Leveraging Trends in Student Interaction to Enhance the Effectiveness of Sketch-Based Educational Software

  • Seth Polsley
  • Jaideep Ray
  • Trevor Nelligan
  • Michael Helms
  • Julie Linsey
  • Tracy Hammond
Chapter
First Online:
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

With the rapid adoption of software-based learning in classrooms, it is increasingly important to design more intelligent educational software, a goal of the emerging field of educational data mining. In this work, we analyze student activities from using a learning tool for engineers, Mechanix, in order to find trends that may be used to make the software a better tutor, combining its natural, sketch-based input with intelligent, experience-based feedback. We see a significant correlation between student performance and the amount of time they work on a problem before submitting; students who attempt to “game” the system by submitting their results too often perform worse than those who work longer (p< 0.05). We also found significance in the number of times a student attempted a problem before moving on, with a strong correlation between being willing to switch among problems and better performance (p< 0.05). Overall, we find that student trends like these could be paired with machine learning techniques to make more intelligent educational tools.

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

Notes

Acknowledgments

This research was funded by NSF EEC Grant No. 1129525. The authors would like to thank Dr. Matthew Green for using Mechanix in his classroom for testing; the members of the iDreem and Sketch Recognition Labs, particularly Stephanie Valentine and David Turner for their significant contributions to Mechanix; and the Computer Science & Engineering department at Texas A&M University.

