최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기정보교육학회논문지 = Journal of the Korean Association of Information Education, v.26 no.3, 2022년, pp.197 - 207
전인성 (한국교원대학교 컴퓨터교육과) , 송기상 (한국교원대학교 컴퓨터교육과)
In this paper, we develop a machine learning based block code generation and recommendation model for the purpose of reducing cognitive load of learners during coding education that learns the learner's block that has been made in the block programming environment using natural processing model and ...
Ala-Mutka, K. M. (2005). A survey of automated assessment approaches for programming assignments. Computer science education, 15(2), 83-102.
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Chow, K., Grabke, E. P., Lee, J., Yoo, J., Musselman, K. E., & Masani, K. (2017). Development of visual feedback training using functional electrical stimulation therapy for balance rehabilitation. STEM Fellowship Journal, 3(2), 1-2.
Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018). Intelligent tutoring systems for programming education: a systematic review. In Proceedings of the 20th Australasian Computing Education Conference, 53-62.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Edwards, S. H., & Perez-Quinones, M. A. (2008). Web-CAT: automatically grading programming assignments. In Proceedings of the 13th annual conference on Innovation and technology in computer science education, 328-328.
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., et al. (2021). GraphCodeBERT: Pre-training Code Representations with Data Flow. arXiv 2021, arXiv:2009.08366v3.
Guo, T.; Gao, H. Content Enhanced BERT-based Text-to-SQL Generation. arXiv 2020, arXiv:1910.07179v5.
Harvey, B., & Monig, J. (2010). Bringing "no ceiling" to scratch: Can one language serve kids and computer scientists. Proc. Constructionism, 1-10.
Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366-371.
Jeon, I. S., & Song, K. S. (2019). The Effect of learning analytics system towards learner's computational thinking capabilities. In Proceedings of the 2019 11th International Conference on Computer and Automation Engineering, 12-16.
Keuning, H., Heeren, B., & Jeuring, J. (2021). A tutoring system to learn code refactoring. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 562-568.
Kim, S. H. (2015). Analysis of Non-Computer Majors' Difficulties in Computational Thinking Education. The Journal of Korean association of computer education, 18(3), 49-57.
Koyya, P., Lee, Y., & Yang, J. (2013). Feedback for programming assignments using software-metrics and reference code. International Scholarly Research Notices.
Lamb, A., & Johnson, L. (2011). Scratch: computer programming for 21st century learners, Teacher Librarian, 38, 64-68.
Le, N. T., Strickroth, S., Gross, S., & Pinkwart, N. (2013). A review of AI-supported tutoring approaches for learning programming. Advanced Computational Methods for Knowledge Engineering, 267-279.
Lee, J. Y., Kim, J. M., & Lee, W. G. (2019). A Study on Partial Scoring in Text Based Program Evaluation. The Journal of Korean Association of Computer Education, 22(2), 29-38.
Lin, C. Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, 74-81.
Lister, R. (2011). Computing education research programming, syntax and cognitive load. ACM Inroads, 2(2), 21-22.
Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice: urban youth learning programming with scratch. In Proceedings of the 39th SIGCSE technical symposium on Computer science education, 367-371.
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 1-15.
Ministry of Education. (2015). Practical (technical/ family) and Information curriculum. (Separate Book 10), Sejong: Ministry of Education, Science and Technology.
Moreno-Leon, J., Robles, G., & Roman-Gonzalez, M. (2015). Dr. Scratch: analisis automatico de proyectos Scratch para evaluar y fomentar el Pensamiento Computacional. Revista de Educacion a Distancia(RED), 46.
Krogstie, J., Opdahl, A.L. and Brinkkemper, S. (2007). Conceptual Modeling in Information Systems Engineering, Springer.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 311-318.
Price, T. W., & Barnes, T. (2017). Position paper: Block-based programming should offer intelligent support for learners. In 2017 IEEE Blocks and Beyond Workshop (B&B), 65-68.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
Seo, J. H., & Kim, Y. S. (2016). Development and Application of Teaching-Learning Strategy for PBL-based Programming Education Using Reflection Journal in Elementary School. Journal of The Korean Association of Information Education, 20(5), 465-474.
Song, J. H., Lee, J. Y., Seo, Y. H., & Kim, H. S. (2021). A Study on the Development Policy of AI and SW Talent in the Fourth Industrial Revolution. Reserch Report RE-101, SPRI.
Spacco, J., & Pugh, W. (2006). Helping students appreciate test-driven development (TDD). In Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications, 907-913.
Sun, Z., Zhu, Q., Xiong, Y., Sun, Y., Mou, L., Zhang, L. (2019). TreeGen: A Tree-Based Transformer Architecture for Code Generation. arXiv 2019, arXiv:1911.09983v2.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems, 3104-3112.
Trower, J., & Gray, J. (2015). Blockly language creation and applications: Visual programming for media computation and bluetooth robotics control. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 5-5.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems, 5998-6008.
Von Wangenheim, C. G., Hauck, J. C., Demetrio, M. F., Pelle, R., da Cruz Alves, N., Barbosa, H., & Azevedo, L. F. (2018). CodeMaster--Automatic Assessment and Grading of App Inventor and Snap! Programs. Informatics in Education, 17(1), 117-150.
Vujosevic-Janicic, M., Nikolic, M., Tosic, D., & Kuncak, V. (2013). Software verification and graph similarity for automated evaluation of students' assignments. Information and Software Technology, 55(6), 1004-1016.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.