Abstract
We present here a novel instructional resource, called DeepCode, to support deep code comprehension and learning in intro-to-programming courses (CS1 and CS2). DeepCode is a set of instructional code examples which we call a codeset and which was annotated by our team with comments (e.g., explaining the logical steps of the underlying problem being solved) and related instructional questions that can play the role of hints meant to help learners think about and articulate explanations of the code. While DeepCode was designed primarily to serve our larger efforts of developing an intelligent tutoring system (ITS) that fosters the monitoring, assessment, and development of code comprehension skills for students learning to program, the codeset can be used for other purposes such as assessment, problem-solving, and in various other learning activities such as studying worked-out code examples with explanations and code visualizations. We present here the underlying principles, theories, and frameworks behind our design process, the annotation guidelines, and summarize the resulting codeset of 98 annotated Java code examples which include 7,157 lines of code (including comments), 260 logical steps, 260 logical step details, 408 statement level comments, and 590 scaffolding questions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aleven, V., Koedinger, K.R.: An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cogn. Sci. 26(2), 147–179 (2002)
Alhassan, R.: The effect of employing self-explanation strategy with worked examples on acquiring computer programming skills. J. Educ. Pract. 8(6), 186–196 (2017)
Bielaczyc, K., Pirolli, P.L., Brown, A.L.: Training in selfexplanation and self-regulation strategies: investigating the effects of knowledge acquisition activities on problem solving. Cogn. Instr. 13(2), 221–252 (1995)
Bloom, B.S.: All Our Children Learning - A Primer for Parents, Teachers, and Other Educators. McGraw-Hill, New York (1981). ISBN 9780070061187
Boehm, B., Basili, V.R.: Software defect reduction top 10 list. Computer 34(1), 135–137 (2001)
Brooks, R.: Towards a theory of the comprehension of computer programs. Int. J. Man Mach. Stud. 18, 543–554 (1983)
Brusilovsky, P., Yudelson, M.: From WebEx to NavEx: interactive access to annotated program examples. Proc. IEEE 96(6), 990–999 (2008)
Buse, R.P.L., Weimer, W.R.: A metric for software readability. In: International Symposium on Software Testing and Analysis, pp. 121–130 (2008)
Chen, B., Azad, S., Haldar, R., West, M., Zilles, C.: A validated scoring rubric for explain-in-plain-English questions. In: The 51st ACM Technical Symposium on Computer Science Education (SIGCSE 2020), Portland, OR, USA, 11–14 March 2020. ACM, New York (2020). 7 pages. https://doi.org/10.1145/3328778.3366879
Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R.: Self-explanations: how students study and use examples in learning to solve problems. Cogn. Sci. 13, 145–182 (1989)
Chi, M.T.H., DeLeeuw, N., Chiu, M.-H., LaVancher, C.: Eliciting self-explanations improves understanding. Cogn. Sci. 18(3), 439–477 (1994)
Chi, M.T.H.: Self-explaining: the dual processes of generating inference and repairing mental models. In: Glaser, R. (ed.) Advances in Instructional Psychology: Educational Design and Cognitive Science, vol. 5, pp. 161–238. Lawrence Erlbaum Associates Publishers (2000)
Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49, 219–243 (2014)
Chi, M.T.H., et al.: Translating the ICAP theory of cognitive engagement into practice. Cogn. Sci. 42, 1777–1832 (2018)
Conati, C., VanLehn, K.: Further results from the evaluation of an intelligent computer tutor to coach self-explanation. In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) ITS 2000. LNCS, vol. 1839, pp. 304–313. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45108-0_34
Détienne, F.: Software Design - Cognitive Aspects. Practitioner Series. Springer, London (2002). https://doi.org/10.1007/978-1-4471-0111-6
Edwards, S.H., Murali, K.P.: CodeWorkout: short programming exercises with built-in data collection. In: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2017), pp. 188–193. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3059009.3059055
Graesser, A.C., Singer, M., Trabasso, T.: Constructing inferences during narrative text comprehension. Psychol. Rev. 101, 371–395 (1994)
Good, J.: Programming paradigms, information types and graphical representations: empirical investigations of novice program comprehension. Ph.D. thesis, University of Edinburgh (1999)
Guo, P.J.: Online Python tutor: embeddable web-based program visualization for cs education. In: Proceedings of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE 2013), Denver, Colorado, USA, pp. 579–584. Association for Computing Machinery (2013)
Kintsch, W.: Learning from text. Cogn. Instr. 3(2), 87–108 (1986)
Kumar, A.N.: Epplets: a tool for solving parsons puzzles. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE 2018), pp. 527–532. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3159450.3159576
Lane, H.C., VanLehn, K.: A dialogue-based tutoring system for beginning programming. In: Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 449–454. AAAI Press (2004)
Lopez, M., Whalley, J., Robbins, P., Lister, R.: Relationships between reading, tracing and writing skills in introductory programming. In: Proceedings of the Fourth International Workshop on Computing Education Research, pp. 101–112. ACM (2008)
O’Brien, M.P.: Software comprehension – a review & research direction. Department of Computer Science & Information Systems University of Limerick, Ireland. Technical report (2003)
Pennington, N.: 1987. Comprehension strategies in programming. In: Soloway, E., Iyengar, S. (eds.) Empirical Studies of Programmers: Second Workshop, pp. 100–113. Ablex, Norwood (1987)
Recker, M.M., Pirolli, P.: A model of self-explanation strategies of instructional text and examples in the acquisition of programming skills (1990)
Rezel, E.S.: The effect of training subjects in self-explanation strategies on problem solving success in computer programming (2003)
Robins, A., Rountree, J., Rountree, N.: Learning and teaching programming: a review and discussion. Comput. Sci. Educ. 13(2), 137–172 (2003)
Roy, M., Chi, M.T.H.: The self-explanation principle in multimedia learning. In: The Cambridge Handbook of Multimedia Learning, pp. 271–286 (2005)
Sanders, K., et al.: The Canterbury QuestionBank: building a repository of multiple-choice CS1 and CS2 questions. In: Proceedings of the ITiCSE Working Group Reports Conference on Innovation and Technology in Computer Science Education-Working Group Reports (ITiCSE -WGR 2013), pp. 33–52. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2543882.2543885
Rugaber, S.: The use of domain knowledge in program understanding. Ann. Softw. Eng. 9(1–4), 143–192 (2000)
Rus, V., Sidney, D., Xiangen, H., Graesser, A.C.: Recent advances in conversational intelligent tutoring systems. AI Mag. 34(3), 42–54 (2013)
Schulte, C., Clear, T., Taherkhani, A., Busjahn, T., Paterson, J.: An introduction to program comprehension for computer science educators. In: Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, pp. 65–86 (2010). https://doi.org/10.1145/1971681.1971687
Shaft, T.M.: The role of application domain knowledge in computer program comprehension and enhancement. Unpublished Ph.D. thesis, Pennsylvania State University (1992)
Sharrock, R., Hamonic, E., Hiron, M., Carlier, S.: CODECAST: an innovative technology to facilitate teaching and learning computer programming in a C language online course. In: Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S 2017), pp. 147–148. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3051457.3053970
Shneiderman, B., Mayer, R.: Syntactic/semantic interactions in programmer behaviour. Int. J. Comput. Inf. Sci. 8(3), 219–238 (1979)
Sirkiä, T.: Creating and tailoring program animations for computing education. J. Softw. Evol. Process 30(2) (2018)
Spacco, J., Hovemeyer, D., Pugh, W., Emad, F., Hollingsworth, J.K., Padua-Perez, N.: Experiences with marmoset: designing and using an advanced submission and testing system for programming courses. In: ITiCSE 2006, pp. 13–17 (2006)
Soloway, E., Spohrer, J.C.: Studying the Novice Programmer. Lawrence Erlbaum Associates, Hillsdale (1989)
Sweller, J., VanMerrienboer, J.J.G., Paas, F.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251 (1998). https://doi.org/10.1023/a:1022193728205
Whalley, J., et al.: An Australasian study of reading and comprehension skills in novice programmers, using the bloom and SOLO taxonomies. In: Eighth Australasian Computing Education Conference (ACE 2006), January 2006
Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-learning. Morgan Kaufman Publishers, Burlington (2009)
Zwaan, R.A., Radvansky, G.A.: Situation models in language comprehension and memory. Psychol. Bull. 123(2), 162 (1998)
VanLehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Educ. 16(3), 227–265 (2006)
Acknowledgments
This work was supported by the National Science Foundation under award 1822816. All findings and opinions expressed or implied are solely the authors’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rus, V., Brusilovsky, P., Tamang, L.J., Akhuseyinoglu, K., Fleming, S. (2022). DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning. 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_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-09680-8_4
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)