year]
• Data size estimate small enough for un-sharded
cluster
• 3 member replica sets
• Write to primary, read from primary and secondary's

30. 30
Performance
• Estimated peak load 100 [events/sec] = 100 [kB/sec]
• Average load of 1,500,000 events/day
• Max of 2,500,000 events per day
• Initially planned on sharded, replicated cluster but for
now do not need this
• Added SQS Queue to handle periods of very high
load
• Upgraded from MongoDB 2.6 to 3.0 (~ x10 faster)

31. 31
Conclusions
• We have a learning analytics platform in
production utilizing a MongoDB data store
• After several iterations we developed a
MongoDB schema which:
• Handles data coming in arbitrary order
with duplicates
• Performs one step, atomic inserts
• Has high performance during peak loads