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Hart & Ganley SOED 2016

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We examined predictors of Calculus II final grades within a sample of 84 college students enrolled in a hybrid course through WEPS. Predictors included “typical” psychological correlates, including math confidence, math anxiety, spatial skills and numerosity ability, as well as clickstream data from the students’ activity in the online course. Results showed the clickstream data were the best predictors of course performance, in that students who spent more time grading other students’ assignments, and students who took fewer quiz attempts, did better in the course. Math confidence and then math anxiety were the next best predictors, in that students with higher confidence and lower math anxiety performed better in the course. We will discuss how results might be dependent on the particular content of this course, and how we might use easy to collect psychological variables along with clickstream data to better understand, and potentially predict, course performance in online courses.

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Hart & Ganley SOED 2016

  1. 1. Colleen M. Ganley & Sara A. Hart Psychology FCRR, FCR-STEM Florida State University hart@psy.fsu.edu @saraannhart ganley@psy.fsu.edu @colleenganley
  2. 2. Background
  3. 3. • Student attitudes are related to higher mathematics achievement • Expectations of success, comparisons of ability, academic-self concept, confidence of own ability, etc (Reyes & Stanic, 1988; Randhawa et al, 1993, House, 1993, House, 1995) • Cognitive factors are also related to higher mathematics achievement • Numerosity, spatial abilities, memory, etc (Halberda et al., 2008; Siegler & Opfer, 2003; Casey et al., 1995) • But these aren’t surprising, even for predicting success in Calculus (and Calculus II) Understanding which students are successful
  4. 4. • Online learning is becoming more available and popular • These courses provide more data related to the “user” • Every action of the student within the course is tracked • Can these data be used to understand success in the course? • Future goal of intervening with students at risk for failure early in the course Understanding which students are successful in a hybrid Calc course
  5. 5. • What are the most important individual differences predictors of success in a hybrid user-driven Calculus II course? • We will examine both clickstream data and information about students’ attitudes and cognitive performance Research Question
  6. 6. • Spring 2014 Calculus II course at FSU • Hybrid course with a flipped classroom • Students used the online course platform (WEPS https://myweps.com/moodle/) to watch videos of the course content and solved problems in class with professor • All teaching content was available to students at all times (graded items time available only) Methods
  7. 7. • Participants • 84 participants (43% female, 84% White) • Took ~45min battery of demographics, student attitudes and cognitive measures (mostly online in qualtrics) • Outcome variable • Final grade (0-100) in Calculus II course Methods
  8. 8. • Math Confidence (adapted from confidence subscale of Fennema & Sherman, 1976) • Generally I have felt secure about attempting mathematics • I am sure I could do advanced work in mathematics • I can get good grades in mathematics • Math has been my worst subject Attitudinal Measures
  9. 9. • Math Anxiety (MARS-R; Plake & Parker, 1982) • Please indicate the amount of anxiety you feel in each of the following situations. • Buying a math textbook. • Looking through the pages on a math text. • Having to use tables of formulae. Attitudinal Measures
  10. 10. • Panamath “Dots Task” (Halberda et al., 2008) • Approximate Number System • Are there more yellow or blue dots? Cognitive Measures
  11. 11. • Mental Rotation Test (Vandenberg & Kuse, 1978) Cognitive Measures
  12. 12. • So much available information • How to get it into something useable in more “traditional” statistical models? • We just want a number!!! • Tried to use variables that we thought we had reasonable interpretations of (but honestly still unsure) Online Course Measures
  13. 13. • Online workshops (graded homeworks) • Mean time to submission across 13 workshops • From 0-100, with 100 being submitted exactly at time due (from when workshop was available) • Mean time to submission of graded workshop assignments of other students • From 0-100, with 100 being submitted exactly at time due (from deadline of workshop) Online Course Measures
  14. 14. • Online quizzes • Unlimited attempts at quizzes (7 total) • Sum of total number of attempts Online Course Measures
  15. 15. Results - -
  16. 16. • Research question: of our key variables of interest, what are the most useful for predicting final grade? • Dominance analysis allows for this specific test (Budescu, 1993; Azen & Budescu, 2003) • All key variables were added to the model, and pitted against each other for relative importance • https://pantherfile.uwm.edu/azen/www/damacro.h tml • 1000 bootstrapped samples Dominance Analysis (DA)
  17. 17. • Complete dominance • (math confidence = quiz attempts = assessment time) > (math anxiety = mental rotation = ANS = workshop time) • Reproducibility quite low (<10%) • General dominance • (assessment time > quiz attempts > math confidence > math anxiety) > (ANS > workshop time > mental rotation) • Reproducibility is high across parentheses DA results
  18. 18. • (assessment time > quiz attempts > math confidence > math anxiety) > (ANS > workshop time > mental rotation) DA results 0.1 0.09 0.06 0.02 0.02 0.010.005 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Final Grade Mental Rotation Workshop Time ANS Math Anxiety Math Confidence Quiz Attempts Assessment Time
  19. 19. Latent Profile Analysis • Please keep in mind the following are very underpowered • Intention was to have more data for full model • Simulation studies suggest we need at least n=200 at first, and to feel comfortable making reliable predictions with our model likely closer to n = 500 (Nylund, Asparouhov & Muthen, 2007)
  20. 20. Mental Rotation ADHD_In att Math Anxiety Math Confidenc e Math Interest Math Motivation Math Importanc e ANS Class 1 n = 77 0.064 0.065 -0.15 0.379 0.425 0.435 0.413 -0.213 Class 2 n = 17 -0.265 -0.32 0.542 -0.975 -1.225 -1.216 -0.945 -0.292 Class 3 n = 4 0.124 0.351 0.031 -1.754 -1.411 -1.603 -2.412 3.828 -3 -2 -1 0 1 2 3 4 5 Score
  21. 21. Final Grade Exam 1 Exam 2 Exam 3 Diagnostic Test class 1 0.09 0.15 0.07 0.09 0.12 class 2 0.02 -0.32 0.1 -0.05 -0.13 class 3 -1.59 -1.05 -1.56 -1.29 -1.06 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 Score
  22. 22. • Student attitudes relatively important • Replication of previous literature showing math confidence important positive predictor of math/Calculus success (e.g., House, 1995) • Possibly role for measuring math anxiety too • May be due to this being Calc II • What happened to the cognitive predictors? Discussion
  23. 23. • Online data also important relative predictors • Assessment total negative predictor • “procrastination” variable • OR, students who struggle in Calculus found this very hard • Number of times retake quiz positive predictor • “perfection” variable Discussion
  24. 24. • We learn more when we look at BOTH: • student’s interactions with online platform to prediction of student success AND • known “psychological” student characteristics • But SO MUCH data, and most of it requires huge assumptions • Hard to know what we are measuring with the online variables! Conclusion
  25. 25. • What other information can we get from clickstream data that might be useful? • How to get it into a useable form? • Can we predict how students will use the online system from their characteristics? • Can we then use this information to develop a recommendation system? Future Directions
  26. 26. • NSF grants 1450501 & E2030291 • Dr. Olga Caprotti & Yahya Almalki hart@psy.fsu.edu @saraannhart Acknowledgements ganley@psy.fsu.edu @colleenganley

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