Skip to main content
SpringerLink
Log in

Guided Inquiry Learning with Technology: Community Feedback and Software for Social Constructivism

  • Conference paper
  • First Online:
Computer Supported Education (CSEDU 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1624))

Included in the following conference series:

  • 438 Accesses

Abstract

To meet current and future demands, education needs to become more effective and more scalable. One evidence-based, social constructivist approach is Process Oriented Guided Inquiry Learning (POGIL). In POGIL, teams of learners work on specifically designed activities that guide them to practice key skills and develop their own understanding of key concepts. This paper expands a prior conference paper, and describes a series of investigations of how technology might enhance POGIL to be more effective and more scalable. The investigations include a survey and structured discussions among POGIL community leaders, a UI mockup and a working prototype, and experiences piloting the prototype in a large introductory course at a university. These investigations reveal that instructors are interested in using such tools. Tools can support richer learning experiences for students and provide better reporting to help instructors monitor progress and facilitate learning. The course pilot demonstrates that a prototype can support a large hybrid class. These investigations also identified promising areas for future work.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49(4), 219–243 (2014). https://doi.org/10.1080/00461520.2014.965823

    Article  Google Scholar 

  2. Mazur, E.: Peer instruction: getting students to think in class. In: AIP Conference Proceedings, vol. 399, no. 1, pp. 981–988 (1997). https://doi.org/10.1063/1.53199

  3. Porter, L., Bouvier, D., Cutts, Q., et al.: A multi-institutional study of peer instruction in introductory computing. In: Proceedings of the ACM Technical Symposium on CS Education, pp. 358–363 ACM, New York (2016). https://doi.org/10.1145/2839509.2844642

  4. Porter, L., Bailey Lee, C., Simon, B., Zingaro, D.: Peer instruction: do students really learn from peer discussion in computing? In: Proceedings of the International Workshop on Computing Education Research (ICER), pp. 45–52. ACM, Providence (2011). https://doi.org/10.1145/2016911.2016923

  5. Gafney, L., Varma-Nelson, P.: Peer-Led Team Learning: Evaluation, Dissemination, and Institutionalization of a College Level Initiative. Springer, Cham (2008). https://doi.org/10.1007/978-1-4020-6186-8

  6. Horwitz, S., Rodger, S.H., Biggers, M., et al.: Using peer-led team learning to increase participation and success of under-represented groups in introductory computer science. In: Proceedings of the ACM Technical Symposium on CS Education (SIGCSE), pp 163–167. ACM, Chattanooga (2009)

    Google Scholar 

  7. Hmelo-Silver, C.E. Problem-based learning: what and how do students learn? Educ. Psychol. Rev. 16, 3, 235–266 (2004). https://doi.org/10.1023/B:EDPR.0000034022.16470.f3

  8. Amador, J.A., Miles, L., Peters, C.A.: The Practice of Problem-Based Learning: A Guide to Implementing PBL in the College Classroom. Anker Publishing Company Inc., Bolton (2007)

    Google Scholar 

  9. Kussmaul, C., Pirmann, T. Guided inquiry learning with technology: investigations to support social constructivism. In: Proceedings of the International Conference on Computer Supported Education (CSEDU) (2021a). https://doi.org/10.5220/0010458104830490

  10. Kussmaul, C., Pirmann, T.: Monitoring student team progress and responses in guided inquiry learning with technology. In: Proceedings of the International Conference on Advanced Learning Technologies (ICALT) (2021b). https://doi.org/10.1109/ICALT52272.2021.00046

  11. Rath, G.J.: The development of computer-assisted instruction. IEEE Trans. Hum. Factors Electron. HFE-8(2), 60–63 (1967). https://doi.org/10.1109/THFE.1967.233312

  12. Buck, G., Hunka, S.: Development of the IBM 1500 computer-assisted instructional system. IEEE Ann. Hist. Comput. 17(1), 19–31 (1995). https://doi.org/10.1109/85.366508

