Skip to main content

Double-Layer Controller for Detecting Learners’ Erroneous Knowledge in Database Programming

  • Conference paper
  • First Online:
Intelligent Tutoring Systems (ITS 2022)

Abstract

Adaptive learning systems employ educational techniques and use computer algorithms to orchestrate the interaction with the learner. One example of their activities is the error detection and diagnosis. Error diagnosis serves for identifying the learners’ mistakes, their nature and the reason for happening. This module is important since it can help learners advance their knowledge. In view of this compelling need, this paper presents a double-layer controller for detecting learners’ erroneous knowledge in the tutoring of database programming, and specifically the Structured Query Language. The controller can reason about two main error categories, i.e. syntax and logic errors, and holds syntax and logical check operators. For the error diagnosis process, the syntax check operator incorporates a string-matching similarity technique, while the logical check operator incorporates the Start End Mid algorithm and the Sørensen–Dice coefficient. The educational software for the database programming, that incorporates the double-layer controller, was evaluated and the results showed that the presented mechanism has a significant effect on learners’ performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rüdian, S., Pinkwart, N.: Generating adaptive and personalized language learning online courses in Moodle with individual learning paths using templates. In: 2021 IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 53–55 (2021). https://doi.org/10.1109/ICALT52272.2021.00024

  2. Troussas, C., Krouska, A., Alepis, E., Virvou, M.: Intelligent and adaptive tutoring through a social network for higher education. New Rev. Hypermedia Multimedia 26(3–4), 138–167 (2020). https://doi.org/10.1080/13614568.2021.1908436

    Article  Google Scholar 

  3. Chiu, T.K.F., Meng, H., Chai, C.-S., King, I., Wong, S., Yam, Y.: Creation and evaluation of a pretertiary Artificial Intelligence (AI) curriculum. IEEE Trans. Educ. 65(1), 30–39 (2022). https://doi.org/10.1109/TE.2021.3085878

    Article  Google Scholar 

  4. Huamani, G.T., Inga, P.M.T.: WIP Adaptive evaluation for a systems theory course according to the learning context. In: 2021 IEEE World Conference on Engineering Education (EDUNINE), pp. 1–4 (2021). https://doi.org/10.1109/EDUNINE51952.2021.9429166

  5. Troussas, C., Krouska, A., Sgouropoulou, C.: A novel teaching strategy through adaptive learning activities for computer programming. IEEE Trans. Educ. 64(2), 103–109 (2021). https://doi.org/10.1109/TE.2020.3012744

    Article  Google Scholar 

  6. Xu, S., Chee, Y.S.: Transformation-based diagnosis of student programs for programming tutoring systems. IEEE Trans. Software Eng. 29(4), 360–384 (2003). https://doi.org/10.1109/TSE.2003.1191799

    Article  Google Scholar 

  7. Henley, A.Z., Ball, J., Klein, B., Rutter, A., Lee, D.: An inquisitive code editor for addressing novice programmers’ misconceptions of program behavior. In: Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), Madrid, Spain, 25–28 May 2021, pp. 165–170 (2021)

    Google Scholar 

  8. Lai, A.F., Wu, T.T., Lee, G.Y., Lai, H.Y.: Developing a web-based simulation-based learning system for enhancing concepts of linked-list structures in data structures curriculum. In: Proceedings of the 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, Malaysia, 2–4 December 2015, pp. 185–188 (2015)

    Google Scholar 

  9. Chang, J.-C.; Li, S.-C.; Chang, A.; Chang, M.: A SCORM/IMS compliance online test and diagnosis system. In: Proceedings of the 2006 7th International Conference on Information Technology Based Higher Education and Training, Ultimo, Australia, 10–13 July 2006, pp. 343–352 (2006)

    Google Scholar 

  10. Barker, S., Douglas, P.: An intelligent tutoring system for program semantics. In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC 2005), Las Vegas, NV, USA, 4–6 April 2005, vol. 1, pp. 482–487

    Google Scholar 

  11. Khalife, J.: Threshold for the introduction of programming: providing learners with a simple computer model. In: Proceedings of the 28th International Conference on Information Technology Interfaces, Cavtat, Croatia, 19–22 June 2006, pp. 71–76 (2006)

    Google Scholar 

  12. Troussas, C., Krouska, A., Virvou, M.: Injecting intelligence into learning management systems: the case of adaptive grain-size instruction. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6 (2019). https://doi.org/10.1109/IISA.2019.8900779

  13. Krugel, J., et al.: Automated measurement of competencies and generation of feedback in object-oriented programming courses. In: Proceedings of the 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, 27–30 April 2020, pp. 329–338 (2020)

    Google Scholar 

  14. Troussas, C., Krouska, A., Virvou, M.: NLP-based error analysis and dynamic motivation techniques in mobile learning. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8 (2019). https://doi.org/10.1109/IISA.2019.8900729

  15. Leal-Flores, A.J., Gonzalez-Guerra, L.H.: Teamwork with an automatic tutoring environment as learning strategy in programming courses. In: 2021 IEEE Global Engineering Education Conference (EDUCON), pp. 131–135 (2021). https://doi.org/10.1109/EDUCON46332.2021.9454102

  16. Algaraibeh, S.M., Dousay, T.A., Jeffery, C.L.: Integrated learning development environment for learning and teaching C/C++ language to novice programmers. In: 2020 IEEE Frontiers in Education Conference (FIE), pp. 1–5 (2020). https://doi.org/10.1109/FIE44824.2020.9273887

  17. Thurner, V.: Fostering the comprehension of the object-oriented programming paradigm by a virtual lab exercise. In: 2019 5th Experiment International Conference (exp.at 2019), pp. 137–142 (2019). https://doi.org/10.1109/EXPAT.2019.8876484

  18. Paladines, J., Ramirez, J.: A systematic literature review of intelligent tutoring systems with dialogue in natural language. IEEE Access 8, 164246–164267 (2020). https://doi.org/10.1109/ACCESS.2020.3021383

    Article  Google Scholar 

  19. Ardiansah, J.T., Wibawa, A.P., Widiyaningtyas, T., Yasuhisa, O.: SQL logic error detection using start end mid algorithm. Knowl. Eng. Data Sci. (KEDS) 1(1), 33–38 (2018). https://doi.org/10.17977/um018v1i12017p33-38.pISSN 2597–4602

  20. Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Kongelige Danske Videnskabernes Selskab. 5(4), 1–34 (1948)

    Google Scholar 

  21. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945). https://doi.org/10.2307/1932409.JSTOR1932409

    Article  Google Scholar 

  22. Ukkonen, E.: Approximate string-matching with q-grams and maximal matches. Theoret. Comput. Sci. 92(1), 191–211 (1992)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christos Troussas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Troussas, C., Krouska, A., Sgouropoulou, C. (2022). Double-Layer Controller for Detecting Learners’ Erroneous Knowledge in Database Programming. In: Crossley, S., Popescu, E. (eds) Intelligent Tutoring Systems. ITS 2022. Lecture Notes in Computer Science, vol 13284. Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09680-8_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09679-2

  • Online ISBN: 978-3-031-09680-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics