AET 2016 Researchers present findings from a series of data mining studies, primarily examining data mining as part of an innovative triangulated approach in program evaluation. Findings suggest that is it possible to apply EDM techniques in online and blended learning classrooms to identify key variables important to the success of learners. Lessons learned will be shared as well as areas for improving data collection in learning management systems for meaningful analysis and visualization.

1. © 2016 Boise State University 1
Kerry Rice, Jui-Long Hung, Yu-Chang Hsu, Brett E. Shelton
Department of Educational Technology
Boise State University
Educational Data Mining in Program
Evaluation: Lessons Learned
AECT 2016, Las Vegas

2. © 2016 Boise State University 2
MET
Ed. D.
Ed. S.
Graduate Certificates:
Online Teaching
Technology Integration Specialist
School Technology Coordinator
K-12 Online Teaching Endorsement
College of Education

3. © 2016 Boise State University 3
Go Broncos!

4. © 2016 Boise State University 4
Decision Tree
Analysis
(performance
prediction)
Cluster Analysis
(engagement)
Sequential
Association
Analysis (path
analysis)
Educational Data Mining Applications
Time Series
Analysis
(future
performance
prediction)

5. © 2016 Boise State University 5
Study #1: Teacher Training Workshops 2010
• Survey Data + Data
Mining + Student
Outcomes
• Research Goal:
– Program
improvement
– Satisfaction
– Impact on practice
• Blackboard
• 103 participants
• 31,417 learning logs
• Cluster Analysis;
Sequential Association
Analysis; Decision Tree
Analysis;

6. © 2016 Boise State University 6
Study #2: Online Graduate Teacher Education 2010
• Data Mining + Student
Outcomes (no demographic
data)
• Research Goal:
– Identify struggling students
– Adjust teaching strategies
– Improve course design
– Data Visualization
• Study Design
– Comparative (between and
within courses)
– Random course selection
• Moodle
• Two graduate courses
(X and Y)
• Each with two sections
– X1 (18 students)
– X2 (19 students)
– Y1 (18 students)
– Y2 (22 students)
• 2,744,433 server logs
• Cluster Analysis; Sequential
Association Analysis;
Decision Tree Analysis; Data
Visualization

7. © 2016 Boise State University 7
• Data mining + Demographics +
Survey Data + Student
Outcomes
• Research Goal: Large scale
program evaluation
– Support decision making at the
course and institutional level
– Identify key variables and
relationships between teacher
and course satisfaction, student
behaviors, and performance
outcomes
Study #3: End of Year K-12 Online Program Evaluation 2012
• Blackboard LMS
• 7500 students
• 883 courses
• 23,854,527 learning logs
(over 1 billion records)
• Cluster Analysis; Decision
Tree Analysis

8. © 2016 Boise State University 8
Study #4: End of Year K-12 Blended Program Evaluation 2012
• Blackboard LMS
• 255 Enrollments
• 33 course sections
• 17 unique courses
• Satisfaction Survey
• Data from 2011 pilot
study
• Cluster Analysis;
Decision Tree Analysis
• Demographics + Survey Data +
Data Mining + Student
Outcomes
• Research Goal: Test Framework
in Blended Learning
– Support decision making at the
course and institutional level?
– Identify key variables and
relationships between teacher
and course satisfaction, student
behaviors, and performance
outcomes

9. © 2016 Boise State University 9
Study #5: Online Graduate Teacher Education, 2014
• Moodle LMS
• 509 Enrollments
• 25 course sections
• 12 unique courses
• 431,708 records
• Time Series Analysis
• 34 original and derived
variables
–static (demographic)
–dynamic (engagement)
• Demographics + Data
Mining + Student
Outcomes
• Research Goal:
– Could we identify at-risk
students in real time?
– When?

10. © 2016 Boise State University 10
Study #6: Online Graduate Teacher Education, 2015
• Moodle LMS
• 661 Enrollments
• 31 course sections
• 18 unique courses
• 546,965 records
• Time Series Analysis
• 34 original and derived
variables
–static (demographic)
–dynamic (engagement)
• Demographics + Data
Mining + Student
Outcomes
• Research Goal:
– Did the model developed in
Study #5 work with new
semester data?
– Were the predictive (timing)
results the same?
– Were frequency data more
predictive?

11. © 2016 Boise State University 11
Variables - actual and derived
Engagement

12. © 2016 Boise State University 12
Cluster Analysis
Student clustering which describes
shared characteristics of students who passed or failed their courses

13. © 2016 Boise State University 13
Cluster Analysis:
Relative Participation Levels and Final Grades
• Average Time Spent
• Average Days Participated
• Average Frequency of Mouse Clicks
• Average Time Spent per Session
• Average Frequency of Mouse Clicks per Session
Participation Variables
(Engagement)

14. © 2016 Boise State University 14
Cluster Analysis: Student Characteristics
Cluster 1 – Low-High, 119 students: Low average participation and higher
performance levels.
Cluster 2 – High-High, 60 students: High average participation and high
performance levels.
Cluster 3 – Low-Low, 76 students (46% remedial): Low average
participation and low performance levels.

15. © 2016 Boise State University 15
Cluster Distributions in Courses
High percentage of
Low-Low students in
Chemistry CP, CP
Pre-Calculus,
English II, English III,
and Pre-Calculus (H)

16. © 2016 Boise State University 16
Decision Tree Analysis
Perception and performance predictions which identify key predictors of
course satisfaction, instruction satisfaction, and final grade

17. © 2016 Boise State University 17
Decision Tree Analysis:
Predictors of Student Performance
Decision Tree Analysis
• Identified at risk
• Number of Courses Taken
• Average Clicks per Week
• Average Time Spent per
Week, and
• Average Time Spent per
Session
Other factors
• Gender
• Ethnicity
• Reason for taking a course

18. © 2016 Boise State University 18
Sequential Association Analysis
Course X Course Y
Does the design of the course (path to learning) predict learner outcomes?

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Sequential Association Analysis

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Time Series Analysis

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Time Series Analysis – course access
A
B
F
Week 10 Spring Break

22. © 2016 Boise State University 22
Time Series Analysis – DB Replies
A
B
F
Week 10 Spring Break

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Overall Analysis
• Students who took fewer courses performed significantly better than
those who took more courses.
• Engagement is a significant factor. High-engaged students performed
better than low-engaged students.
• Students identified as at risk performed differently than all other
students.
• Type of engagement matters. Students who accessed their courses more
often performed better than those who had more interactions within the
course. Consistent interaction over time is a better predictor of
performance. (higher ed only)
• Advanced courses - High-engagement and high performance (K-12)
• Entry level courses - Low performance regardless of engagement (K-12)
• Gender and ethnicity (higher ed) were identified as significant factors
• Satisfaction did not always equate to higher performance

24. © 2016 Boise State University 24
Characteristics of successful students
• Female (k-12)
• Younger (k-12)
• Were enrolled in advanced courses (k-12)
• Took fewer courses
• Were more engaged overall
• Were consistently engaged

25. © 2016 Boise State University 25
Characteristics of at-risk students
• Male (K-12)
• Older (K-12)
• Took entry-level courses (K-12)
• Took a greater number of courses
• Were low engaged overall
• Were inconsistent in their engagement

26. © 2016 Boise State University 26
Data Collection Challenges
• Bb activity accumulator grouped wide ranging behaviors into
only five useful categories
• Missing data (empty fields – ex. internal handler)
• Mismatched data fields/data stored in the wrong fields
• Inconsistent data collection (i.e. failure to track every forum
reply)
• Partial or missing timestamp (needed for sequential analysis)
• Course or student ID not linked to survey
• Demographic data not linked to course or program
• Inconsistent course models (blended)

27. © 2016 Boise State University 27
Educational Data Mining
Special Challenges
• Learning behaviors are complex
• Target variables (learning
outcomes/performance) require
wide range of assessments and
indicators
• Goal of improving teaching and
learning is hard to quantify
• Limited number of DM techniques
suitable to meet educational goals
• Only interactions that occur in the
LMS can be tracked through data
mining
• Still a very intensive process to
identify rules and patterns

28. © 2016 Boise State University 28
References
•Hung, J. L., Rice, K., & Saba, A. (2012). An educational data mining model for online
teaching and learning. Journal of Educational Technology Development and Exchange,
5(2), 77-94.
•Hung, J. L., Hsu, Y.-C., & Rice, K. (2012). Integrating data mining in program
evaluation of K-12 online education. Educational Technology & Society, 15(3), 27-41.
•Rice, K., & Hung. J. (2015). Data mining in online professional development program:
An exploratory case study. International Journal of Technology in Teaching and
Learning, 11(1), 1-20.
•Shelton, B., Hung, J. L., & Baughman, S. (2015). Online graduate teacher education:
Establishing and EKG for student success intervention. Technology, Knowledge and
Learning.
•Rice, K., & Hung, J. L. (2015). Identifying variables important to the success of K-12
students in blended learning. Paper presented at the Northern Rocky Mountain
Educational Research Association Conference, Boise, Idaho.
•Shelton, B. E., Hung, J. L.., & Lowenthal, P. (under review). Predicting student success
by modeling student interaction in online courses.