Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-mwx4w Total loading time: 0 Render date: 2024-06-22T07:48:26.944Z Has data issue: false hasContentIssue false

29 - Students As Teachers and Communicators

from Teacher and Student Knowledge

Published online by Cambridge University Press:  15 February 2019

By
Beth Simon ,
Christopher Hundhausen ,
Charlie McDowell ,
Linda Werner ,
Helen Hu  and
Clif Kussmaul
Edited by
Sally A. Fincher  and
Anthony V. Robins
Sally A. Fincher
Affiliation:
University of Kent, Canterbury
Anthony V. Robins
Affiliation:
University of Otago, New Zealand
Get access

Summary

Students learn by constructing their own knowledge -- and can learn very effectively from each other. We present four practices that leverage the power of socially constructed learning among students: Pair Programming, Peer Instruction, Studio-based learning, and Process-Oriented Guided Inquiry Learning (POGIL). Pair Programming is a process to guide students in learning more from the program writing process. Peer Instruction is a classroom practice to develop students’ analysis skills and a way of implementing a flipped classroom. Studio-based learning is a socially-oriented instructional model that is based on architecture and fine arts educational practices. POGIL focuses on the simultaneous development of both content knowledge and process skills.
Type
Chapter
Information
The Cambridge Handbook of Computing Education Research , pp. 827 - 858
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: Macmillan.Google Scholar
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72(1), 187–206.CrossRefGoogle ScholarPubMed
Barker, L., & Cohoon, J. M. (2017). Pair Programming (Case Study 1). Retrieved from: www.ncwit.org/resources/how-do-you-retain-women-through-collaborative-learning/pair-programming-case-study-1Google Scholar
Basili, V. R., Green, S., Laitenberger, O., Lanubile, F., Shull, F., Sørumgård, S., & Zelkowitz, M. V. (1996). The empirical investigation of perspective-based reading. Empirical Software Engineering, 1(2), 133–164.Google Scholar
Boyer, E. L., & Mitgang, L. D. (1996). Building Community: A New Future for Architecture Education and Practice: A Special Report. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching.Google Scholar
Carter, A. S., & Hundhausen, C. D. (2015). The design of a programming environment to support greater social awareness and participation in early computing courses. Journal of Computing Sciences in Colleges, 31(1), 143–153.Google Scholar
Chaiklin, S. (2003). The zone of proximal development in Vygotsky’s analysis of learning and instruction. In Kozulin, A., Gindis, B., Ageyev, V., & Miller, S. (Eds.), Vygotsky’s Educational Theory in Cultural Context, 1 (pp. 39–64). Cambridge, UK: Cambridge University Press.Google Scholar
Cockburn, A., & Williams, L. (2000). The costs and benefits of pair programming. Extreme Programming Examined, 8, 223–247.Google Scholar
Code.org (2014). Pair Programming. Retrieved from www.youtube.com/watch?v=vgkahOzFH2QGoogle Scholar
Crouch, C. H., & Mazur, E. (2001). Peer instruction: Ten years of experience and results. American Journal of Physics, 69(9), 970–977.Google Scholar
Crowther, P. (2013). Understanding the signature pedagogy of the design studio and the opportunities for its technological enhancement. Journal of Learning Design, 6(3), 18–28.Google Scholar
Cutts, Q., Cutts, E., Draper, S., O’Donnell, P., & Saffrey, P. (2010). Manipulating mindset to positively influence introductory programming performance. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 431–435). New York: ACM.CrossRefGoogle Scholar
Docherty, M., Sutton, P., Brereton, M., & Kaplan, S. (2001). An innovative design and studio-based CS degree. ACM SIGCSE Bulletin, 33(1), 233–237.Google Scholar
Dougiamas, M., & Taylor, P. (2003). Moodle: Using learning communities to create an open source course management system. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (EDMEDIA) (pp. 171–178). Waynesville, NC: Association for the Advancement of Computing in Education.Google Scholar
Dweck, C. S. (2006). Mindset: The New Psychology of Success. New York: Random House.Google Scholar
Fagan, M. E. (1986). Advances in Software Inspections. IEEE Transactions on Software Engineering, 12(7): 744–751.Google Scholar
Faro, S., & Swan, K. (2006). An investigation into the efficacy of the studio model at the high school level. Journal of Educational Computing Research, 35(1), 45–59.Google Scholar
Flor, N. V., & Hutchins, E. L. (1991). A case study of team programming during perfective software maintenance. In Koenemann-Belliveau, J., Moher, T. G., & Robertson, S. P. (Eds.), Empirical Studies of Programmers: Fourth Workshop (pp. 36–59). Norwood, NJ: Ablex Publishing.Google Scholar
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.Google Scholar
Gilb, T., & Graham, D. (1993). Software Inspection. Menlo Park, CA: Addison-Wesley.Google Scholar
Hanks, B., Fitzgerald, S., McCauley, R., Murphy, L., & Zander, C. (2011). Pair programming in education: A literature review. Computer Science Education, 21(2), 135–173.Google Scholar
Hanson, D. M. (2006). Instructor’s Guide to Process-Oriented Guided-Inquiry Learning. Lisle, IL: Pacific Crest.Google Scholar
Hendrix, D., Myneni, L., Narayanan, H., & Ross, M. (2010). Implementing studio-based learning in CS2. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 505–509). New York: ACM.Google Scholar
Hestenes, D., & Halloun, I. (1995). Interpreting the force concept inventory. The Physics Teacher, 33(8), 502–506.Google Scholar
Horn, E. M., Collier, W. G., Oxford, J. A., BondJr., C. F., & Dansereau, D. F. (1998). Individual differences in dyadic cooperative learning. Journal of Educational Psychology, 90(1), 153–161.Google Scholar
Hu, H. H., & Campbell, P. B. (2017). A framework for Levels of student participation and stages of relevant curriculum. Computing in Science & Engineering, 19(3), 20–29.Google Scholar
Hu, H. H., Kussmaul, C., Knaeble, B., Mayfield, C., & Yadav, A. (2016). Results from a survey of faculty adoption of process oriented guided inquiry learning (POGIL) in computer science. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 186–191). New York: ACM.Google Scholar
Hundhausen, C. D., Agrawal, A., & Agarwal, P. (2013). Talking about code: Integrating pedagogical code reviews into early computing courses. ACM Transactions on Computing Education (TOCE), 13(3), 14.Google Scholar
Hundhausen, C. D., & Brown, J. L. (2008). Designing, visualizing, and discussing algorithms within a CS 1 studio experience: An empirical study. Computers & Education, 50(1), 301–326.Google Scholar
Hundhausen, C., Agrawal, A., Fairbrother, D., & Trevisan, M. (2010). Does studio-based instruction work in CS 1?: An empirical comparison with a traditional approach. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 500–504). New York: ACM.Google Scholar
Hundhausen, C. D., Fairbrother, D., & Petre, M. (2012). An empirical study of the “prototype walkthrough”: A studio-based activity for HCI education. ACM Transactions on Computer-Human Interaction (TOCHI), 19(4), 26.Google Scholar
Johnson, D. W., Johnson, R. T., & Smith, K. A. (1991). Active Learning: Cooperation in the College Classroom. Edina, MN: Interaction Book Company.Google Scholar
Karplus, R., & Thier, H. D. (1967). A New Look at Elementary School Science: Science Curriculum Improvement Study. Chicago, IL, and New York: Rand McNally.Google Scholar
Kehoe, C. M. (2001). Bringing design dialog to HCI education. In CHI’01 Extended Abstracts on Human Factors in Computing Systems (pp. 473–474). New York: ACM.Google Scholar
Lackney, J. (1999). A history of the studio-based learning model. Retrieved from: http://edi.msstate.edu/work/pdf/history_studio_based_learning.pdfGoogle Scholar
Lave, J. (1993). The practice of learning. In S. Chaiklin & J. Lave (Eds.), Understanding Practice: Perspectives on Activity and Context (pp. 3–32). Cambridge, UK: Cambridge University Press.Google Scholar
Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.Google Scholar
Lave, J., & Wenger, E. (1999). Legitimate peripheral participation. In P. Murphy, (Ed.), Learners, Learning and Assessment (pp. 83–89), London: Paul Chapman Publishing Ltd.Google Scholar
Lee, C. B., Garcia, S., & Porter, L. (2013). Can peer instruction be effective in upper-division computer science courses? ACM Transactions on Computing Education (TOCE), 13(3), 12.Google Scholar
Lewis, C. M. (2011). Is pair programming more effective than other forms of collaboration for young students? Computer Science Education, 21(2), 105–134.CrossRefGoogle Scholar
Lewis, C. M., & Shah, N. (2015). How equity and inequity can emerge in pair programming. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (pp. 41–50). New York: ACM.Google Scholar
Liao, S. N., Zingaro, D., Laurenzano, M. A., Griswold, W. G., & Porter, L. (2016). Lightweight, early identification of at-risk CS1 students. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 123–131). New York: ACM.Google Scholar
Moog, R. S., & Spencer, J. N. (2008). POGIL: An overview. In Moog, R. S. & Spencer, J. N. (Eds.), Process-Oriented Guided Inquiry Learning: ACS Symposium Series 994 (pp. 1–13). Washington, DC: American Chemical Society.Google Scholar
McDowell, C., Werner, L., Bullock, H. E., & Fernald, J. (2006). Pair programming improves student retention, confidence, and program quality. Communications of the ACM, 49(8), 90–95.Google Scholar
Narayanan, N. H., Hendrix, D., Ross, M., Hundhausen, C., & Crosby, M. (2018). Broadening Studio-Based Learning in Computing Education: Final Report to NSF 2015. Technical Report CSSE18-01. Auburn, AL: Department of Computer Science & Software Engineering, Auburn University.Google Scholar
NCWIT (2009). Pair Programming-in-a-Box: The Power of Collaborative Learning. Retrieved from www.ncwit.org/pairprogrammingGoogle Scholar
O’Donnell, A. M., & Dansereau, D. F. (1992). Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance. In Hertz-Lazarowitz, R. & Norman, M. (Eds.), Interaction in Cooperative Groups: The Theoretical Anatomy of Group Learning (pp. 120–141). Cambridge, UK: Cambridge University Press.Google Scholar
Olivares, D. M., & Hundhausen, C. D. (2016). OSBLE+: A next-generation learning management and analytics environment for computing education. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (p. 5-5). New York: ACM.Google Scholar
Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.Google Scholar
Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Eds.), Distributed Cognitions: Psychological and Educational Considerations (pp. 47–87). Cambridge: Cambridge University Press.Google Scholar
Porter, L., Bailey-Lee, C., & Simon, B. (2013). Halving fail rates using peer instruction: A study of four computer science courses. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (pp. 177–182). New York: ACM.Google Scholar
Porter, L., Bailey-Lee, C., Simon, B., Cutts, Q., & Zingaro, D. (2011). Experience report: A multi-classroom report on the value of peer instruction. In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education (pp. 138–142). New York: ACM.Google Scholar
Porter, L., Bouvier, D., Cutts, Q., Grissom, S., Lee, C., McCartney, R., Zingaro, D., & Simon, B. (2016). A multi-institutional study of peer instruction in introductory computing. ACM Inroads, 7(2), 76–81.Google Scholar
Porter, L., Garcia, S., Glick, J., Matusiewicz, A., & Taylor, C. (2013). Peer instruction in computer science at small liberal arts colleges. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (pp. 129–134). New York: ACM.Google Scholar
Porter, L., Zingaro, D., & Lister, R. (2014). Predicting student success using fine grain clicker data. In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 51–58). New York: ACM.Google Scholar
Reimer, Y. J., & Douglas, S. A. (2003). Teaching HCI design with the studio approach. Computer Science Education, 13(3), 191–205.Google Scholar
Rodríguez, F. J., Price, K. M., & Boyer, K.E. (2017). Exploring the pair programming process: characteristics of effective collaboration. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 507–512). New York: ACM.Google Scholar
Ruder, S. M., & Hunnicutt, S. S. (2008). POGIL in chemistry courses at a large urban university: A case study. In ACS Symposium Series (Vol. 994, pp. 133–147). Oxford: Oxford University Press.Google Scholar
Salleh, N., Mendes, E., & Grundy, J. (2011). Empirical studies of pair programming for CS/SE teaching in higher education: A systematic literature review. IEEE Transactions on Software Engineering, 37(4), 509–525.Google Scholar
Sauer, C., Jeffery, D. R., Land, L., & Yetton, P. (2000). The effectiveness of software development technical reviews: A behaviorally motivated program of research. IEEE Transactions on Software Engineering, 26(1), 1–14.Google Scholar
Schlimmer, J. C., Fletcher, J. B., & Hermens, L. A. (1994). Team-oriented software practicum. IEEE Transactions on Education, 37(2), 212–220.Google Scholar
Schon, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. New York: Basic Books.Google Scholar
Simon, B., & Hanks, B. (2008). First-year students’ impressions of pair programming in CS1. Journal on Educational Resources in Computing (JERIC), 7(4), 5.Google Scholar
Simon, B., Kohanfars, M., Lee, J., Tamayo, K., & Cutts, Q. (2010). Experience report: Peer instruction in introductory computing. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 341–345). New York: ACM.Google Scholar
Simon, B., Parris, J., & Spacco, J. (2013). How we teach impacts student learning: Peer instruction vs. lecture in CS0. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (pp. 41–46). New York: ACM.Google Scholar
Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21(1), 43–69.Google Scholar
Slavin, R. E. (1990). Cooperative Learning: Theory, Research, and Practice. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Smith, M. K., Wood, W. B., Adams, W. K., Wieman, C., Knight, J. K., Guild, N., & Su, T. T. (2009). Why peer discussion improves student performance on in-class concept questions. Science, 323(5910), 122–124.Google Scholar
Straumanis, A., & Simons, E. (2008). A multi-institutional assessment of the use of POGIL in organic chemistry. In Moog, R. S. & Spencer, J. N. (Eds.), Process-Oriented Guided Inquiry Learning: ACS Symposium Series 994 (pp. 226–239). Washington, DC: American Chemical Society.Google Scholar
The POGIL Project (2018). POGIL Implementation Guide. Retrieved from www.pogil.org/educators/additional-resourcesGoogle Scholar
Totten, S. S., Digby, T. A., & Russ, P. (1991). Cooperative Learning: A Guide to Research. New York: Garland.Google Scholar
Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.Google Scholar
Vygotsky, L. S. (1986). Thought and Language – Revised Edition. Cambridge, MA: MIT Press.Google Scholar
Werner, L., Denner, J., Campe, S., Ortiz, E., DeLay, D., Hartl, A. C., & Laursen, B. (2013). Pair programming for middle school students: Does friendship influence academic outcomes? In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (pp. 421–426). New York: ACM.Google Scholar
Wiegers, K. E. (1995). Improving quality through software inspections. Software Development, 3(4), 1–15.Google Scholar
Williams, L. A., & Kessler, R. R. (2000a). The effects of “pair-pressure” and “pair-learning” on software engineering education. In Proceedings of the 13th Conference on Software Engineering Education & Training (pp. 59–65). New York: IEEE.Google Scholar
Williams, L. A., & Kessler, R. R. (2000b). All I Really Need to Know About Pair Programming I Learned in Kindergarten. Communications of the ACM, 43(5), 108–114.Google Scholar
Zingaro, D. (2014). Peer instruction contributes to self-efficacy in CS1. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 373–378). New York: ACM.Google Scholar
Zingaro, D., & Porter, L. (2015). Tracking student learning from class to exam using isomorphic questions. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 356–361). New York: ACM.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×