Douglas Lusa Krug
Yifan Zhang
David C. Shepherd
ABSTRACT
Broadening participation in computer science has been widely studied, creating many different techniques to attract, motivate, and engage students. A common meta-strategy is to use an outside domain as a hook, using the concepts in that domain to teach computer science. These domains are selected to interest the student, but students often lack a strong background in these domains. Therefore, a strategy designed to increase students' interest, motivation, and engagement could actually create more barriers for students, who now are faced with learning two new topics. To reduce this potential barrier in the domain of music, this paper presents the use of automated, immediate feedback during programming activities at a summer camp that uses music to teach foundational programming concepts. The feedback guides students musically, correcting notes that are out-of-key or rhythmic phrases that are too long or short, allowing students to focus their learning on the computer science concepts. This paper compares the correctness of students that received automated feedback with students that did not, which shows the effectiveness of the feedback. Follow up focus groups with students confirmed this quantitative data, with students claiming that the feedback was not only useful but that the activities would be much more challenging without the feedback.
- Samuel Aaron and Alan F. Blackwell. 2013. From Sonic Pi to Overtone: Creative Musical Experiences with Domain-Specific and Functional Languages. In Proceedings of the First ACM SIGPLAN Workshop on Functional Art, Music, Modeling & Design (Boston, Massachusetts, USA) (FARM '13). Association for Computing Machinery, New York, NY, USA, 35--46. https://doi.org/10.1145/2505341.2505346Google Scholar
Digital Library
- Luciana Benotti, Federico Aloi, Franco Bulgarelli, and Marcos J. Gomez. 2018. The Effect of a Web-Based Coding Tool with Automatic Feedback on Students' Performance and Perceptions. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (Baltimore, Maryland, USA) (SIGCSE '18). Association for Computing Machinery, New York, NY, USA, 2--7. https://doi.org/10.1145/3159450.3159579Google ScholarDigital Library
- Galina Deeva, Daria Bogdanova, Estefanía Serral, Monique Snoeck, and Jochen De Weerdt. 2021. A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education, Vol. 162 (2021), 104094. https://doi.org/10.1016/j.compedu.2020.104094Google ScholarCross Ref
- Ayelet Fishbach and Stacey R Finkelstein. 2012. How feedback influences persistence, disengagement, and change in goal pursuit. Goal-directed behavior (2012), 203--230.Google Scholar
- Jason Freeman, Brian Magerko, Tom McKlin, Mike Reilly, Justin Permar, Cameron Summers, and Eric Fruchter. 2014. Engaging Underrepresented Groups in High School Introductory Computing through Computational Remixing with EarSketch. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (Atlanta, Georgia, USA) (SIGCSE '14). Association for Computing Machinery, New York, NY, USA, 85--90. https://doi.org/10.1145/2538862.2538906Google ScholarDigital Library
- Alex Gerdes, Bastiaan Heeren, Johan Jeuring, and L Thomas Van Binsbergen. 2017. Ask-Elle: an adaptable programming tutor for Haskell giving automated feedback. International Journal of Artificial Intelligence in Education, Vol. 27, 1 (2017), 65--100.Google ScholarCross Ref
- Jamie Gorson, Nikita Patel, Elham Beheshti, Brian Magerko, and Michael Horn. 2017. TunePad: Computational thinking through sound composition. In Proceedings of the 2017 Conference on Interaction Design and Children. 484--489.Google ScholarDigital Library
- Sumit Gulwani, Ivan Radivc ek, and Florian Zuleger. 2018. Automated clustering and program repair for introductory programming assignments. ACM SIGPLAN Notices, Vol. 53, 4 (2018), 465--480.Google ScholarDigital Library
- Philip J Guo. 2013. Online python tutor: embeddable web-based program visualization for cs education. In Proceeding of the 44th ACM technical symposium on Computer science education. 579--584.Google Scholar
- Michael Horn, Amartya Banerjee, Melanie West, Nichole Pinkard, Amy Pratt, Jason Freeman, Brian Magerko, and Tom McKlin. 2020. TunePad: Engaging learners at the intersection of music and code. (2020).Google Scholar
- Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2018. A Systematic Literature Review of Automated Feedback Generation for Programming Exercises. ACM Trans. Comput. Educ., Vol. 19, 1, Article 3 (sep 2018), 43 pages. https://doi.org/10.1145/3231711Google ScholarDigital Library
- Irene Lee, Fred Martin, Jill Denner, Bob Coulter, Walter Allan, Jeri Erickson, Joyce Malyn-Smith, and Linda Werner. 2011. Computational Thinking for Youth in Practice. ACM Inroads, Vol. 2, 1 (Feb. 2011), 32--37. https://doi.org/10.1145/1929887.1929902Google ScholarDigital Library
- Colleen M Lewis, Ken Yasuhara, and Ruth E Anderson. 2011. Deciding to major in computer science: a grounded theory of students' self-assessment of ability. In Proceedings of the seventh international workshop on Computing education research. 3--10.Google ScholarDigital Library
- Douglas Lusa Krug, Edtwuan Bowman, Taylor Barnett, Lori Pollock, and David Shepherd. 2021. Code Beats: A Virtual Camp for Middle Schoolers Coding Hip Hop. Association for Computing Machinery, New York, NY, USA, 397--403. https://doi.org/10.1145/3408877.3432424Google ScholarDigital Library
- Brian Magerko, Jason Freeman, Tom McKlin, Scott McCoid, Tom Jenkins, and Elise Livingston. 2013. Tackling Engagement in Computing with Computational Music Remixing. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (Denver, Colorado, USA) (SIGCSE '13). Association for Computing Machinery, New York, NY, USA, 657--662. https://doi.org/10.1145/2445196.2445390Google ScholarDigital Library
- Bill Manaris, Blake Stevens, and Andrew R Brown. 2016. JythonMusic: An environment for teaching algorithmic music composition, dynamic coding and musical performativity. Journal of Music, Technology & Education, Vol. 9, 1 (2016), 33--56.Google ScholarCross Ref
- Samiha Marwan, Ge Gao, Susan Fisk, Thomas W. Price, and Tiffany Barnes. 2020. Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science. In Proceedings of the 2020 ACM Conference on International Computing Education Research (Virtual Event, New Zealand) (ICER '20). Association for Computing Machinery, New York, NY, USA, 194--203. https://doi.org/10.1145/3372782.3406264Google ScholarDigital Library
- Samiha Marwan, Joseph Jay Williams, and Thomas Price. 2019. An Evaluation of the Impact of Automated Programming Hints on Performance and Learning. In Proceedings of the 2019 ACM Conference on International Computing Education Research (Toronto ON, Canada) (ICER '19). Association for Computing Machinery, New York, NY, USA, 61--70. https://doi.org/10.1145/3291279.3339420Google ScholarDigital Library
- Jessica McBroom, Irena Koprinska, and Kalina Yacef. 2021. A Survey of Automated Programming Hint Generation: The HINTS Framework. ACM Comput. Surv., Vol. 54, 8, Article 172 (oct 2021), 27 pages. https://doi.org/10.1145/3469885Google ScholarDigital Library
- Antonija Mitrovic, Stellan Ohlsson, and Devon K. Barrow. 2013. The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, Vol. 60, 1 (2013), 264--272. https://doi.org/10.1016/j.compedu.2012.07.002Google ScholarDigital Library
- Susanne Narciss. 2008. Feedback strategies for interactive learning tasks. In Handbook of research on educational communications and technology. Routledge, 125--143.Google Scholar
- Ruan Reis, Gustavo Soares, Melina Mongiovi, and Wilkerson L. Andrade. 2019. Evaluating Feedback Tools in Introductory Programming Classes. In 2019 IEEE Frontiers in Education Conference (FIE). 1--7. https://doi.org/10.1109/FIE43999.2019.9028418Google ScholarDigital Library
- Mary Catherine Scheeler, Kathy L Ruhl, and James K McAfee. 2004. Providing performance feedback to teachers: A review. Teacher education and special education, Vol. 27, 4 (2004), 396--407.Google ScholarCross Ref
- Allison Scott, Alexis Martin, Frieda McAlear, and Tia C. Madkins. 2016. Broadening Participation in Computer Science: Existing Out-of-School Initiatives and a Case Study. ACM Inroads, Vol. 7, 4 (Nov. 2016), 84--90. https://doi.org/10.1145/2994153Google ScholarDigital Library
- Valerie J Shute. 2008. Focus on formative feedback. Review of educational research, Vol. 78, 1 (2008), 153--189.Google Scholar
Index Terms
- Using Domain-Specific, Immediate Feedback to Support Students Learning Computer Programming to Make Music
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