skip to main content
10.1145/3227609.3227662acmotherconferencesArticle/Chapter ViewAbstractPublication PageswimsConference Proceedingsconference-collections
research-article

Web-based educational ecosystem for automatization of teaching process and assessment of students

Authors Info & Claims
Published:25 June 2018Publication History

ABSTRACT

The complexity of the teaching process at universities creates many challenges. It becomes much harder for teachers to observe, control and adjust the learning process. Teaching process can be enhanced with use of different educational systems that not only help students construct their knowledge, but also make this process the most effective and efficient. One of the processes that could be automated and supported is the assessment of students' assignments. Three e-learning systems are currently used at different universities for teaching software design basics. The goal of this paper is to propose new integrated tool that can be used in university courses to support different stages of learning and evaluation of students' assignments. Such integrated system will be used to simplify the correction process of software design assignments.

References

  1. José Luis Fernández Alemán. 2011. Automated assessment in a programming tools course. IEEE Transactions on Education 54, 4: 576--581. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Noraida Haji Ali, Zarina Shukur, and Sufian Idris. 2007. A design of an assessment system for UML class diagram. In Proceedings - The 2007 International Conference on Computational Science and its Applications, ICCSA 2007, 539--544. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Helen Ashman, Tim Brailsford, Alexandra I Cristea, Quan Z Sheng, Craig Stewart, Elaine G Toms, and Vincent Wade. 2014. The ethical and social implications of personalization technologies for e-learning. Information & Management 51, 6: 819--832. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Mohammed Basheri, Malcolm Munro, and Liz Burd. 2013. Collaborative Learning Skills in Multi-touch Tables for UML Software Design. International Journal of Advanced Computer Science and Applications 4, 3: 60--66.Google ScholarGoogle ScholarCross RefCross Ref
  5. Julia Clemente, Jaime Ramírez, and Angélica De Antonio. 2011. A proposal for student modeling based on ontologies and diagnosis rules. Expert Systems with Applications 38, 7: 8066--8078. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Helder Correia, José Paulo Leal, and José Carlos Paiva. 2017. Enhancing feedback to students in automated diagram assessment. In OASIcs-OpenAccess Series in Informatics.Google ScholarGoogle Scholar
  7. Vittorio Cortellessa, Harshinder Singh, and Bojan Cukic. 2002. Early reliability assessment of UML based software models. In Proceedings of the 3rd international workshop on Software and performance, 302--309. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Fathi Essalmi, Leila Jemni Ben Ayed, Mohamed Jemni, Sabine Graf, and others. 2015. Generalized metrics for the analysis of E-learning personalization strategies. Computers in Human Behavior 48: 310--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Petri Ihantola, Tuukka Ahoniemi, Ville Karavirta, and Otto Seppälä. 2010. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli calling international conference on computing education research, 86--93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Mirjana Ivanović, Dejan Mitrović, Zoran Budimac, Boban Vesin, and Lubomir Jerinić. 2014. Different roles of agents in personalized programming learning environment.Google ScholarGoogle Scholar
  11. Rodi Jolak, Boban Vesin, and Michel R.V. Chaudron. 2017. Using voice commands for uml modelling support on interactive whiteboards: Insights & experiences. In ClbSE 2017 - XX Ibero-American Conference on Software Engineering.Google ScholarGoogle Scholar
  12. Rodi Jolak, Boban Vesin, Marcus Isaksson, and Michel R.V. Chaudron. 2016. Towards a new generation of software design environments: Supporting the use of informal and formal notations with OctoUML. In CEUR Workshop Proceedings, 3--10.Google ScholarGoogle Scholar
  13. Aleksandra Klašnja-Milićević, Mirjana Ivanović, Boban Vesin, and Zoran Budimac. 2017. Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence.Google ScholarGoogle Scholar
  14. Aleksandra Klašnja-Milićević, Boban Vesin, and Mirjana Ivanović. 2018. Social tagging strategy for enhancing e-learning experience. Computers & Education 118: 166--181. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, and Lakhmi C Jain. 2017. Design, Architecture and Interface of Protus 2.1 System. In E-Learning Systems. Springer International Publishing, 185--212.Google ScholarGoogle Scholar
  16. Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, and Lakhmi C Jain. 2017. Personalization in Protus 2.1 System. In E-Learning Systems. Springer International Publishing, 213--257.Google ScholarGoogle Scholar
  17. Amruth Kumar. 2006. A scalable solution for adaptive problem sequencing and its evaluation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 161--171. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Eugenijus Kurilovas, Inga Zilinskiene, and Valentina Dagiene. 2015. Recommending suitable learning paths according to learners' preferences: Experimental research results. Computers in Human Behavior 51: 945--951. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Katerina Mangaroska and Michail Giannakos. 2017. Learning Analytics for Learning Design: Towards Evidence-Driven Decisions to Enhance Learning. In European Conference on Technology Enhanced Learning, 428--433.Google ScholarGoogle ScholarCross RefCross Ref
  20. Antonija Mitrovic, Brent Martin, and Pramuditha Suraweera. 2007. Intelligent tutors for all: The constraint-based approach. IEEE Intelligent Systems 22, 4: 38--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Eileen O'Donnell, Séamus Lawless, Mary Sharp, and Vincent Wade. 2015. A review of personalised e-learning: Towards supporting learner diversity.Google ScholarGoogle Scholar
  22. Vreda Pieterse. 2013. Automated Assessment of Programming Assignments. 3rd Computer Science Education Research Conference on Computer Science Education Research 3, April: 45--56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Juan C Rodríguez-del-Pino, Enrique Rubio-Royo, and Zenón J Hernández-Figueroa. 2012. A Virtual Programming Lab for Moodle with automatic assessment and anti-plagiarism features. In Proceedings of the International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (EEE), 1.Google ScholarGoogle Scholar
  24. María Jesús Rodríguez-Triana, Alejandra Martínez-Monés, Juan I. Asensio-Pérez, and Yannis Dimitriadis. 2015. Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology 46, 2: 330--343.Google ScholarGoogle ScholarCross RefCross Ref
  25. Rohaida Romli, Shahida Sulaiman, and Kamal Zuhairi Zamli. 2010. Automatic programming assessment and test data generation a review on its approaches. In Information Technology (ITSim), 2010 International Symposium in, 1186--1192.Google ScholarGoogle ScholarCross RefCross Ref
  26. Manuel Rubio-Sánchez, Päivi Kinnunen, Cristóbal Pareja-Flores, and Ángel Velázquez-Iturbide. 2014. Student perception and usage of an automated programming assessment tool. Computers in Human Behavior 31: 453--460. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Rishabh Singh, Sumit Gulwani, and Armando Solar-Lezama. 2013. Automated feedback generation for introductory programming assignments. ACM SIGPLAN Notices 48, 6: 15--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Josep Soler, Imma Boada, Ferran Prados, Jordi Poch, and Ramon Fabregat. 2010. A web-based e-learning tool for UML class diagrams. In Education Engineering (EDUCON), 2010 IEEE, 973--979.Google ScholarGoogle Scholar
  29. Sergey Sosnovsky, Peter Brusilovsky, Michael Yudelson, Antonija Mitrovic, Moffat Mathews, and Amruth Kumar. 2009. Semantic Integration of Adaptive Educational Systems. Advances in Ubiquitous User Modelling: 134--158.Google ScholarGoogle Scholar
  30. Thomas Staubitz, Hauke Klement, Jan Renz, Ralf Teusner, and Christoph Meinel. 2015. Towards practical programming exercises and automated assessment in Massive Open Online Courses. In Teaching, Assessment, and Learning for Engineering (TALE), 2015 IEEE International Conference on, 23--30.Google ScholarGoogle Scholar
  31. Dave R. Stikkolorum, Truong Ho-Quang, and Michel R V Chaudron. 2015. Revealing Students' UML Class Diagram Modelling Strategies with WebUML and LogViz. In Proceedings - 41st Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2015, 275--279. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Dave R Stikkolorum, Truong Ho-Quang, Bilal Karasneh, and Michel R V Chaudron. 2015. Uncovering Students' Common Difficulties and Strategies During a Class Diagram Design Process: an Online Experiment. In Proceedings of the {MODELS} Educators Symposium, Ottawa, Canada, September 29, 2015. ({CEUR} Workshop Proceedings), 29--42. Retrieved from http://ceurws.org/Vol-1555/4.pdfGoogle ScholarGoogle Scholar
  33. Michael Striewe and Michael Goedicke. 2011. Automated checks on UML diagrams. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education, 38--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Michael Striewe and Michael Goedicke. 2014. Automated assessment of UML activity diagrams. In Proceedings of the 2014 conference on Innovation & technology in computer science education, 336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Hallvard Trætteberg and Trond Aalberg. 2006. JExercise: a specification-based and test-driven exercise support plugin for Eclipse. In Proceedings of the 2006 OOPSLA workshop on Eclipse technology eXchange, 70--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Boban Vesin, Mirjana Ivanović, Aleksandra Klašnja-Milićević, and Zoran Budimac. 2011. Rule-based reasoning for building learner model in programming tutoring system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 154--163. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Boban Vesin, Rodi Jolak, and Michel R V M.R.V. Chaudron. 2017. OctoUML: An Environment for Exploratory and Collaborative Software Design. In Proceedings of the 39th International Conference on Software Engineering Companion (ICSE-C '17), 7--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Andrii Vozniuk, Sten Govaerts, and Denis Gillet. 2013. Towards portable learning analytics dashboards. In Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013, 412--416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Tiantian Wang, Xiaohong Su, Peijun Ma, Yuying Wang, and Kuanquan Wang. 2011. Ability-training-oriented automated assessment in introductory programming course. Computers & Education 56, 1: 220--226. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Thomas Wanner and Edward Palmer. 2015. Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers & Education 51, 6: 1. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Web-based educational ecosystem for automatization of teaching process and assessment of students

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics
        June 2018
        398 pages

        Copyright © 2018 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 June 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate140of278submissions,50%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader