research-article

Increasing Adoption of Smart Learning Content for Computer Science Education

Authors:

Peter Brusilovsky,

Stephen Edwards,

Luciana Benotti,

Petri Ihantola,

Sergey Sosnovsky,

Arto Vihavainen,

Michael WollowskiAuthors Info & Claims

ITiCSE-WGR '14: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference

Pages 31 - 57

https://doi.org/10.1145/2713609.2713611

Published: 23 June 2014 Publication History

Abstract

Computer science educators are increasingly using interactive learning content to enrich and enhance the pedagogy of their courses. A plethora of such learning content, specifically designed for computer science education, such as visualization, simulation, and web-based environments for learning programming, are now available for various courses. We call such content smart learning content. However, such learning content is seldom used outside its host site despite the benefits it could offer to learners everywhere. In this paper, we investigate the factors that impede dissemination of such content among the wider computer science education community. To accomplish this we surveyed educators, existing tools and recent research literature to identify the current state of the art and analyzed the characteristics of a large number of smart learning content examples along canonical dimensions. In our analysis we focused on examining the technical issues that must be resolved to support finding, integrating and customizing smart learning content in computer science courses. Finally, we propose a new architecture for hosting, integrating and disseminating smart learning content and discuss how it could be implemented based on existing protocols and standards.

References

[1]

K. M. Ala-Mutka. A Survey of Automated Assessment Approaches for Programming Assignments. Computer Science Education, 15(2):83--102, 2005.

[2]

E. Allen, R. Cartwright, and B. Stoler. Drjava: A lightweight pedagogic environment for java. In Proceedings of the 33rd SIGCSE Technical Symposium on Computer Science Education, SIGCSE '02, pages 137--141, New York, NY, USA, 2002. ACM.

Digital Library

[3]

F. J. Almeida Martínez, J. U. Fuentes, and Ángel Velázquez Iturbide. VAST: Visualization of Abstract Syntax Trees Within Language Processors Courses. In Proceedings of the 4th ACM Symposium on Software Visualization, SoftVis '08, pages 209--210, New York, NY, USA, 2008. ACM.

Digital Library

[4]

M. Ben-Ari, R. Bednarik, R. Ben-Bassat Levy, G. Ebel, A. Moreno, N. Myller, and E. Sutinen. A decade of research and development on program animation: The Jeliot experience. Journal of Visual Languages & Computing, 22(5):375--384, 2011.

[5]

B. S. Bloom. Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company, 1956.

[6]

C. Bonwell and J. Eison. Active Learning: Creating Excitement in the Classroom AEHE-ERIC Higher Education Report No. 1. Jossey-Bass, Washington, D.C., 1991.

[7]

C. Brown, R. Pastel, B. Siever, and J. Earnest. JUG: A JUnit Generation, Time Complexity Analysis and Reporting Tool to Streamline Grading. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 99--104, New York, NY, USA, 2012. ACM.

Digital Library

[8]

P. Brusilovsky. KnowledgeTree: A Distributed Architecture for Adaptive e-Learning. In Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, WWW Alt. '04, pages 104--113, New York, NY, USA, 2004. ACM.

Digital Library

[9]

P. Brusilovsky. Adaptive Hypermedia for Education and Training, pages 46--68. Cambridge University Press, 2012.

[10]

P. Brusilovsky, D. Baishya, R. Hosseini, J. Guerra, and M. Liang. KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on, pages 275--279. IEEE, July 2013.

Digital Library

[11]

P. Brusilovsky and T. D. Loboda. WADEIn II: A Case for Adaptive Explanatory Visualization. SIGCSE Bull., 38(3):48--52, June 2006.

Digital Library

[12]

P. Brusilovsky, E. Schwarz, and G. Weber. ELM-ART: An intelligent tutoring system on world wide web. In C. Frasson, G. Gauthier, and A. Lesgold, editors, Intelligent Tutoring Systems, volume 1086 of Lecture Notes in Computer Science, pages 261--269. Springer Berlin Heidelberg, 1996.

Digital Library

[13]

P. Brusilovsky, S. Sosnovsky, and O. Shcherbinina. User modeling in a distributed e-learning architecture. In User Modeling 2005, pages 387--391. Springer, 2005.

Digital Library

[14]

P. Brusilovsky, S. Sosnovsky, M. V. Yudelson, D. H. Lee, V. Zadorozhny, and X. Zhou. Learning SQL Programming with Interactive Tools: From Integration to Personalization. Trans. Comput. Educ., 9(4):1--15, Jan. 2010.

Digital Library

[15]

S. Bryfczynski, R. P. Pargas, M. M. Cooper, M. Klymkowsky, and B. C. Dean. Teaching Data Structures with beSocratic. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 105--110, New York, NY, USA, 2013. ACM.

Digital Library

[16]

L. M. S. Cámara, M. P. Velasco, and J. Ángel Velázquez Iturbide. Evaluation of a Collaborative Instructional Framework for Programming Learning. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 162--167, New York, NY, USA, 2012. ACM.

Digital Library

[17]

D. R. Cerezo, M. G. Albarrán, and J. L. S. Rodríguez. Interactive Educational Simulations for Promoting the Comprehension of Basic Compiler Construction Concepts. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 28--33, New York, NY, USA, 2013. ACM.

Digital Library

[18]

C. Cooper. Individual Differences. Oxford Illustrated Press, Oxford, UK, 1997.

[19]

J. H. Cross, T. D. Hendrix, L. A. Barowski, and Others. Combining Dynamic Program Viewing and Testing in Early Computing Courses. In Computer Software and Applications Conference (COMPSAC), 2011 IEEE 35th Annual, pages 184--192. IEEE, 2011.

Digital Library

[20]

J. H. Cross, T. D. Hendrix, J. Jain, and L. A. Barowski. Dynamic Object Viewers for Data Structures. In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education, SIGCSE '07, pages 4--8, New York, NY, USA, 2007. ACM.

Digital Library

[21]

P. Dillenbourg. Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. Elsevier Science, Inc., New York, NY, 1999.

[22]

S. H. Edwards. Improving student performance by evaluating how well students test their own programs. J. Educ. Resour. Comput., 3(3), Sept. 2003.

Digital Library

[23]

S. H. Edwards. Using software testing to move students from trial-and-error to reection-in-action. In Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education, SIGCSE '04, pages 26--30, New York, NY, USA, 2004. ACM.

Digital Library

[24]

S. H. Edwards and M. A. Pérez-Quiñones. Experiences using test-driven development with an automated grader. J. Comput. Sci. Coll., 22(3):44--50, Jan. 2007.

Digital Library

[25]

S. Esper, S. R. Foster, and W. G. Griswold. CodeSpells: Embodying the Metaphor of Wizardry for Programming. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 249--254, New York, NY, USA, 2013. ACM.

Digital Library

[26]

R. Farzan and P. Brusilovsky. Social navigation support in a course recommendation system. In Adaptive hypermedia and adaptive web-based systems, pages 91--100. Springer, 2006.

Digital Library

[27]

R. M. Felder. Learning and Teaching Styles in Engineering Education. Engineering Education, 78(7):674--681, 1988.

[28]

E. Fouh, V. Karavirta, D. A. Breakiron, S. Hamouda, S. Hall, T. Naps, and C. A. Shaffer. Design and architecture of an interactive eTextbook|The OpenDSA system. Science of Computer Programming, 88:22--40, Aug. 2014.

[29]

M. Goldweber, R. Davoli, and T. Jonjic. Supporting Operating Systems Projects Using the ÂţMPS2 Hardware Simulator. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 63--68, New York, NY, USA, 2012. ACM.

Digital Library

[30]

J. Hattie and H. Timperlay. The Power of Feedback. Review of Educational Research, 77:81--112, 2007.

[31]

J. Helminen, P. Ihantola, V. Karavirta, and L. Malmi. How do students solve parsons programming problems?: An analysis of interaction traces. In Proceedings of the Ninth Annual International Conference on International Computing Education Research, ICER '12, pages 119--126, New York, NY, USA, 2012. ACM.

Digital Library

[32]

C. Higgins, T. Hegazy, P. Symeonidis, and A. Tsintsifas. The coursemarker cba system: Improvements over ceilidh. Education and Information Technologies, 8(3):287--304, 2003.

Digital Library

[33]

M. Hilton and D. S. Janzen. On Teaching Arrays with Test-driven Learning in WebIDE. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 93--98, New York, NY, USA, 2012. ACM.

Digital Library

[34]

D. Hovemeyer, M. Hertz, P. Denny, J. Spacco, A. Papancea, J. Stamper, and K. Rivers. CloudCoder: Building a Community for Creating, Assigning, Evaluating and Sharing Programming Exercises (Abstract Only). In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, SIGCSE '13, page 742, New York, NY, USA, 2013. ACM.

Digital Library

[35]

I.-H. Hsiao, F. Bakalov, P. Brusilovsky, and B. König-Ries. Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2):112--131, June 2013.

Digital Library

[36]

I.-H. Hsiao, S. Sosnovsky, and P. Brusilovsky. Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. Journal of Computer Assisted Learning, 26(4):270--283, 2010.

[37]

J. Hyvönen and L. Malmi. TRAKLA - A System for Teaching Algorithms Using Email and a Graphical Editor. In Proceedings of HYPERMEDIA in Vaasa, pages 141--147, 1993.

[38]

P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppälä. Review of Recent Systems for Automatic Assessment of Programming Assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli Calling '10, pages 86--93, New York, NY, USA, 2010. ACM.

Digital Library

[39]

P. Ihantola, J. Helminen, and V. Karavirta. How to Study Programming on Mobile Touch Devices: Interactive Python Code Exercises. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling '13, pages 51--58, New York, NY, USA, 2013. ACM.

Digital Library

[40]

P. Ihantola and V. Karavirta. Two-Dimensional Parson's Puzzles: The Concept, Tools, and First Observations. Journal of Information Technology Education: Innovations in Practice, 10:1--14, 2011.

[41]

P. Ihantola, V. Karavirta, A. Korhonen, and J. Nikander. Taxonomy of effortless creation of algorithm visualizations. In Proceedings of the First International Workshop on Computing Education Research, ICER '05, pages 123--133, New York, NY, USA, 2005. ACM.

Digital Library

[42]

S. A. Jalil, B. Plimmer, I. Warren, and A. L. Reilly. Design Eye: An Interactive Learning Environment Based on the Solo Taxonomy. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 22--27, New York, NY, USA, 2013. ACM.

Digital Library

[43]

P. Jarusek and R. Pel_anek. A Web-based Problem Solving Tool for Introductory Computer Science. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, page 371, New York, NY, USA, 2012. ACM.

Digital Library

[44]

J. Jeuring, A. Gerdes, and B. Heeren. Ask-Elle: A Haskell Tutor. In A. Ravenscroft, S. Lindstaedt, C. Kloos, and D. Hernández-Leo, editors, 21st Century Learning for 21st Century Skills, volume 7563 of Lecture Notes in Computer Science, pages 453--458. Springer Berlin Heidelberg, 2012.

Digital Library

[45]

M. Joy, N. Gri_ths, and R. Boyatt. The boss online submission and assessment system. Journal on Educational Resources in Computing (JERIC), 5(3):2, 2005.

Digital Library

[46]

V. Karavirta, P. Ihantola, and T. Koskinen. Service-Oriented Approach to Improve Interoperability of E-Learning Systems. In Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on, ICALT '13, pages 341--345, Washington, DC, USA, July 2013. IEEE.

Digital Library

[47]

V. Karavirta, A. Korhonen, L. Malmi, and K. Stalnacke. MatrixPro - A Tool for Demonstrating Data Structures and Algorithms Ex Tempore. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, ICALT '04, pages 892--893, Washington, DC, USA, 2004. IEEE Computer Society.

