1 / 20

Analyzing Students' Behavior in a Beginner's Programming Course

Department of Informatics, University of Rijeka Radmile Matejčić 2 , 51000 Rijeka, Croatia http://www.inf.uniri.hr. Analyzing Students' Behavior in a Beginner's Programming Course. Marija Brkić , Higher Teaching Assistant mbrkic@inf.uniri.hr Maja Matetić , Ass ociate Professor

cody
Download Presentation

Analyzing Students' Behavior in a Beginner's Programming Course

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Department of Informatics, University of Rijeka Radmile Matejčić 2, 51000 Rijeka, Croatia http://www.inf.uniri.hr Analyzing Students' Behavior in a Beginner's Programming Course Marija Brkić, Higher Teaching Assistant mbrkic@inf.uniri.hr Maja Matetić, Associate Professor majam@inf.uniri.hr

  2. Why are we doing this? • Task 6 of the strategy of the University of Rijeka for the period 2008-2013: • Pass rate increase for 2nd year enrollment to 75% • The course pass rate in the academic year 2012/2013 was 61%. • The course pass rate in the academic year 2011/2012 was 63%. • We are facing a falling pass rate!!!

  3. Programming 1 mandatory course 1st year of undergraduate study of Informatics C++ procedural programming 82 students in our case study LMS Moodle, supplemental instruction classes Course info

  4. Visualization as a pre-processing tool

  5. Grade distribution

  6. Avoiding examination

  7. Repeating the course

  8. Additional activity I

  9. Additional activity II

  10. Relationship with the final grade

  11. Pre-processing • Missing values for one part of activities have been replaced with minimum values • Examples with the remaining missing values have been filtered out • Additional attribute has been generated (Labs)

  12. Data mining techniques • Association • Classification • Clustering • Outlier detection

  13. Association rules

  14. Classification rules

  15. Clustering

  16. Outlier detection

  17. Student comments on newly introduced activities • Official evaluation • I liked the labs because they force us to work on new materials continuously • I liked the labs because they encourage us to exercise regularly • professors gave us a lot of materials and organized everything perfectly – from labs to supplemental instruction • Class evaluation • the labs made us work continuously • it is a good idea for getting scores, though the evaluation system should be less harsh and give partial credits • excellent idea set to practice perfectly • labs helped a lot for continuous engagement

  18. Future work • Time analysis (self-evaluation) • Log analysis (forum, laboratory exercises, etc.) • Classification issues

  19. Conclusion • We are actually not facing a falling pass rate!!! 

  20. Thank You for your attention!

More Related