Open Access
Issue |
SHS Web Conf.
Volume 77, 2020
The 2nd ACM Chapter International Conference on Educational Technology, Language and Technical Communication (ETLTC2020)
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|
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Article Number | 01004 | |
Number of page(s) | 11 | |
Section | Educational Technology | |
DOI | https://doi.org/10.1051/shsconf/20207701004 | |
Published online | 08 May 2020 |
- Miguel A. Revilla, Shahriar Manzoor, Rujia Liu. 2008. Competitive Learning in Informatics: The UVa Online Judge Experience, Olympiads in Informatics 2 (2008), 131 – 148. [Google Scholar]
- Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, Tomasz Sternal. 2018. A Survey on Online Judge Systems and Their Applications, ACM Computing Surveys (CSUR) 51, 1, Article 3 (2018). [Google Scholar]
- IEEE Standard for Learning Object Metadata 2002. IEEE Std 1484.12.1-2002, 1–40 (2002). [Google Scholar]
- Yutaka Watanobe. Aizu Online Judge, https://onlinejudge.u-aizu.ac.jp/ [Google Scholar]
- Yutaka Watanobe. 2015. Development and Operation of an Online Judge System, IPSJ Magazine 56, 10 (2015), 998 – 1005. [Google Scholar]
- Luisa M. Regueras, Elena Verdu, Juan P. de Castro, Maria A. Perez, Maria J. Verdu. 2009. A Proposal of User Interface for a Distributed Asynchronous Remote Evaluation System: An Evolution of the QUESTOURnament Tool. In Proceedings of the 9th IEEE International Conference on Advanced Learning Technologies, 75–77. [Google Scholar]
- Wenju Zhou, Yigong Pan, Yinghua Zhou, Guangzhong Sun. 2018. The framework of a new online judge system for programming education, In Proceedings of ACM Turing Celebration Conference, 9–14. [CrossRef] [Google Scholar]
- Jordi Petit, Salvador Roura, Josep Carmona, Jordi Cortadella, Jordi Duch, Omer Gimenez, Anaga Mani, Jan Mas, Enric Rodriguez-Carbonell, Enric Rubio, Enric de San Pedro, and Divya Venkataramani. 2018. IEEE Transactions on Learning Technologies 11, 3 (2018). [Google Scholar]
- Jose Paulo Leal, Fernando Silva. 2003. Mooshak: a Web!>based multi!>site programming contest system, Software-Practice & Experience 33, 6 (2003), 567–581. https://doi.org/10.1002/spe.522 [Google Scholar]
- Katerina Georgouli, Pedro Guerreiro. 2010. Incorporating an Automatic Judge into Blended Learning Programming Activities, Lecture Notes in Computer Science, Vol. 6483, 81–90. [Google Scholar]
- David G. Messerschmitt, Clemens Szyperski. 2003. Software Ecosystem: Understanding and Indispensable Technology and Industry, 1st ed. The MIT Press. [Google Scholar]
- Konstantinos Manikas, Klaus Marius Hansen. 2013. Software ecosystems - a systematic literature review, Journal of Systems and Software 86, 5 (2013), 1294–1306. [CrossRef] [Google Scholar]
- Desamparados Blazquez, Josep Domenech. 2018. Big Data sources and methods for social and economic analyses, Technological Forecasting and Social Change 130, 99–113 (2018). [CrossRef] [Google Scholar]
- Jimmy Lin, Dmitriy Ryaboy. 2013. Scaling big data mining infrastructure: the twitter experience, ACM SIGKDD Explorations Newsletter 14 (2013), 6–19. [CrossRef] [Google Scholar]
- Roshan Sumbaly, Jay Kreps, Sam Shah. 2013. The big data ecosystem at linkedin, In Proceedings of International Conference on Management of data, 1125–1134. [Google Scholar]
- Qinghua Zheng, Huan He, Tian Ma, Ni Xue, Bing Li, Bo Dong. 2014. Big Log Analysis for E-Learning Ecosystem, In Proceedings of e-Business Engineering, 258–263. [Google Scholar]
- Paulo Blikstein. 2011. Using learning analytics to assess students’ behavior in open-ended programming tasks, In Proceedings of International Conference on Learning Analytics and Knowledge, 110–116. [CrossRef] [Google Scholar]
- Paulo Blikstein, Marcelo Worsley, Chris Piech, Mehran Sahami, Steven Cooper, Daphne Koller. 2014. Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming, Journal of the Learning Sciences 23, 4 (2014), 561-599. [CrossRef] [Google Scholar]
- Elena L. Glassman, Jeremy Scott, Rishabh Singh, Philip J. Guo, Robert C. Miller. 2015. OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale, ACM Transactions on Computer-Human Interaction (TOCHI) - Special Issue on Online Learning at Scale, 22, 2, Article 7 (2015). [Google Scholar]
- Bo Dong, Qinghua Zheng, Jie Yang, Haifei Li, Mu Qiao. 2009. An E-learning Ecosystem Based on Cloud Computing Infrastructure, In Proceedings of 9th IEEE International Conference on Advanced Learning Technologies, 125 – 127. [Google Scholar]
- Lorna Uden, Ince Trisnawaty Wangsa, Ernesto Damiani. 2007. The future of E-learning: E-learning ecosystem, In Proceedings of Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, 113 – 117. [CrossRef] [Google Scholar]