Digital Library

[48]

V. Karavirta and C. A. Shaffer. JSAV: The JavaScript Algorithm Visualization Library. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 159--164, New York, NY, USA, 2013. ACM.

Digital Library

[49]

J. Kay, B. Kummerfield, and P. Lauder. Personis: A Server for User Models. In P. De Bra, P. Brusilovsky, and R. Conejo, editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 2347 of Lecture Notes in Computer Science, pages 203--212. Springer Berlin Heidelberg, 2002.

Digital Library

[50]

C. Kelleher and R. Pausch. Lowering the Barriers to Programming: A Taxonomy of Programming Environments and Languages for Novice Programmers. ACM Comput. Surv., 37(2):83--137, June 2005.

Digital Library

[51]

Kiosked. Smart content trend report 2013, 2013. {Online; accessed 10-July-2014}.

[52]

K. R. Koedinger, R. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper. A data repository for the EDM community: The PSLC DataShop. Handbook of educational data mining, 43, 2010.

[53]

M. Kölling, B. Quig, A. Patterson, and J. Rosenberg. The BlueJ System and its Pedagogy. Computer Science Education, 13(4):249--268, Dec. 2003.

[54]

A. Korhonen, J. Helminen, V. Karavirta, and O. Seppälä. Trakla2. In A. Pears and C. Schulte, editors, Proceedings of the 9th Koli Calling International Conference on Computing Education Research, pages 43--46. University of Joensuu, Nov. 2010.

[55]

A. Korhonen, T. Naps, C. Boisvert, P. Crescenzi, V. Karavirta, L. Mannila, B. Miller, B. Morrison, S. H. Rodger, R. Ross, and C. A. Shaffer. Requirements and Design Strategies for Open Source Interactive Computer Science eBooks. In Proceedings of the ITiCSE Working Group Reports Conference on Innovation and Technology in Computer Science Education-working Group Reports, ITiCSE -WGR '13, pages 53--72, New York, NY, USA, 2013. ACM.

Digital Library

[56]

A. Kumar. A Scalable Solution for Adaptive Problem Sequencing and Its Evaluation. In V. Wade, H. Ashman, and B. Smyth, editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 4018 of Lecture Notes in Computer Science, pages 161--171. Springer Berlin Heidelberg, 2006.

Digital Library

[57]

A. N. Kumar. Results from the Evaluation of the Effectiveness of an Online Tutor on Expression Evaluation. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education, SIGCSE '05, pages 216--220, New York, NY, USA, 2005. ACM.

Digital Library

[58]

A. N. Kumar. A Study of the Inuence of Code-tracing Problems on Code-writing Skills. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 183--188, New York, NY, USA, 2013. ACM.

Digital Library

[59]

J. Kurhila, M. Miettinen, P. Nokelainen, and H. Tirri. Educo - A Collaborative Learning Environment Based on Social Navigation. In P. De Bra, P. Brusilovsky, and R. Conejo, editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 2347 of Lecture Notes in Computer Science, pages 242--252. Springer Berlin Heidelberg, 2002.

Digital Library

[60]

A. D. Learning. Trainging & learning architecture (TLA): Learning record store, 2014. {Online; accessed 10-July-2014}.

[61]

T. R. Liyanagunawardena, A. A. Adams, and S. A. Williams. MOOCs: A systematic study of the published literature 2008-2012. The International Review of Research in Open and Distance Learning, 14(3):202--227, 2013.

[62]

T. MacWilliam and D. J. Malan. Streamlining Grading Toward Better Feedback. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 147--152, New York, NY, USA, 2013. ACM.

Digital Library

[63]

Y. Matsuzawa, K. Okada, and S. Sakai. Programming Process Visualizer: A Proposal of the Tool for Students to Observe Their Programming Process. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 46--51, New York, NY, USA, 2013. ACM.

Digital Library

[64]

T. L. Naps, J. R. Eagan, and L. L. Norton. JHAVÉ&Mdash;an Environment to Actively Engage Students in Web-based Algorithm Visualizations. SIGCSE Bull., 32(1):109--113, Mar. 2000.

Digital Library

[65]

T. L. Naps, J. R. Eagan, and L. L. Norton. Jhavéâ Tan environment to actively engage students in web-based algorithm visualizations. ACM SIGCSE Bulletin, 32(1):109--113, 2000.

Digital Library

[66]

M. C. Orsega, B. T. Vander Zanden, and C. H. Skinner. Two experiments using learning rate to evaluate an experimenter developed tool for splay trees. In Proceedings of the 42Nd ACM Technical Symposium on Computer Science Education, SIGCSE '11, pages 135--140, New York, NY, USA, 2011. ACM.

Digital Library

[67]

A. Pears, S. Seidman, L. Malmi, L. Mannila, E. Adams, J. Bennedsen, M. Devlin, and J. Paterson. A Survey of Literature on the Teaching of Introductory Programming. In Working Group Reports on ITiCSE on Innovation and Technology in Computer Science Education, ITiCSE-WGR '07, pages 204--223, New York, NY, USA, 2007. ACM.

Digital Library

[68]

M. C. Polson and J. J. Richardson. Foundations of intelligent tutoring systems. Psychology Press, 2013.

[69]

B. A. Price, R. M. Baecker, and I. S. Small. A Principled Taxonomy of Software Visualization. Journal of Visual Languages and Computing, 4(3):211--266, 1993.

[70]

M. Procopiuc, O. Procopiuc, and S. H. Rodger. Visualization and Interaction in the Computer Science Formal Languages Course with JFLAP. In Frontiers in Education Conference, 1996.

Digital Library

[71]

T. Rajala, M. J. Laakso, E. Kaila, and T. Salakoski. VILLE: A Language-independent Program Visualization Tool. In Proceedings of the Seventh Baltic Sea Conference on Computing Education Research - Volume 88, Koli Calling '07, pages 151--159, Darlinghurst, Australia, Australia, 2007. Australian Computer Society, Inc.

Digital Library

[72]

G. Rössling, M. Joy, A. Moreno, A. Radenski, L. Malmi, A. Kerren, T. Naps, R. J. Ross, M. Clancy, A. Korhonen, R. Oechsle, and _ Angel Velázquez Iturbide. Enhancing Learning Management Systems to Better Support Computer Science Education. SIGCSE Bull., 40(4):142--166, Nov. 2008.

Digital Library

[73]

G. Rössling, M. McNally, P. Crescenzi, A. Radenski, P. Ihantola, and M. G. S_anchez-Torrubia. Adapting Moodle to Better Support CS Education. In Proceedings of the 2010 ITiCSE Working Group Reports, ITiCSE-WGR '10, pages 15--27, New York, NY, USA, 2010. ACM.

Digital Library

[74]

G. Rössling, M. Schüer, and B. Freisleben. The ANIMAL Algorithm Animation Tool. SIGCSE Bull., 32(3):37--40, July 2000.

Digital Library

[75]

A. L. Santos. An Open-ended Environment for Teaching Java in Context. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 87--92, New York, NY, USA, 2012. ACM.

Digital Library

[76]

M. Scaife and Y. Rogers. External cognition : how do graphical representations work? Int . J . Human Computer Studies, 45:185--213, 1996.

Digital Library

[77]

Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative Filtering Recommender Systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, chapter 9, pages 291--324. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.

Digital Library

[78]

C. A. Sha_er, M. L. Cooper, A. J. Alon, M. Akbar, M. Stewart, S. Ponce, and S. H. Edwards. Algorithm Visualization: The State of the Field. Trans. Comput. Educ., 10(3), Aug. 2010.

Digital Library

[79]

T. Sirkiä. A JavaScript Library for Visualizing Program Execution. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling '13, pages 189--190, New York, NY, USA, 2013. ACM.

Digital Library

[80]

D. Skrien. CPU Sim 3.1: A Tool for Simulating Computer Architectures for Computer Organization Classes. J. Educ. Resour. Comput., 1(4):46--59, Dec. 2001.

Digital Library

[81]

J. Sorva, V. Karavirta, and L. Malmi. A Review of Generic Program Visualization Systems for Introductory Programming Education. Trans. Comput. Educ., 13(4), Nov. 2013.

Digital Library

[82]

J. Sorva and T. Sirkiä. UUhistle: A Software Tool for Visual Program Simulation. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli Calling '10, pages 49--54, New York, NY, USA, 2010. ACM.

Digital Library

[83]

S. Sosnovsky, P. Brusilovsky, M. Yudelson, A. Mitrovic, M. Mathews, and A. N. Kumar. Semantic Integration of Adaptive Educational Systems. In T. Kuik, S. Berkovsky, F. Carmagnola, D. Heckmann, and A. Krüger, editors, Advances in Ubiquitous User Modelling, volume 5830 of Lecture Notes in Computer Science, chapter Semantic Integration of Adaptive Educational Systems, pages 134--158. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.

Digital Library

[84]

S. Sosnovsky and T. Gavrilova. Development of Educational Ontology for C-programming. International Journal on Information Theories & Applications, 13(4):303--308, 2006.

[85]

J. Spacco, D. Fossati, J. Stamper, and K. Rivers. Towards Improving Programming Habits to Create Better Computer Science Course Outcomes. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 243--248, New York, NY, USA, 2013. ACM.

Digital Library

[86]

J. Sweller. Cognitive Load Theory, learning difficulty, and instructional design. Learning and Instruction, 4(4):295--312, 1994.

[87]

J. Urquiza-Fuentes and J. A. Velázquez-Iturbide. A Survey of Successful Evaluations of Program Visualization and Algorithm Animation Systems. Trans. Comput. Educ., 9(2), June 2009.

Digital Library

[88]

K. Veeramachaneni, Z. Pardos, U.-M. O'Reilly, F. Dernoncourt, and C. Taylor. MOOCdb: Developing Data Standards and Systems for MOOC Data Science. In 1st Workshop on Massive Open Online Courses at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, 2013.

[89]

J. A. Velázquez Iturbide. Refinement of an Experimental Approach Tocomputer-based, Active Learning of Greedy Algorithms. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE '12, pages 46--51, New York, NY, USA, 2012. ACM.

Digital Library

[90]

A. Vihavainen, T. Vikberg, M. Luukkainen, and M. Pártel. Sca_olding Students' Learning Using Test My Code. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE '13, pages 117--122, New York, NY, USA, 2013. ACM.

Digital Library

[91]

L. S. Vygotsky. The history of the development of higher mental functions, volume 4 of The collected works of L. S. Vygotsky. Plenum Press., New York, 1997.

[92]

M. Yudelson, P. Brusilovsky, and V. Zadorozhny. A User Modeling Server for Contemporary Adaptive Hypermedia: An Evaluation of the Push Approach to Evidence Propagation. In C. Conati, K. McCoy, and G. Paliouras, editors, User Modeling 2007, volume 4511 of Lecture Notes in Computer Science, chapter 6, pages 27--36. Springer Berlin / Heidelberg, Berlin, Heidelberg, 2007.

Digital Library

[93]

B. V. Zanden, D. Anderson, C. Taylor, W. Davis, and M. W. Berry. Codeassessor: An Interactive, Web-based Tool for Introductory Programming. J. Comput. Sci. Coll., 28(2):73--80, Dec. 2012.

