Personifying programming tool feedback improves novice programmers' learningMichael J. Lee, Andrew J. Ko
Publikationsdatum:
Zu finden in: ICER 2011 (Seite 109 bis 116), 2011
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Zusammenfassungen
Many novice programmers view programming tools as all-knowing, infallible authorities about what is right and wrong about code. This misconception is particularly detrimental to beginners, who may view the cold, terse, and often judgmental errors from compilers as a sign of personal failure. It is possible, however, that attributing this failure to the computer, rather than the learner, may improve learners' motivation to program. To test this hypothesis, we present Gidget, a game where the eponymous robot protagonist is cast as a fallible character that blames itself for not being able to correctly write code to complete its missions. Players learn programming by working with Gidget to debug its problematic code. In a two-condition controlled experiment, we manipulated Gidget's level of personification in: communication style, sound effects, and image. We tested our game with 116 self-described novice programmers recruited on Amazon's Mechanical Turk and found that, when given the option to quit at any time, those in the experimental condition (with a personable Gidget) completed significantly more levels in a similar amount of time. Participants in the control and experimental groups played the game for an average time of 39.4 minutes (SD=34.3) and 50.1 minutes (SD=42.6) respectively. These finding suggest that how programming tool feedback is portrayed to learners can have a significant impact on motivation to program and learning success.
Dieses Konferenz-Paper erwähnt ...
Begriffe KB IB clear | Lernenlearning , Motivationmotivation , Programmierenprogramming |
1 Erwähnungen
- ICER 2020 - International Computing Education Research Conference, Virtual Event, New Zealand, August 10-12, 2020 (Anthony V. Robins, Adon Moskal, Amy J. Ko, Renée McCauley) (2020)
- Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science (Samiha Marwan, Ge Gao, Susan R. Fisk, Thomas W. Price, Tiffany Barnes) (2020)
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