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Rethinking Student Success 
Analytics in Support of Teaching and Learning 
Timothy D. Harfield, Scholar in Residence (Learning Analytics) 
Emory University Libraries and Information Technology Services 
@tdharfield
2 
Why Learning Analytics? 
@tdharfield
 Founded in 1836 
 11 Schools 
(4 Undergraduate, 7 Graduate) 
 14,513 Students 
(7,836 Undergraduate, 6,677 Graduate & 
Professional) 
 Average Freshman Retention Rate: 
95.3% 
 4-year Graduation Rate: 84% 
 94% College Success Rate (<> 
DFW) 
 US News National University 
Ranking #21 
Fact & Figures as of Fall 2013 
(http://www.emory.edu/home/about/factsfigures/) 
@tdharfield 
Why Learning Analytics? 
Emory University
• “The most dramatic factor shaping the future of higher education” 
• Drop in U.S. News & World Report Rankings, from 20th to 21st 
• Existing rates of student success makes at-risk students difficult to identify 
Why Blackboard Analytics for Learn? 
• Learning Management Gap in Oracle BI Solution 
• Fast, lightweight, inexpensive 
@tdhar4field 
Why Learning Analytics? 
Emory University
5 
Blackboard Analytics Pilot 
@tdharfield
@tdhar6field 
Blackboard Analytics Pilot 
Methodology 
Blackboard Analytics for Learn™ Adds to Emory’s existing analytical capacity 
In three areas: 
I. Learning Analytics 
Integrated Blackboard Reports for teachers and students 
 Survey Instrument based on Learning Analytics Acceptance Model 
II. Academic Analytics 
Online Analytical Processing for instructional designers and university leadership 
 Focus Group 
III. Educational Data Mining / Predictive Analytics 
Direct database queries and table exports for longitudinal modeling ( >180 days) and analysis using 
statistical software packages and machine learning algorithms 
 Exploratory Data Mining
Overall 
Ease of Use 
@tdhar7field 
Blackboard Analytics Pilot 
Integrated Reports 
Would You Like to Have Access 
Yes 
51% 
in Other Courses? 
No 
9% 
Maybe 
40% 
6%3% 
27% 
21% 
43% 
3% 
23% 
47% 
15% 
12% 
Overall 
Usefulness 
Would You Recommend It 
Yes 
36% 
to Others? 
No 
17% 
Maybe 
47% 
Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree
@tdhar8field 
Blackboard Analytics Pilot 
Integrated Reports | Student Feedback 
“I believe it's important for professors to have this tool available to them, so they can see early if we're not turning up for 
class enough and intervention might be warranted. I do know that I need to get in a habit of logging onto BB at least twice a 
day. I need to make it part of my daily life” 
“I liked being able to compare my activity to others, but I don't think it changed my behavior. It may be useful to others.” 
“I think its useful if someone is concerned about how they are doing in the class or if they are uncertain about how much 
time they should be committing to the class. I personally did not find it useful but I can see how someone may use it as a 
tool to determine if they are meeting expectations” 
“I think this system also puts undue pressure on students to log in or click on specific items in blackboard more, without 
taking into consideration factors such as work done offline in the class or via email. This is especially true for distance 
learners. I personally cut and paste many of my assignments in Word so they are easier to view and manipulate. I also like 
to keep files from each semester so I do a considerable amount of work offline and I am doing very well in my classes so 
far. When I logged in to check my activity, the data I saw pretty much showed the opposite-it looked like I wasn't very 
engaged at all.” 
“If this is a tool that will be used by Professors to grade participation I would have likely used it more; however, if this tool 
were indeed used by Professors it may cause students to post unnecessarily.” 
“You should be a big button at the front of the page with an icon to remind people to use it.”
@tdhar9field 
Blackboard Analytics Pilot 
Integrated Reports | Faculty Feedback 
• Anecdotally, faculty report that students like the new analytics feature, although it is a 
distraction for some high performing students who become overly driven by metrics at 
the expense of actual course objectives 
Takeaway: Use of Course Analytics should be accompanied by clear, continuous 
communication about the tool’s use and limitations, and the ways in 
which data are being used for assessment. There is much to be 
gained from the use of embedded analytics, but also risk if not 
implemented in manner that is pedagogically sound.
@tdha1r0field 
Blackboard Analytics Pilot 
Online Analytical Processing 
• Delivered reports for the Pyramid BI reporting environment are of little value to 
instructional designers. 
a. Data dictionary is out of date, inaccurate, confusing, and inconsistent 
b. It is unclear what various reports are meant to accomplish, and how they might help generate 
actionable insights 
Takeaways: Reporting using the Pyramid BI reporting environment is more useful 
to the Office of Institutional Research than to instructional designers 
Instructional designers are interested in analyzing data using more 
inferential and predictive techniques. Program improvement should 
start with questions rather than with data.
11 
62% 
38% 
Course Items Added No Course Items Added 
DFW Rate by Blackboard Use 
5% 5% 
100% 
75% 
50% 
25% 
0% 
Course Items Added No Course Items Added 
Course Items Added No Course Items Added 
@tdharfield 
Blackboard Analytics Pilot 
Exploratory Data Mining
@tdha1r2field 
Blackboard Analytics Pilot 
Exploratory Data Mining 
DFW Rate by Design Quartile 
4.8% 
5.2% 
5.8% 
4.0% 
5.1% 
10% 
5% 
0% 
First Quartile Second Quartile Third Quartile Fourth Quartile Non-Blackboard
@tdha1r3field 
Blackboard Analytics Pilot 
Exploratory Data Mining 
DFW Rate by Class 
3.3% 
4.1% 
7.3% 
5.5% 
10% 
5% 
0% 
Freshman Sophomore Junior Senior
DFW Rate by Class 
(Oxford College) 
10.1% 9.8% 
Junior Senior 
@tdha1r4field 
Blackboard Analytics Pilot 
Exploratory Data Mining 
DFW Rate by Class 
(Excluding Oxford College) 
3.3% 
4.1% 
20% 
10% 
0% 
6.4% 
4.4% 
10% 
5% 
0% 
Freshman Sophomore Junior Senior
15 
5.3% 
6.1% 
4.8% 4.8% 
10% 
5% 
0% 
Files Items 
DFW Rate by Item Type 
DFW Rate by Link Type 
5.3% 5.4% 
1.0% 
4.0% 
10% 
5% 
0% 
Course Links External URLs 
Not Used Used 
@tdharfield 
Blackboard Analytics Pilot 
Exploratory Data Mining
16 
DFW Rate by Grade Center Use 
4.1% 
5.8% 
10% 
5% 
0% 
No Yes 
@tdharfield 
Blackboard Analytics Pilot 
Exploratory Data Mining
17 
DFW Rate by Minutes Z-Score 
2.9% 
4.3% 
5.8% 6.5% 
20% 
10% 
0% 
>= 1 >= 0 & < 1 > -1 & > 0 <= -1 
DFW Rate by Interactions Z-Score 
2.8% 3.5% 
6.5% 
8.6% 
20% 
10% 
0% 
>= 1 >= 0 & < 1 > -1 & > 0 <= -1 
DFW Rate by Access Z-Score 
2.5% 
3.4% 
6.7% 
10.3% 
20% 
10% 
0% 
>= 1 >= 0 & < 1 > -1 & > 0 <= -1 
@tdharfield 
Blackboard Analytics Pilot 
Exploratory Data Mining
18 
Next Steps 
@tdharfield
@tdha1r9field 
Regional Community of Practice 
“Data Wrangling” 
Local Community of Practice 
Next Steps
@tdha2r0field 
Next Steps 
Data Wrangling 
Predicting Students at Risk 
 Data Validation 
 Data Dictionary 
 Predictive Modeling
@tdha2r1field 
Next Steps 
Data Wrangling 
Online Course Evaluation Framework 
Roxanne Russell, PhD 
 Are logged course behaviors predictive of 
student satisfaction with course design, 
implementation, and interaction dynamics? 
 Can we develop algorithms for identifying 
pain points, and effective interventions to 
mitigate dissatisfaction and other barriers to 
learning?
@tdha2r2field 
Next Steps 
Data Wrangling 
Getting a Leg Up at Emory (GLUE) 
Drew Kohlhorst, PhD 
 Does participation in the GLUE program contribute 
significantly to student success in STEM courses at 
Emory? 
 Does GLUE function to attract and retain more 
STEM majors? 
 Are there significant differences in the success 
rates of students participating in the online versus 
residential versions of the program? 
 Does participation see changes in 
attitudes/confidence in scientific skills, and/or 
changes in on-campus resource utilization?
@tdha2r3field 
Next Steps 
Data Wrangling 
Emory Coursera Initiative 
Stephanie Parisi 
 Who are our learners? 
 Why do learners take a particular 
course? 
 What factors contribute most to 
learner satisfaction?
@tdha2r4field 
Next Steps 
Local Community of Practice 
Analytics for Learning at Emory (ALE) 
 Receive inspiration and assistance in the 
formation of questions that might guide the use 
of data to optimize learning and learning 
environments 
 Share current interests and projects, in order to 
receive feedback and encouragement, and to 
develop possibilities for future collaboration 
 Gain exposure to the field of learning analytics 
(language, key concepts, methodologies, etc) 
through access to experts
@tdha2r5field 
Next Steps 
Regional Community of Practice 
https://scholarblogs.emory.edu/ale
THANKS! 
Timothy D. Harfield 
Scholar in Residence (Learning Analytics) 
timothy.harfield@emory.edu 
@tdharfield

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Rethinking Student Success: Analytics in Support of Teaching and Learning

  • 1. Rethinking Student Success Analytics in Support of Teaching and Learning Timothy D. Harfield, Scholar in Residence (Learning Analytics) Emory University Libraries and Information Technology Services @tdharfield
  • 2. 2 Why Learning Analytics? @tdharfield
  • 3.  Founded in 1836  11 Schools (4 Undergraduate, 7 Graduate)  14,513 Students (7,836 Undergraduate, 6,677 Graduate & Professional)  Average Freshman Retention Rate: 95.3%  4-year Graduation Rate: 84%  94% College Success Rate (<> DFW)  US News National University Ranking #21 Fact & Figures as of Fall 2013 (http://www.emory.edu/home/about/factsfigures/) @tdharfield Why Learning Analytics? Emory University
  • 4. • “The most dramatic factor shaping the future of higher education” • Drop in U.S. News & World Report Rankings, from 20th to 21st • Existing rates of student success makes at-risk students difficult to identify Why Blackboard Analytics for Learn? • Learning Management Gap in Oracle BI Solution • Fast, lightweight, inexpensive @tdhar4field Why Learning Analytics? Emory University
  • 5. 5 Blackboard Analytics Pilot @tdharfield
  • 6. @tdhar6field Blackboard Analytics Pilot Methodology Blackboard Analytics for Learn™ Adds to Emory’s existing analytical capacity In three areas: I. Learning Analytics Integrated Blackboard Reports for teachers and students  Survey Instrument based on Learning Analytics Acceptance Model II. Academic Analytics Online Analytical Processing for instructional designers and university leadership  Focus Group III. Educational Data Mining / Predictive Analytics Direct database queries and table exports for longitudinal modeling ( >180 days) and analysis using statistical software packages and machine learning algorithms  Exploratory Data Mining
  • 7. Overall Ease of Use @tdhar7field Blackboard Analytics Pilot Integrated Reports Would You Like to Have Access Yes 51% in Other Courses? No 9% Maybe 40% 6%3% 27% 21% 43% 3% 23% 47% 15% 12% Overall Usefulness Would You Recommend It Yes 36% to Others? No 17% Maybe 47% Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree
  • 8. @tdhar8field Blackboard Analytics Pilot Integrated Reports | Student Feedback “I believe it's important for professors to have this tool available to them, so they can see early if we're not turning up for class enough and intervention might be warranted. I do know that I need to get in a habit of logging onto BB at least twice a day. I need to make it part of my daily life” “I liked being able to compare my activity to others, but I don't think it changed my behavior. It may be useful to others.” “I think its useful if someone is concerned about how they are doing in the class or if they are uncertain about how much time they should be committing to the class. I personally did not find it useful but I can see how someone may use it as a tool to determine if they are meeting expectations” “I think this system also puts undue pressure on students to log in or click on specific items in blackboard more, without taking into consideration factors such as work done offline in the class or via email. This is especially true for distance learners. I personally cut and paste many of my assignments in Word so they are easier to view and manipulate. I also like to keep files from each semester so I do a considerable amount of work offline and I am doing very well in my classes so far. When I logged in to check my activity, the data I saw pretty much showed the opposite-it looked like I wasn't very engaged at all.” “If this is a tool that will be used by Professors to grade participation I would have likely used it more; however, if this tool were indeed used by Professors it may cause students to post unnecessarily.” “You should be a big button at the front of the page with an icon to remind people to use it.”
  • 9. @tdhar9field Blackboard Analytics Pilot Integrated Reports | Faculty Feedback • Anecdotally, faculty report that students like the new analytics feature, although it is a distraction for some high performing students who become overly driven by metrics at the expense of actual course objectives Takeaway: Use of Course Analytics should be accompanied by clear, continuous communication about the tool’s use and limitations, and the ways in which data are being used for assessment. There is much to be gained from the use of embedded analytics, but also risk if not implemented in manner that is pedagogically sound.
  • 10. @tdha1r0field Blackboard Analytics Pilot Online Analytical Processing • Delivered reports for the Pyramid BI reporting environment are of little value to instructional designers. a. Data dictionary is out of date, inaccurate, confusing, and inconsistent b. It is unclear what various reports are meant to accomplish, and how they might help generate actionable insights Takeaways: Reporting using the Pyramid BI reporting environment is more useful to the Office of Institutional Research than to instructional designers Instructional designers are interested in analyzing data using more inferential and predictive techniques. Program improvement should start with questions rather than with data.
  • 11. 11 62% 38% Course Items Added No Course Items Added DFW Rate by Blackboard Use 5% 5% 100% 75% 50% 25% 0% Course Items Added No Course Items Added Course Items Added No Course Items Added @tdharfield Blackboard Analytics Pilot Exploratory Data Mining
  • 12. @tdha1r2field Blackboard Analytics Pilot Exploratory Data Mining DFW Rate by Design Quartile 4.8% 5.2% 5.8% 4.0% 5.1% 10% 5% 0% First Quartile Second Quartile Third Quartile Fourth Quartile Non-Blackboard
  • 13. @tdha1r3field Blackboard Analytics Pilot Exploratory Data Mining DFW Rate by Class 3.3% 4.1% 7.3% 5.5% 10% 5% 0% Freshman Sophomore Junior Senior
  • 14. DFW Rate by Class (Oxford College) 10.1% 9.8% Junior Senior @tdha1r4field Blackboard Analytics Pilot Exploratory Data Mining DFW Rate by Class (Excluding Oxford College) 3.3% 4.1% 20% 10% 0% 6.4% 4.4% 10% 5% 0% Freshman Sophomore Junior Senior
  • 15. 15 5.3% 6.1% 4.8% 4.8% 10% 5% 0% Files Items DFW Rate by Item Type DFW Rate by Link Type 5.3% 5.4% 1.0% 4.0% 10% 5% 0% Course Links External URLs Not Used Used @tdharfield Blackboard Analytics Pilot Exploratory Data Mining
  • 16. 16 DFW Rate by Grade Center Use 4.1% 5.8% 10% 5% 0% No Yes @tdharfield Blackboard Analytics Pilot Exploratory Data Mining
  • 17. 17 DFW Rate by Minutes Z-Score 2.9% 4.3% 5.8% 6.5% 20% 10% 0% >= 1 >= 0 & < 1 > -1 & > 0 <= -1 DFW Rate by Interactions Z-Score 2.8% 3.5% 6.5% 8.6% 20% 10% 0% >= 1 >= 0 & < 1 > -1 & > 0 <= -1 DFW Rate by Access Z-Score 2.5% 3.4% 6.7% 10.3% 20% 10% 0% >= 1 >= 0 & < 1 > -1 & > 0 <= -1 @tdharfield Blackboard Analytics Pilot Exploratory Data Mining
  • 18. 18 Next Steps @tdharfield
  • 19. @tdha1r9field Regional Community of Practice “Data Wrangling” Local Community of Practice Next Steps
  • 20. @tdha2r0field Next Steps Data Wrangling Predicting Students at Risk  Data Validation  Data Dictionary  Predictive Modeling
  • 21. @tdha2r1field Next Steps Data Wrangling Online Course Evaluation Framework Roxanne Russell, PhD  Are logged course behaviors predictive of student satisfaction with course design, implementation, and interaction dynamics?  Can we develop algorithms for identifying pain points, and effective interventions to mitigate dissatisfaction and other barriers to learning?
  • 22. @tdha2r2field Next Steps Data Wrangling Getting a Leg Up at Emory (GLUE) Drew Kohlhorst, PhD  Does participation in the GLUE program contribute significantly to student success in STEM courses at Emory?  Does GLUE function to attract and retain more STEM majors?  Are there significant differences in the success rates of students participating in the online versus residential versions of the program?  Does participation see changes in attitudes/confidence in scientific skills, and/or changes in on-campus resource utilization?
  • 23. @tdha2r3field Next Steps Data Wrangling Emory Coursera Initiative Stephanie Parisi  Who are our learners?  Why do learners take a particular course?  What factors contribute most to learner satisfaction?
  • 24. @tdha2r4field Next Steps Local Community of Practice Analytics for Learning at Emory (ALE)  Receive inspiration and assistance in the formation of questions that might guide the use of data to optimize learning and learning environments  Share current interests and projects, in order to receive feedback and encouragement, and to develop possibilities for future collaboration  Gain exposure to the field of learning analytics (language, key concepts, methodologies, etc) through access to experts
  • 25. @tdha2r5field Next Steps Regional Community of Practice https://scholarblogs.emory.edu/ale
  • 26. THANKS! Timothy D. Harfield Scholar in Residence (Learning Analytics) timothy.harfield@emory.edu @tdharfield

Editor's Notes

  • #8: 6 Courses (8 sections), 387 Students 60 total respondents 41 Students (Survey Respondents from PRS 535: Questionnaire Design and Analysis (2 Sections)) Response Rate: 58%
  • #9: Base: 35 Students (Survey Respondents from PRS 535: Questionnaire Design and Analysis (2 Sections)) Response Rate: 58%
  • #10: Base: 35 Students (Survey Respondents from PRS 535: Questionnaire Design and Analysis (2 Sections)) Response Rate: 58%
  • #13: Design Index = ((Content Quartile * Content Weight) + (Assessment Quartile * Assessment Weight) + (Tool Quartile * Tool Weight) / (Content Weight + Assessment Weight + Tool Weight) * 4 )
  • #23: changes in attitudes/confidence in scientific skills; building learning communities utilization of on-campus resources