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Using real-time dashboards to
improve student engagement in
virtual learning environments
Robert Bodily, Steven Wood
Brigham Young University
Research questions
Dashboard Development
R1: What technical requirements are needed for an online learning system to
collect and provide students with personalized information in a student
dashboard?
R2: What functionality do students want in a dashboard and how should it be
visually represented?
Data Mining Analysis
R3: What is predictive of student success in a first-year general chemistry
course?
R4: Can we develop an early alert warning system using clickstream data?
R1: Technical Requirements
Challenges with current LMS
• A lot of learning is not occurring within a Learning Management
System (LMS)
• Interoperability standards
• No access to real-time data
• Canvas data (1 day old)
• API (rate limiting factors)
• Do not track enough data
• No information on how students interact with a page
Our analytics system
LTI (Learning Tools Interoperability)
Single sign-on system for learning
applications and learning management
systems
xAPI (Experience API/Tincan API)
Data format specification for data
management interoperability
LRS (Learning Record Store
Database that stores xAPI statements
and provides real-time data access
R2: Functionality
Student Dashboard
Challenges:
Low student use
Initially not useful for
students
Students had too many
things to do already
Low engagement with
feedback
Instructor dashboard
R3: Predictive elements of
student success
Context
• Class
• First year chemistry course
• Blended – class 3x per week,
• Resources
• 150 videos (avg. 2 min long, supplemental resources)
• 15 weekly quizzes (unlimited question attempts)
• Participants
• 200 students (online interactions)
• 96 students took the self-report resource use survey
Data collected
• Quiz
• Confidence in answer (just a guess, pretty sure, very sure)
• Time spent on quiz
• Correct/incorrect
• Number of attempts per question
• Leave tab (still open, but inactive), come back to tab (active again)
• Video
• Play, pause, skip forward/backward, change play rate, change volume,
• Dashboard
• Number of times students follow recommendations given in dashboard
• Number of clicks within the dashboard
What course elements are predictive of
student success?
Variable Beta P-value
Online homework score 0.366 0.000
In-class IClicker scores 0.154 0.024
# of attempts/question -0.411 0.000
Amount of question navigation -0.206 0.040
# of online activity sessions -0.195 0.020
Variable Beta P-value
Read the textbook 2.443 0.059
Ask professor questions in class 7.363 0.000
Watch Khan Academy -2.738 0.051
Use the internet -3.199 0.010
Skip recitation -4.820 0.041
Model 1 – regressing online interaction data
on final exam score.
Model 2 – regressing self-report resource use
on final exam score.
R4: Early alert warning system
Develop an early course prediction of
student achievement
Online student interaction data Online student interaction data AND exam scores
There is significant improvement in both models until week 3 or 4, so that seems to be a good time to make
predictions for instructors and students.
Thank you! Questions?

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Using real-time dashboards to improve student engagement in virtual learning environments

  • 1. Using real-time dashboards to improve student engagement in virtual learning environments Robert Bodily, Steven Wood Brigham Young University
  • 2. Research questions Dashboard Development R1: What technical requirements are needed for an online learning system to collect and provide students with personalized information in a student dashboard? R2: What functionality do students want in a dashboard and how should it be visually represented? Data Mining Analysis R3: What is predictive of student success in a first-year general chemistry course? R4: Can we develop an early alert warning system using clickstream data?
  • 4. Challenges with current LMS • A lot of learning is not occurring within a Learning Management System (LMS) • Interoperability standards • No access to real-time data • Canvas data (1 day old) • API (rate limiting factors) • Do not track enough data • No information on how students interact with a page
  • 5. Our analytics system LTI (Learning Tools Interoperability) Single sign-on system for learning applications and learning management systems xAPI (Experience API/Tincan API) Data format specification for data management interoperability LRS (Learning Record Store Database that stores xAPI statements and provides real-time data access
  • 7. Student Dashboard Challenges: Low student use Initially not useful for students Students had too many things to do already Low engagement with feedback
  • 9. R3: Predictive elements of student success
  • 10. Context • Class • First year chemistry course • Blended – class 3x per week, • Resources • 150 videos (avg. 2 min long, supplemental resources) • 15 weekly quizzes (unlimited question attempts) • Participants • 200 students (online interactions) • 96 students took the self-report resource use survey
  • 11. Data collected • Quiz • Confidence in answer (just a guess, pretty sure, very sure) • Time spent on quiz • Correct/incorrect • Number of attempts per question • Leave tab (still open, but inactive), come back to tab (active again) • Video • Play, pause, skip forward/backward, change play rate, change volume, • Dashboard • Number of times students follow recommendations given in dashboard • Number of clicks within the dashboard
  • 12. What course elements are predictive of student success? Variable Beta P-value Online homework score 0.366 0.000 In-class IClicker scores 0.154 0.024 # of attempts/question -0.411 0.000 Amount of question navigation -0.206 0.040 # of online activity sessions -0.195 0.020 Variable Beta P-value Read the textbook 2.443 0.059 Ask professor questions in class 7.363 0.000 Watch Khan Academy -2.738 0.051 Use the internet -3.199 0.010 Skip recitation -4.820 0.041 Model 1 – regressing online interaction data on final exam score. Model 2 – regressing self-report resource use on final exam score.
  • 13. R4: Early alert warning system
  • 14. Develop an early course prediction of student achievement Online student interaction data Online student interaction data AND exam scores There is significant improvement in both models until week 3 or 4, so that seems to be a good time to make predictions for instructors and students.