Abstract
We describe a programming tutor framework that consists of two configurable components, a guided-planning component and an assisted-coding component that offers task relevant automatically-generated hints on demand to students. We evaluate the effectiveness of the new integrated planning and coding environment by comparing it to three other tutor conditions: planning-only, coding-only, and planning-only interleaved with planning-coding. We conclude that the integrated planning and coding tutor environment is more effective than tutored planning-only activities and that students make more efficient use of tutor feedback in the integrated environment than in the coding only environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: Example-Tracing Tutors: A New Paradigm for Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education 19, 105–154 (2009)
Anderson, J.R., Conrad, F.G., Corbett, A.T.: Skill Acquisition and the LISP Tutor. Cognitive Science 13(4), 467–505 (1989)
Barnes, T., Stamper, J.: Automatic hint generation for logic proof tutoring using historical data. Journal Educational Technology & Society 13(1), 3–12 (2010); Special issue on Intelligent Tutoring Systems
Bloom, B.S.: The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13, 4–16 (1984)
Bonar, J., Cunningham, R.: BRIDGE: An intelligent tutor for thinking about programming. In: Self, J. (ed.) Artificial Intelligence and Human Learning, ch. 24, pp. 391–409. Chapman and Hall (1988)
Coheen, J., Chen, L.Y.: Migrating out of computer science. Computing Research News 15(2) (2003)
Corbett, A.T., Anderson, J.R.: Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In: ACM Conference on Human Factors in Computing Systems, pp. 245–252 (2001)
Huang, J., Piech, C., Nguyen, A., Guibas, L.: Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC Stanford University. In: 1st Workshop on Massive Open Online Courses at the 16th Annual Conference on Artificial Intelligence in Education (2013)
Jin, W., Barnes, T., Stamper, J., Eagle, M.J., Johnson, M.W., Lehmann, L.: Program Representation for Automatic Hint Generation for a Data-Driven Novice Programming Tutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 304–309. Springer, Heidelberg (2012)
Jin, W., Corbett, A.: Effectiveness of Cognitive Apprenticeship Learning (CAL) and Cognitive Tutors (CT) for Problem Solving Using Fundamental Programming Concepts. In: 42nd ACM SIGCSE Technical Symposium on Computer Science Education, pp. 305–310 (2011)
Lane, H.C., VanLehn, K.: Teaching program planning skills to novices with natural language tutoring. Computer Science Education 15(3), 183–201 (2005)
McCracken, M., et al.: A multi-national multi-institutional study of assessment of programming skills of first-year CS students. SIGCSE Bulletin 34(11) (March 2002)
Mitrovic, A., Ohlsson, S.: Evaluation of a Constraint-Based Tutor for a Database Language. International Journal of AI in Education 10(3-4), 238–256 (1999)
Pirolli, P.: A cognitive model and computer tutor for programming recursion. Human Computer Interaction 2, 319–355 (1986)
Rivers, K., Koedinger, K.R.: Automatic Generation of Programming Feedback: A Data-Driven Approach. In: The Workshops at the 16th International Conference on Artificial Intelligence in Education, pp. 50–59 (2013)
Stamper, J., Barnes, T., Croy, M.: Enhancing the automatic generation of hints with expert seeding. Intl Journal of AI in Education 21(1), 153–167 (2011)
VanLehn, K.: The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist 46(4), 197–221 (2011)
Weber, G., Brusilovsky, P.: ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education 12(4), 351–384 (2001); Special Issue on Adaptive and Intelligent Web-based Educational Systems
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Jin, W., Corbett, A., Lloyd, W., Baumstark, L., Rolka, C. (2014). Evaluation of Guided-Planning and Assisted-Coding with Task Relevant Dynamic Hinting. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-07221-0_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
Online ISBN: 978-3-319-07221-0
eBook Packages: Computer ScienceComputer Science (R0)