References

  1. 1.
    Allen IE, Seaman J (2007) Online nation: five years of growth in online learning. ERICGoogle Scholar
  2. 2.
    Atilola O, Field M, McTigue E, Hammond T, Linsey J (2011) Evaluation of a natural sketch interface for truss fbds and analysis. In: Frontiers in Education Conference (FIE), 2011, pp S2E-1–S2E-6. doi: 10.1109/FIE.2011.6142959
  3. 3.
    Atilola O, Field M, McTigue E, Hammond T, Linsey J (2011) Mechanix: a sketch recognition truss tutoring system. In: ASME 2011 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, New York, pp 645–654Google Scholar
  4. 4.
    Atilola O, Vides F, Mctigue EM, Linsey JS, Hammond TA (2012) Automatic identification of student misconceptions and errors for truss analysis. In: 119th American society for engineering education annual conference and exposition (ASEE). American Society for Engineering Education, Washington, DCGoogle Scholar
  5. 5.
    Atilola O, Valentine S, Kim HH, Turner D, McTigue E, Hammond T, Linsey J (2014) Mechanix: a natural sketch interface tool for teaching truss analysis and free-body diagrams. Artif Intel Eng Des Anal Manuf 28(02):169–192CrossRefGoogle Scholar
  6. 6.
    Baker RS, Corbett AT, Koedinger KR (2004) Detecting student misuse of intelligent tutoring systems. In: Intelligent tutoring systems. Springer, Heidelberg, pp 531–540Google Scholar
  7. 7.
    Baker RS, Corbett AT, Koedinger KR, Wagner AZ (2004) Off-task behavior in the cognitive tutor classroom: when students game the system. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 383–390Google Scholar
  8. 8.
    Baker RS, D’Mello SK, Rodrigo MMT, Graesser AC (2010) Better to be frustrated than bored: the incidence, persistence, and impact of learners cognitive-affective states during interactions with three different computer-based learning environments. Int J Hum-Comput Stud 68(4):223–241CrossRefGoogle Scholar
  9. 9.
    Champaign J, Colvin KF, Liu A, Fredericks C, Seaton D, Pritchard DE (2014) Correlating skill and improvement in 2 moocs with a student’s time on tasks. In: Proceedings of the first ACM conference on learning @ scale conference. ACM, pp 11–20Google Scholar
  10. 10.
    Dixon D, Prasad M, Hammond T (2010) Icandraw: using sketch recognition and corrective feedback to assist a user in drawing human faces. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 897–906Google Scholar
  11. 11.
    Field M, Valentine S, Linsey J, Hammond T (2011) Sketch recognition algorithms for comparing complex and unpredictable shapes. In: Proceedings of the twenty-second international joint conference on artificial intelligence, IJCAI ’11, vol 3. AAAI Press, pp 2436–2441Google Scholar
  12. 12.
    Forbus K, Usher J, Chang M (2015) Sketch worksheets: a brief summary. In: Hammond AAT, Valentine S, Payton M (eds) The impact of pen and touch technology on education. Part III, Chapter 10. Springer, New YorkGoogle Scholar
  13. 13.
    Hiltz SR, Turoff M (2005) Education goes digital: the evolution of online learning and the revolution in higher education. Commun ACM 48(10):59–64CrossRefGoogle Scholar
  14. 14.
    Nelligan T, Polsley S, Ray J, Helms M, Linsey J, Hammond T (2015) Mechanix: a sketch-based educational interface. In: Proceedings of the 2015 ACM international conference on intelligent user interfaceGoogle Scholar
  15. 15.
    Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Trans Syst, Man, Cybern, Part C: Appl Rev 40(6):601–618CrossRefGoogle Scholar
  16. 16.
    Siemens G, d Baker RS (2012) Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 252–254Google Scholar
  17. 17.
    Siyao L, Qianrang G (2011) The research on anti-cheating strategy of online examination system. In: 2011 2nd International conference on artificial intelligence, management science and electronic commerce (AIMSEC). IEEE, pp 1738–1741Google Scholar
  18. 18.
    Soga M, Matsuda N, Taki H (2008) A sketch learning support environment that gives area-dependent advice during drawing the sketch. Trans Jpn Soc Artif Intel 23:96–104CrossRefGoogle Scholar
  19. 19.
    Valentine S, Field M, Hammond T, Smith A (2011) A shape comparison technique for use in sketch-based tutoring systems. In: Proceedings of the 2011 intelligent user interfaces workshop on sketch recognition, IUI, vol 11. Palo AltoGoogle Scholar
  20. 20.
    Valentine S, Vides F, Lucchese G, Turner D, Kim H-H, Li W, Linsey J, Hammond T (2012) Mechanix: a sketch-based tutoring and grading system for free-body diagrams. AI Mag 34(1):55Google Scholar
  21. 21.
    Valentine S, Vides F, Lucchese G, Turner D, Kim H-H, Li W, Linsey J, Hammond T (2012) Mechanix: a sketch-based tutoring system for statics courses. In: IAAIGoogle Scholar
  22. 22.
    Valentine S, Lara-Garduno R, Linsey J, Hammond T (2015) Mechanix: a sketch based tutoring system that automatically corrects hand-sketched statics homework. In: Hammond T, Valentine S, Adler A, Payton M (eds) The impact of pen and touch technology on education. Springer, New YorkGoogle Scholar
  23. 23.
    Wetzel J, Forbus K (2015) Increasing student confidence in engineering sketching via a software coach. In: Hammond AAT, Valentine S, Payton M (eds) The impact of pen and touch technology on education. Part III, Chapter 11. Springer International Publishing, Springer, New YorkGoogle Scholar
  24. 24.
    Yin P, Forbus K, Usher J, Sageman B, Jee B (2010) Sketch worksheets: a sketch-based educational software system. In: Proceedings of the 22nd annual conference on innovative applications of artificial intelligence. Atlanta, GeorgiaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Seth Polsley
    • 1
  • Jaideep Ray
    • 1
  • Trevor Nelligan
    • 1
  • Michael Helms
    • 2
  • Julie Linsey
    • 2
  • Tracy Hammond
    • 1
  1. 1.Sketch Recognition Lab, Computer Science & Engineering DepartmentTexas A&M UniversityCollege StationUSA
  2. 2.iDreem Lab at Georgia Institute of TechnologyAtlantaUSA

Personalised recommendations