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Exploring the Relationship Between Novice Programmer Confusion and Achievement

Exploring the Relationship Between Novice Programmer Confusion and Achievement. By: Diane Marie Lee Ma. Mercedes Rodrigo Ryan Baker Jessica Sugay Andrei Coronel. Affective States and Achievement. Recent studies have illustrated the relationships between affective states and achievement

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Exploring the Relationship Between Novice Programmer Confusion and Achievement

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  1. Exploring the Relationship Between Novice Programmer Confusion and Achievement By: Diane Marie Lee Ma. Mercedes Rodrigo Ryan Baker Jessica Sugay Andrei Coronel

  2. Affective States and Achievement • Recent studies have illustrated the relationships between affective states and achievement • Negative affective states have negative impact on student’s achievement (Craig et al, 2006; Rodrigo, 2009; Lagud, 2010)

  3. Confusion • Double-edged/ Dual Nature (D’Mello 2009) • Harmful • Helpful

  4. Goal • Discovery-with-models approach to finding the relationship between novice programmer confusion and achievement

  5. Data Collection • 149 students enrolled in CS21a – Introduction to Computing I • Four lab sessions • BlueJ IDE • BlueJ Plug-in (Jadud and Henriksen, 2009)

  6. Data Collection • Compilation logs = all submissions made to the compiler • Compilation logs include • Computer number • Timestamp • Code • Error message (if any) • And many more!

  7. Data Collection • Total of 340 student-lab sessions • Total of 13,528 compilation logs collected

  8. Data Labeling • Sorted the compilations by student and by Java class name • Grouped the compilations into clips • Clips = 8 compilations • Total: 2,386 clips • Raters were asked to label a sample of 664 clips

  9. Data Labeling • Used low-fidelity text replays • Maintains good inter-rater reliability and efficient in aiding coders to label student disengagement (Baker et al. 2006) • Labels • Confused • Not Confused • Bad Clip • Cohen’s Kappa between raters: 0.77

  10. Data Labeling • Filter out “bad clips” • Remove clips where raters disagreed on the label • Left with 418 clips for model construction

  11. Model Construction • Used RapidMiner version 5.1 • Used J48 Decision Trees • Features were mined from the clips

  12. Model Construction • Feature set used: • Average time between compilations • Maximum time between compilations • Average time between compilations w/ errors • Maximum time between compilations w/ errors • Number of compilations w/ errors • Number of pairs consecutive compilations ending w/ the same error

  13. Kappa: 0.86

  14. Data Relabeling • Model was coded as a Java program • Had the program relabel all the 2,386 clips • Generated three sets of confused-not confused sequences • Correlated the percentage of the sequences of each student to their midterm exam scores

  15. Results

  16. Results

  17. Conclusion • Prolonged confusion has a negative impact on student’s performance • Resolved confusion has a positive impact on student’s performance • A certain amount of confusion is needed for learning

  18. On-going Work • Support the incorporation of tools for automatic detection of confusion in computer science learning environments • Redoing the sampling and clipping method

  19. Thank you Questions?

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