Digital Library

Cited By

Hou IMan OMettille SGutierrez SAngelikas KMacNeil S(2024)More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons ProblemsProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636247(29-38)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636247
Tran KBacher JShi YSkripchuk JPrice T(2024)Overcoming Barriers in Scaling Computing Education Research Programming Tools: A Developer's PerspectiveProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671113(312-325)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671113
Štuikys VBurbaitė RŠtuikys VBurbaitė R(2024)Enforcing STEM-Driven CS Education Through PersonalisationEvolution of STEM-Driven Computer Science Education10.1007/978-3-031-48235-9_3(81-107)Online publication date: 1-Jan-2024
https://doi.org/10.1007/978-3-031-48235-9_3
Show More Cited By

Index Terms

Increasing Adoption of Smart Learning Content for Computer Science Education
1. Applied computing
  1. Education
2. Social and professional topics
  1. Professional topics
    1. Computing education

Recommendations

Stepwise Help and Scaffolding for Java Code Tracing Problems With an Interactive Trace Table
Koli Calling '21: Proceedings of the 21st Koli Calling International Conference on Computing Education Research

In this paper, we describe the integration of a step-by-step interactive trace table into an existing practice system for introductory Java programming. These autogenerated trace problems provide help and scaffolding for students who have trouble in ...
Motivational active learning for computer science education (abstract only)
SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science education

Motivational Active Learning (MAL) is an innovative pedagogical approach based on MIT's teaching format TEAL (Technology-Enabled Active Learning) combined with advanced motivational strategies based on gamification design aspects. The main idea of MAL ...
Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ITiCSE-WGR '14: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference

June 2014

83 pages

ISBN:9781450334068

DOI:10.1145/2713609

Editors:
Alison Clear
Eastern Institute of Technology, New Zealand
,
Raymond Lister
University of Technology, Sydney, Australia

Copyright © 2014 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]

Sponsors

Bilkent University: Bilkent University
SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

ITICSE '14

Sponsor:

Bilkent University
SIGCSE

ITICSE '14: Innovation and Technology in Computer Science Education Conference 2014

June 23 - 25, 2014

Uppsala, Sweden

Acceptance Rates

Overall Acceptance Rate 552 of 1,613 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

48
Total Citations
View Citations
849
Total Downloads

Downloads (Last 12 months)66
Downloads (Last 6 weeks)4

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hou IMan OMettille SGutierrez SAngelikas KMacNeil S(2024)More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons ProblemsProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636247(29-38)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636247
Tran KBacher JShi YSkripchuk JPrice T(2024)Overcoming Barriers in Scaling Computing Education Research Programming Tools: A Developer's PerspectiveProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671113(312-325)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671113
Štuikys VBurbaitė RŠtuikys VBurbaitė R(2024)Enforcing STEM-Driven CS Education Through PersonalisationEvolution of STEM-Driven Computer Science Education10.1007/978-3-031-48235-9_3(81-107)Online publication date: 1-Jan-2024
https://doi.org/10.1007/978-3-031-48235-9_3
Haaranen LAhrenberg LHellas A(2023)Decades of Striving for Pedagogical and Technological AlignmentProceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631809(1-8)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3631802.3631809
Barria-Pineda JAkhuseyinoglu KBrusilovsky P(2023)Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice SystemProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609054(1-9)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3603163.3609054
Birillo AArtser EGolubev YTigina MKeuning HVyahhi NBryksin TLaakso MMonga MSimon Sheard J(2023)Detecting Code Quality Issues in Pre-written Templates of Programming Tasks in Online CoursesProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588800(152-158)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588800
Hovey CBunde DButler ZTaylor CDoyle MStephenson BDorn BSoh LBattestilli L(2023)How Do I Get People to Use My Ideas?Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569779(841-847)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569779
Khan MEdwards JBodily PKarimi H(2023)Deciphering Student Coding Behavior: Interpretable Keystroke Features and Ensemble Strategies for Grade Prediction2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386085(5799-5808)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386085
Daniels MMalmi LPears ASimon (2023)What is Computing Education Research (CER)?Past, Present and Future of Computing Education Research10.1007/978-3-031-25336-2_2(9-31)Online publication date: 5-Jan-2023
https://doi.org/10.1007/978-3-031-25336-2_2
Rugube TChibaya CGovender D(2022)A Software Design Model for Integrating LMS and MOOCsJournal of Information Technology Research10.4018/JITR.29937515:1(1-14)Online publication date: 1-Jan-2022
https://doi.org/10.4018/JITR.299375
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents