Using Learning Analytics to Assess Innovation & Improve Student Achievement
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Presentation about Learning Analytics for JISC network event; discussion of research findings and implications for individual and institutions considering a Learning Analytics project. Also discuss implications for my work with Blackboard on "Platform Analytics."
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Using Learning Analytics to Assess Innovation & Improve Student Achievement
- 1. Using Learning Analytics to Assess Innovation & Improve Student Achievement John Whitmer, Ed.D. john.whitmer@blackboard.com @johncwhitmer UK Learning Analytics Network Event (JISC) March 5, 2015 http://bit.ly/jwhitmer-jisc
- 2. Quick bio 15 years managing academic technology at public higher ed institutions (R1, 4-year, CC’s) • Always multi-campus projects, innovative uses of academic technologies • Most recently: California State University, Chancellor’s Office, Academic Technology Services Doctorate in Education from UC Davis (2013) with Learning Analytics study on Hybrid, Large Enrollment course Active academic research practice (San Diego State Learning Analytics, MOOC Research Initiative, Udacity SJSU Study…)
- 3. Meta-questions driving my research 1. How can we provide students with immediate, real-time feedback? (esp identify students at-risk of failing a course) 2. How can we design effective interventions for these students? 3. How can we assess innovations (or status quo deployments) of academic technologies? 4. Do these findings apply equally to students ‘at promise’ due to their background (e.g. race, class, family education, geography)
- 4. Outline 1. Defining & Positioning Learning Analytics 2. A Few Empirical Research Findings • Understanding Contradictory Outcomes in a Redesigned Hybrid Course (Chico State) • Creating Accurate Learning Analytics Triggers & Effective Interventions (SDSU) 3. How we’re Applying this Research @ Blackboard 4. Discussion 4
- 5. Economist. (2010, 11/4/2010). Augmented business: Smart systems will disrupt lots of industries, and perhaps the entire economy. The Economist. 5
- 6. 200MBof data emissions annually Economist. (2010, 11/4/2010). Augmented business: Smart systems will disrupt lots of industries, and perhaps the entire economy. The Economist. 6
- 7. Logged into course within 24 hours Interacts frequently in discussion boards Failed first exam Hasn’t taken college-level math No declared major 7
- 8. What is learning analytics? Learning and Knowledge Analytics Conference, 2011 “ ...measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
- 9. Strong interest by faculty & students From Eden Dahlstrom, D. Christopher Brooks, and Jacqueline Bichsel. The Current Ecosystem of Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives. Research report. Louisville, CO: ECAR, September 2014. Available from http://www.educause.edu/ecar.
- 10. Source: Educause and AIR, 2012 (2012), http://goo.gl/337mA
- 11. 2. A Few Empirical Research Findings
- 12. Study 1: Understanding Contradictory Outcomes in a Redesigned Hybrid Course (Chico State)
- 13. Course redesigned for hybrid delivery in year-long program Enrollment: 373 students (54% increase largest section) Highest LMS usage entire campus Fall 2010 (>250k hits) Bimodal outcomes: • 10% increased SLO mastery • 7% & 11% increase in DWF Why? Can’t tell with aggregated reporting data Study Overview 54 F’s
- 14. Grades Significantly Related to Access Course: “Introduction to Religious Studies” CSU Chico, Fall 2013 (n=373) Variable % Variance Total Hits 23% Assessment activity hits 22% Content activity hits 17% Engagement activity hits 16% Administrative activity hits 12% Mean value all significant variables 18%
- 15. LMS Activity better Predictor than Demographic/Educational Variables Variable % Var. HS GPA 9% URM and Pell-Eligibility Interaction 7% Under-Represented Minority 4% Enrollment Status 3% URM and Gender Interaction 2% Pell Eligible 2% First in Family to Attend College 1% Mean value all significant variables 4% Not Statistically Significant Gender Major-College
- 16. At-risk students: “Over-working gap”
- 17. Activities by Pell and grade Grade / Pell-Eligible A B+ C C- 0K 5K 10K 15K 20K 25K 30K 35K Measure Names Admin Assess Engage Content Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible
- 18. Activities by Pell and grade Grade / Pell-Eligible A B+ C C- 0K 5K 10K 15K 20K 25K 30K 35K Measure Names Admin Assess Engage Content Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible Not Pell- Eligible Pell- Eligible Extra effort In content-related activities
- 19. Study 2: Creating Accurate Learning Analytics Triggers & Effective Interventions (SDSU)
- 20. Study Overview • President-level initiative • Goal: identify effective interventions driven by Learning Analytics “triggers” • Multiple “triggers” (e.g., LMS access, Grade, Online Homework/Quiz, Clicker use) • At scale & over time: conducted for 3 terms, 5 unique courses, 3,529 students • “Gold standard” experimental design (control / treatment) 20
- 21. Focus on High Need Courses 21 84% 62% 69% 78.5% 70.8% 16% 38% 31% 21.5% 29.2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ANTH 101 COMPE 270 ECON 101 PSY 101 STAT 119 Repeatable Grades Non- Repeatable Grades
- 22. 1. Identify courses and recruit instructors 2. Prior to course start, review syllabus, schedule meaningful “triggers” for each course (e.g. attendance, graded items, Blackboard use, etc.) 3. Run reports in Blackboard, Online Homework/Quiz software to identify students with low activity or performance (~ weekly) 4. Send “flagged” student in experimental group a notification/intervention 5. Aggregate data, add demographic data. Analyze. Study Protocol 22
- 23. Key Questions 1. Are triggers accurate predictors of course grade? 2. Do interventions (based on triggers) improve student grades? 3. Do these relationships vary based on student background characteristics?
- 24. Frequency of interventions (Spring 2014) # Students Receiving >0 Interventions: PSY: 177 (84%) STAT: 165 (70%) 14% 19% 11% 17% 10% 6% 5% 6% 2% 1% 3% 2% 30% 17% 13% 12% 7% 6% 6% 3% 4% 1% 2% 4% 0% 5% 10% 15% 20% 25% 30% 35% 0 1 2 3 4 5 6 7 8 9 10 >10 Students Interventions PSY STAT
- 25. Frequency of interventions (Spring 2015) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Students Triggers Activated Triggers Activated per StudentS (Spring 2015) Anthro1 Anthro3 Comp Engr Econ4 Psych1 Psych2 Stats3 Stats4 25
- 26. Interventions Spring 2014 Fall 2014 Fall 2015 26
- 27. A Typical Intervention: “Concerned Friend” tone 27 … data that I've gathered over the years via clickers indicates that students who attend every face-to-face class meeting reduce their chances of getting a D or an F in the class from almost 30% down to approximately 8%. So, please take my friendly advice and attend class and participate in our classroom activities via your clicker. You'll be happy you did! Let me know if you have any questions. Good luck, Dr. Laumakis
- 28. Poll question A Not significant <10%, significant .05 level 20%, significant .01 level 30%, significant .001 level Did triggers predict achievement? What level significance? How much variation in student grade was explained? B C D E 50%+, significant .0001 level 28
- 29. Poll question A Not significant <10%, significant .05 level 20%, significant .01 level 30%, significant .001 level Did triggers predict achievement? What level significance? How much variation in student grade was explained? B C D E 50%+, significant .0001 level (Spring 2014, Fall 2014) 29
- 30. Statistics Learning analytics triggers vs. final course points Spring 2014: 4 sections, 2 courses, 882 students Psychology p<0.0001; r2=0.4828 p<0.0001; r2=0.6558
- 31. Fall 2014 results: Almost identical 5 Sections, 3 Courses, N=1,220 students p<0.00001; r2=0.4836
- 32. Spring 2015 Results (tentative): lower relationship 8 Sections, 5 Courses, N=1,390 students p<0.00001; r2=0.28 32
- 33. Spring 2015 Results (tentative): lower relationship 8 Sections, 5 Courses, N=1,390 students p<0.00001; r2=0.28 33
- 34. Explained by differences between courses (Spring 2015 Results by Course) R2 (all triggers) R2 (no grades) Anthro1 (Online) 0.54 0.65 Anthro3 (In Person) not significant not significant Comp Eng not significant not significant Econ4 not significant not significant Psych1 0.58 0.4 Psych2 0.41 0.23 Stat3 0.2 0.11 Stat4 0.33 0.21 34 R2 (all triggers) R2 (no grades) Anthro1 (Online) 0.54 0.65 Anthro3 (In Person) not significant not significant Comp Eng not significant not significant Econ4 not significant not significant Psych1 0.58 0.4 Psych2 0.41 0.23 Stat3 0.2 0.11 Stat4 0.33 0.21
- 35. So did the interventions make a difference in learning outcomes?
- 36. 79% 79% 89% 82% 21% 21% 11% 18% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Control (STAT) Exp. (STAT) Exp. (PSY) Control (PSY) Experimental Participation vs. Repeatable Grade (Spring 2014) Repeatible Grade Passing Grade 36
- 37. 77% 91%23% 9% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% No Interventions (n=87, PSY, Pell-Eligible) Interventions (n=81, PSY, Pell-eligible) Experimental Participation vs. Repeatable Grade (Pell-Eligible) (n=168, Spring 2014, PSY 101) Passing Grade Repeatible Grade 24 additional Pell-eligible students would have passed the class if the intervention was applied to all participating students. 37
- 38. Fall 2014 / Spring 2015 Intervention Results: No Significant Difference Between Experimental/Control Groups. 38
- 39. One Explanation: Low Reach 39 Fall 2014 (n = 1,220) Row Labels # Triggers Message Open Rate Clickthrough Rate Econ1 8 76% 36% Psych1 6 70% 29% Psych2 7 69% 35% Stat3 9 62% 25% Stat4 8 65% 27% Grand Total 38 68% 30% Spring 2015 (n = 1,138) Row Labels # Triggers Message Open Rate Clickthrough Rate Anthro-In Person 17 57% 10% Anthro-Online 7 71% 35% Comp Engineering 15 52% 14% Econ 15 44% 13% Psych1 17 60% 13% Psych2 17 63% 13% Stat3 21 64% 9% Stat4 20 55% 5% Grand Total 129 58% 12%
- 40. Proposed Next Steps • Add interventions that move “beyond informing” students to address underlying study skills and behaviors – Supplemental Instruction <http://www.umkc.edu/asm/si/> – Adaptive Release within online courses (content, activities) 40
- 41. 1. Data from academic technology use predicts student achievement; diverse sources provide better predictions. 2. Tech use > demographic data to predict course success; adding demographic data provides nuanced understandings and identifies trends not otherwise visible. 3. Academic technology use is not a “cause” in itself, but reveals underlying study habits and behaviors (e.g. effort, time on task, massed vs. distributed activity). 4. Predictions are necessary, but not sufficient, to change academic outcomes. Research into interventions is promising. 5. We’re at an early stage in Learning Analytics; expect quantum leaps in the near future. 41 Conclusions and Implications
- 42. 4. How we’re Applying this Research @ Blackboard 42
- 43. Improved instrumentation for learning activity within applications Blackboard’s “Platform Analytics” Project A new effort to enhance our analytics offerings across our academic technology applications that include Applied findings from analysis (inc. inferential statistics and data mining) Integrated analytics into user experiences (inc. student and faculty) Aggregated usage data across cloud applications (anonymized, rolled-up, privacy-compliant)
- 46. Blackboard Analytics for Learn
- 47. 3. Wrap-Up and Discussion
- 48. Factors affecting growth of learning analytics Enabler Constraint WidespreadRare New education models Resources ($$$, talent) Data governance (privacy, security, ownership) Clear goals and linked actions Data valued in academic decisions Tools/systems for data co-mingling and analysis Academic technology adoption Low data quality (fidelity with meaningful learning) Difficulty of data preparation Not invented here syndrome
- 49. Call to action [with amendments] (from a May 2012 Keynote Presentation @ San Diego State U) You’re not behind the curve, this is a rapidly emerging area that we can (should) lead... [together with interested partners] Metrics reporting is the foundation for analytics [don’t under or over-estimate the importance] Start with what you have! Don’t wait for student characteristics and detailed database information; LMS data can provide significant insights If there’s any ed tech software folks in the audience, please help us with better reporting! [we’re working on it and feel your pain!]
- 50. Discussion / Questions John Whitmer, Ed.D. john.whitmer@blackboard.com @johncwhitmer http://bit.ly/jwhitmer-jisc
Notes de l'éditeur
- John
- John
- 2013 SP – Study began with two courses (N=`2,000) in Spring 2014 Weekly reports; triggered students sent email and multimedia “interventions” (low/high intensity) Our hypothesis was - as you will see as the number of trigger events increase, so would the likelihood of having to repeat the course
- We focused on high needs courses. 16-38% “Repeatable grades”
- James Weekly reports; triggered students sent email “interventions” (low intensity). Did people show up i.e., Clicker (participation/attendance points)
- Talking points: Almost ¾ of students got at least one trigger in each course More PSY students got interventions than Stat students (b/c not completing homework) The pattern of the # of interventions in both courses is about the same – high up to 2-3, then trails off. Interesting findings – when consider that the triggers were very different between courses (e.g. PSY only 2 graded items, PSY: Online Homework, Stat: Online Quizzes. Etc).
- James: most recent semester: different trajectory by course, although see trail-off pattern over time, most of the triggers in small range (1-3) But significant differences between courses in number of triggers and number of students “activating” trigger. Show differences as we have expanded the study over time.
- James For the experimental students, we have expanded the interventions over time to use different modalities: but the focus of all interventions has been in increasing the awareness of students about their at-risk behaviors/status, so that they can do something about it (self regulate).
- Message written by instructor, with strong attention to tone: a “concerned friend”, which we thought would be more effective with students. Also include serious results that might resonate with them and they would take notice about.
- John
- John
- These graphs illustrate that DECREASES in triggers are related to INCREASES in student grade. (explanation: Each dot is a student; Y axis is the total points (lower to higher), and X axis is the total # of triggers (higher to lower)) Significantly significant results for both courses; possibility due to chance less than 1 in 10,000. Size of effect different: PSY: triggers explain 48% variation in final grade STAT: triggers explain 66% of variation in final grade (if remove graded items from Stat, triggers explains 49%)
- John
- John
- John: Million dollar question: did this reduce student repeatable grade rates.
- John
- James Definition: Supplemental Instruction (SI) is an academic assistance program that utilizes peer-assisted study sessions. SI sessions are regularly-scheduled, informal review sessions in which students compare notes, discuss readings, develop organizational tools, and predict test items. Students learn how to integrate course content and study skills while working together. The sessions are facilitated by “SI leaders”, students who have previously done well in the course and who attend all class lectures, take notes, and act as model students. Purpose: To increase retention within targeted historically difficult courses To improve student grades in targeted historically difficult courses To increase the graduation rates of students Participants: SI is a “free service” offered to all students in a targeted course. SI is a non remedial approach to learning as the program targets high-risk courses rather than high-risk students. All students are encouraged to attend SI sessions, as it is a voluntary program. Students with varying levels of academic preparedness and diverse ethnicities participate. There is no remedial stigma attached to SI since the program targets high-risk courses rather than high-risk students.
- John – first two points (provide examples of academic technology use when explaining first bullet) James – last two points (because so much of a students participation and performance in a class is logged via academic technology tools it provides us with evidence of student behavior which reveal patterns that can predict struggling students or successful students)
- More and better data: More data sources, less structured data, integrated data, potential for expansion of data sources in future. Unlock meaning from data that’s meaningful/available. Intended for multiple audiences: Blackboard internal development, CIOs/administrative customers interested in metrics, faculty/student customers interested in learning/teaching, researchers interested in underlying relationships.