+ Examining the Effect of Programming Experience When Using Snap Raquel Lawrence a Learn Da Learn Dialogue
+ Introduction n Created a parser n Developed a parser for data in a database n Study n Used data from two studies n Research Hypothesis n Students with prior programming experience will have a relatively better interaction with Snap than students with little prior programming experience 2
+ Background n Snap! Visual Programming language n A free browser-based graphical programming language tool designed to teach CS concepts to students n The benefits of pair programming [2] : n Higher retention rates n Increase in meaningful interaction between students and instructor n Increase in student enjoyment of programming 3
+ Related Work n Effects of pair programming performance [1] n Data collected on 600 students programmed who individually and in pairs n Pairs produced better programs n Pairs completed the course at higher rates n Scored significantly higher on programming assignments n Did just as well as individual students on final exam n Several advantages to pair programming, however, not yet proven if it’s advantageous to student’s performance in the long term (exam scores) [2] 4
+ Prior Work n Prior study conducted at North Carolina State University to model students’ problem-solving modes [3] n Students worked individually and in pairs n 14 individuals and 8 pairs assigned randomly n Completed a rock, paper, scissors game in Snap n No prior visual programming experience n Prior java programming experience (nearly 2 full semesters) 5
+ Implementation n 6 pairs of undergraduate students from introductory programming course n Remote collaboration, students worked in Google Hangouts, with screen sharing using only text chat for communication n Given pre/post test and survey and 95 minutes in total to complete the assignment 6
+ Snap n Visual programming language Image: Rodríguez, F., Boyer, K.: Discovering Individual and Collaborative Problem-Solving Modes with Hidden Markov Models. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), pp. 7 408-418 (2015)
+ Cont’d n SAX Parser was developed to read activity logs of student’s actions within Snap interface 8
+ Parser 9
+ Results 700 600 500 No. of Events 400 Inexperienced Pairs 300 Experienced Pairs 200 100 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Duration (in minutes) 10
+ Discussion/Conclusion n Mann-Whitney U-Test n Using the Mann-Whitney U-Test, there was no significant difference in the number of actions between conditions n Activity duration for inexperienced pairs was significantly higher than for experienced pairs n Suggests that the experienced pairs had less trouble handling the interface. Inexperienced ¡Pairs ¡ Mean ¡Dura)on ¡ 64.2 ¡ Std. ¡Dev ¡Dura)on ¡ 19.3 ¡ Mean ¡ ¡# ¡of ¡Events ¡ 379.7 ¡ Std. ¡Dev ¡# ¡of ¡Events ¡ 205.6 ¡ Experienced ¡Pairs ¡ Mean ¡Dura)on ¡ 34 ¡ Std. ¡Dev ¡Dura)on ¡ 6.3 ¡ Mean ¡ ¡# ¡of ¡Events ¡ 337.1 ¡ 11 Std. ¡Dev ¡Dura)on ¡ 76 ¡
+ Threats n Technical Difficulties n Two groups did not finish n Computers crashed 3 times n Slow computer speed n Difference Study Design n One study had explicitly textual discourse between partners n Takes longer to type than to speak during collaboration n Partners type at different speeds n Mann Whitney U -Test n Used for small data sets that are too small to be significant 12
+ Future Work n Conduct the study once again without technical difficulties and with a bigger sample size n Analyzing the effect of dialogue between the pairs. 13
+ References McDowell, C., Werner, L., Bullock, H., Fernald, J.: The effects of pair- 1. programming on performance in an introductory programming course. In: Proceedings of the 33rd ACM Technical Symposium on Computer Science Education (SIGCSE 2002), pp. 38–42 (2002) Radermacher, A., Gursimran W ., Rummelt, R.,: Improving Student Learning 2. Outcomes with Pair Programming. In: Proceedings of the ninth International conference on Computing education research (ICER '12) , pp. 87-92 (2012) Rodríguez, F., Boyer, K.: Discovering Individual and Collaborative Problem- 3. Solving Modes with Hidden Markov Models. In: Proceedings of the 17 th International Conference on Artificial Intelligence in Education (AIED 2015), pp. 408-418 (2015) 14
+ Questions? 15
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