- Hemant Kumar Mehta, Manohar Chandwani, Priyesh Kanungo. 2010. Towards development of a distributed e-Learning ecosystem, In Proceedings of International Conference on Technology for Education, 68 – 71. [Google Scholar]
- Roy T. Fielding, Richard N. Taylor. 2002. Principled design of the modern Web architecture, ACM Transactions on Internet Technology (TOIT) 2, 2 (2002), 115–150. [Google Scholar]
- Fitzgerald, Sue and McCauley, Renée and Hanks, Brian and Murphy, Laurie and Simon, Beth and Zander, Carol. 2009. Debugging from the student perspective, IEEE Transactions on Education 53, 3 (2009), 390–396. [CrossRef] [Google Scholar]
- Ahmadzadeh, Marzieh and Elliman, Dave and Higgins, Colin. 2005. An analysis of patterns of debugging among novice computer science students, Acm sigcse bulletin 37, 3 (2005), 84–88. [Google Scholar]
- Fitzgerald, Sue and Lewandowski, Gary and McCauley, Renee and Murphy, Laurie and Simon, Beth and Thomas, Lynda and Zander, Carol. 2008. Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers, Computer Science Education 18, 2 (2008), 93–116. [CrossRef] [Google Scholar]
- Yunosuke Teshima, Yutaka Watanobe. 2018. Bug Detection based on LSTM Networks and Solution Codes, In Proceedings of The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), 3531-3536. [Google Scholar]
- Yuto Yoshizawa, Yutaka Watanobe. 2019. Logic Error Detection System based on Structure Pattern and Error Degree, Advances in Science, Technology and Engineering Systems Journal 4, 5 (2019),1-15. [CrossRef] [Google Scholar]
- Taku Matsumoto, Yutaka Watanobe. 2019. Hybrid intelligence for logic error detection, In Proceedings of The 18th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2019), 120-131. [Google Scholar]
- Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton. 2018. A Survey of Machine Learning for Big Code and Naturalness, ACM Computing Surveys 51, 4, Article 81 (2018). [Google Scholar]
- Tomohiro Saito, Yutaka Watanobe. 2020. Learning Path Recommendation System for Programming Education based on Neural Networks, International Journal of Distance Education Technologies (IJDET) 18, 1, Article 4 (2019). [Google Scholar]
- Chowdhury Md Intisar, Yutaka Watanobe. 2018. Cluster Analysis to Estimate the Difficulty of Programming Problems, In Proceedings of 3rd International Conference on Applications in Information Technology (ICAIT), 23–28. [Google Scholar]
- Chowdhury Md Intisar, Yutaka Watanobe, Manoj Poudel, Subhash Bhalla. 2019. Classification of Programming Problems based on Topic Modeling, In Proceedings of International Conference on Information and Education Technology (ICIET), 275-283. [Google Scholar]
- Chowdhury Md Intisar, Yutaka Watanobe. 2018. Classification of Online Judge Programmers based on Rule Extraction from Self Organizing Feature Map, In Proceedings of 9th IEEE International Conference on Awareness Science and Technology (iCAST), 308–313. [Google Scholar]
- Hiroki Ohashi, Yutaka Watanobe. 2019. Convolutional Neural Network for Classification of Source Codes, In Proceedings of IEEE 13th International Symposium on Embedded Multicore/Manycore Systems-on-Chip (MCSoC-2019), 194–200. [Google Scholar]
- Wayne Xin Zhao, Wenhui Zhang, Yulan He, Xing Xie, Ji-Rong Wen. 2018. Automatically Learning Topics and Difficulty Levels of Problems in Online Judge Systems, ACM Transaction on Information Systems (TOIS) 36, 3, Article 27 (2018). [Google Scholar]
- Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2015. Learning to generate pseudo-code from source code using statistical machine translation, In Proceedings of 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), 574–584 (2015). [Google Scholar]
- Kenta Terada, Yutaka Watanobe. 2019. Code Completion for Programming Education based on Recurrent Neural Network, In Proceedings of 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA), 109–114. [CrossRef] [Google Scholar]
- Kenta Terada, Yutaka Watanobe. 2019. Automatic Generation of Fill-in-the-Blank Programming Problems, In Proceedings of IEEE 13th International Symposium on Embedded Multicore/Manycore Systems-on-Chip (MCSoC-2019), 187–193. [Google Scholar]
- Armando Fox, David A. Patterson, Samuel Joseph, Paul McCulloch. 2015. MAGIC: Massive Automated Grading in the Cloud, CHANGEE/WAPLA/HybridEd@EC-TEL (2015). [Google Scholar]
- Thomas Staubitz, Ralf Teusner, Christoph Meinel. 2017. Towards a repository for open auto-gradable programming exercises, In Proceedings of IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 66–73 (2017). [Google Scholar]
- Guillaume Derval, Anthony Gego, Pierre Reinbold, Benjamin Frantzen and Peter Van Roy. 2015. Automatic grading of programming exercises in a MOOC using the INGInious platform, In Proceedings of European MOOC Stakeholder Summit 2015, 86–91. [Google Scholar]
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