    Article  Google Scholar 

  13. Johnson, D.A., Rubin, S.: Effectiveness of interactive computer-based instruction: a review of studies published between 1995 and 2007. J. Organ. Behav. Manag. 31(1), 55–94 (2011). https://doi.org/10.1080/01608061.2010.541821

    Article  Google Scholar 

  14. Sleeman, D., Brown, J.S. (eds.): Intelligent Tutoring Systems. Academic Press, Cambridge (1982)

    Google Scholar 

  15. Graesser, A.C., Conley, M.W., Olney, A.: Intelligent tutoring systems. In: APA Educational Psychology Handbook, vol. 3: Application to Learning and Teaching, pp. 451–473. American Psychological Association (2012). https://doi.org/10.1037/13275-018

  16. Paviotti, G., Rossi, P.G., Zarka, D. (eds.): Intelligent Tutoring Systems: An Overview. Pensa Multimedia (2012)

    Google Scholar 

  17. Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106(4), 901–918 (2014). https://doi.org/10.1037/a0037123

    Article  Google Scholar 

  18. Steenbergen-Hu, S., Cooper, H.: A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. J. Educ. Psychol. 106(2), 331–347 (2014). https://doi.org/10.1037/a0034752

  19. Kulik, J.A., Fletcher, J.D.: Effectiveness of intelligent tutoring systems: a meta-analytic review. Rev. Educ. Res. 86(1), 42–78 (2016). https://doi.org/10.3102/0034654315581420

    Article  Google Scholar 

  20. Baker, R.S.: Stupid tutoring systems, intelligent humans. Int. J. Artif. Intell. Educ. 26(2), 600–614 (2016). https://doi.org/10.1007/s40593-016-0105-0

    Article  Google Scholar 

  21. Goodyear, P., Jones, C., Thompson, K.: Computer-supported collaborative learning: instructional approaches, group processes and educational designs. In: Spector, J.M., Merrill, M.D., Elen, J., Bishop, M.J. (eds.) Handbook of Research on Educational Communications and Technology, pp. 439–451. Springer, Cham (2014). https://doi.org/10.1007/978-1-4614-3185-5_35

    Chapter  Google Scholar 

  22. Chen, J., Wang, M., Kirschner, P.A., Tsai, C.-C.: The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: a meta-analysis. Rev. Educ. Res. 88(6), 799–843 (2018). https://doi.org/10.3102/0034654318791584

    Article  Google Scholar 

  23. Jeong, H., Hmelo-Silver, C.E., Jo, K.: Ten years of computer-supported collaborative learning: a meta-analysis of CSCL in STEM education during 2005–2014. Educ. Res. Rev. 28, 100284 (2019). https://doi.org/10.1016/j.edurev.2019.100284

  24. Stahl, G., Koschmann, T., Suthers, D.: Computer-supported collaborative learning. In: Sawyer, R.K. (ed.) Cambridge Handbook of the Learning Sciences, 3rd edn. Cambridge University Press (2021)

    Google Scholar 

  25. Jeong, H., Hmelo-Silver, C.E.: Seven affordances of computer-supported collaborative learning: how to support collaborative learning? How can technologies help? Educ. Psychol. 51(2), 247–265 (2016). https://doi.org/10.1080/00461520.2016.1158654

  26. Siemens, G., Gašević, D.: Guest editorial: learning and knowledge analytics. Educ. Technol. Soc. 15(3), 1–163 (2012)

    Google Scholar 

  27. Schwendimann, B.A., Rodriguez-Triana, M.J., Vozniuk, A, et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2017). https://doi.org/10.1109/TLT.2016.2599522

  28. Gašević, D., Dawson, S., Siemens, G.: Let’s not forget: learning analytics are about learning. TechTrends 59(1), 64–71 (2014). https://doi.org/10.1007/s11528-014-0822-x

    Article  Google Scholar 

  29. Viberg, O., Hatakka, M., Balter, O., Mavroudi, A.: The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018). https://doi.org/10.1016/j.chb.2018.07.027

    Article  Google Scholar 

  30. Moog, R.S., Spencer, J.N. (eds.): Process-Oriented Guided Inquiry Learning (POGIL). In: ACS Symposium Series, vol. 994. American Chemical Society (2008)

    Google Scholar 

  31. Simonson, S.R. (ed.): POGIL: An Introduction to Process Oriented Guided Inquiry Learning for Those Who Wish to Empower Learners. Stylus Publishing, Sterling (2019)

    Google Scholar 

  32. Farrell, J.J., Moog, R.S., Spencer, J.N.: A guided-inquiry general chemistry course. J. Chem. Educ. 76(4), 570 (1999). https://doi.org/10.1021/ed076p570

    Article  Google Scholar 

  33. Straumanis, A., Simons, E.A.: 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), vol. 994, pp. 226–239. American Chemical Society (2008). https://doi.org/10.1021/bk-2008-0994.ch019

  34. Douglas, E.P., Chiu, C.-C.: Use of guided inquiry as an active learning technique in engineering. In: Proceedings of the Research in Engineering Education Symposium, Queensland, Australia (2009)

    Google Scholar 

  35. Lenz, L.: Active learning in a math for liberal arts classroom. Primus 25(3), 279–296 (2015). https://doi.org/10.1080/10511970.2014.971474

    Article  Google Scholar 

  36. Hu, H.H., Kussmaul, C., Knaeble, B., Mayfield, C., Yadav, A.: Results from a survey of faculty adoption of POGIL in computer science. In: Proceedings of the ACM Conference on Innovation and Technology in CS Education, Arequipa, Peru, pp. 186–191 (2016). https://doi.org/10.1145/2899415.2899471

  37. Lo, S.M., Mendez, J.I.L.: Learning—the evidence. In: Simonson, S.R. (ed.) POGIL: An Introduction to Process Oriented Guided Inquiry Learning for Those Who Wish to Empower Learners, pp. 85–110. Stylus Publishing (2019)

    Google Scholar 

  38. Kezar, A., Gehrke, S., Bernstein-Sierra, S.: Communities of transformation: creating changes to deeply entrenched issues. J. High. Educ. 89(6), 832–864 (2018). https://doi.org/10.1080/00221546.2018.1441108

    Article  Google Scholar 

  39. Kussmaul, C.: Patterns in classroom activities for process oriented guided inquiry learning (POGIL). In: Proceedings of the Conference on Pattern Languages of Programs, Monticello, IL, pp. 1–16 (2016). https://dl.acm.org/doi/abs/10.5555/3158161.3158181

  40. Flener-Lovitt, C., Bailey, K., Han, R.: Using structured teams to develop social presence in asynchronous chemistry courses. J. Chem. Educ. 97(9), 2519–2525 (2020). https://doi.org/10.1021/acs.jchemed.0c00765

    Article  Google Scholar 

  41. Reynders, G., Ruder, S.M.: Moving a large-lecture organic POGIL classroom to an online setting. J. Chem. Educ. 97(9), 3182–3187 (2020). https://doi.org/10.1021/acs.jchemed.0c00615

    Article  Google Scholar 

  42. Hu, H.H., Kussmaul, C.: Improving online collaborative learning with POGIL practices. In: Proceedings of the ACM Technical Symposium on CS Education (SIGCSE) (2021). https://doi.org/10.1145/3408877.3439600

  43. Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011). https://doi.org/10.1109/TVCG.2011.185

  44. Wilensky, U., Stroup, W.: Learning through participatory simulations: network-based design for systems learning in classrooms. In: Proceedings of the Conference on Computer Support for Collaborative Learning (CSCL), Stanford, CA, 80-es (1999). https://dl.acm.org/doi/10.5555/1150240.1150320

  45. Perkins, K., et al.: PhET: interactive simulations for teaching and learning physics. Phys. Teach. 44(1), 18–23 (2005). https://doi.org/10.1119/1.2150754

    Article  MathSciNet  Google Scholar 

  46. Resnick, M., et al.: Scratch: programming for all. Commun. ACM 52(11), 60–67 (2009). https://doi.org/10.1145/1592761.1592779

    Article  Google Scholar 

  47. Sawada, D., Piburn, M.D., Judson, E.: Measuring reform practices in science and mathematics classrooms: the reformed teaching observation protocol. Sch. Sci. Math. 102(6), 245–253 (2002). https://doi.org/10.1111/j.1949-8594.2002.tb17883.x

    Article  Google Scholar 

  48. Smith, M.K., Jones, F.H.M., Gilbert, S.L., Wieman, C.E.: The classroom observation protocol for undergraduate STEM (COPUS): a new instrument to characterize university STEM classroom practices. CBE—Life Sci. Educ. 12(4), 618–627 (2013). https://doi.org/10.1187/cbe.13-08-0154

  49. Frey, R.F., Halliday, U., Radford, S., Wachowski, S.: Development of an observation protocol for teaching in interactive classrooms (OPTIC). Abstracts of Papers of the American Chemical Society, vol. 257 (2019)

    Google Scholar 

  50. Billingham, L.: Improving academic library website accessibility for people with disabilities. Libr. Manag. 35(8/9), 565–581 (2014). https://doi.org/10.1108/LM-11-2013-0107

    Article  Google Scholar 

  51. Shawar, B.A.: Evaluating web accessibility of educational websites. Int. J. Emerg. Technol. Learn. (iJET) 10(4), 4–10 (2015)

    Article  Google Scholar 

  52. Babin, L.A., Kopp, J.: ADA website accessibility: what businesses need to know. J. Manag. Policy Pract. 21(3), 99–107 (2020). https://doi.org/10.33423/jmpp.v21i3.3144

    Article  Google Scholar 

  53. Rysavy, M.D.T., Michalak, R.: Assessing the accessibility of library tools and services when you aren’t an accessibility expert: part 1. J. Libr. Adm. 60(1), 71–79 (2020). https://doi.org/10.1080/01930826.2019.1685273

    Article  Google Scholar 

  54. Manning, R., et al.: Pa11y (2021). https://pa11y.org

  55. Smith, J., Whiting, J.: WAVE Web Accessibility Evaluation Tool (2021). https://wave.webaim.org

  56. Caldwell, B. et al. (eds.) Web Content Accessibility Guidelines (WCAG) 2.0. (2008). https://www.w3.org/TR/WCAG20/

  57. Carpenter, S.K., Wilford, M.M., Kornell, N., Mullaney, K.M.: Appearances can be deceiving: instructor fluency increases perceptions of learning without increasing actual learning. Psychon. Bull. Rev. 20(6), 1350–1356 (2013). https://doi.org/10.3758/s13423-013-0442-z

    Article  Google Scholar 

  58. Menekse, M., Stump, G.S., Krause, S., Chi, M.T.H.: Differentiated overt learning activities for effective instruction in engineering classrooms. J. Eng. Educ. 102(3), 346–374 (2013). https://doi.org/10.1002/jee.20021

    Article  Google Scholar 

  59. Deslauriers, L., McCarty, L.S., Miller, K., Callaghan, K., Kestin, G.: Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proc. Natl. Acad. Sci. 116(39), 19251–19257 (2019). https://doi.org/10.1073/pnas.1821936116

Download references

Acknowledgements

This material is based in part upon work supported by the US National Science Foundation (NSF) grant #1626765. Any opinions, findings and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the NSF.

The POGIL Project (http://pogil.org) and the broader POGIL community have provided invaluable advice, encouragement, and support.

Author information

Authors and Affiliations

  1. Green Mango Associates, LLC, Bethlehem, PA, 18018, USA

    Clif Kussmaul

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, 19104, USA

    Tammy Pirmann

Authors
  1. Clif Kussmaul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tammy Pirmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Clif Kussmaul .

Editor information

Editors and Affiliations

  1. Institute of Education, University of Szeged, Szeged, Hungary

    Beno Csapó

  2. School of Engineering, University of Ulster, Newtownabbey, UK

    James Uhomoibhi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kussmaul, C., Pirmann, T. (2022). Guided Inquiry Learning with Technology: Community Feedback and Software for Social Constructivism. In: Csapó, B., Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2021. Communications in Computer and Information Science, vol 1624. Springer, Cham. https://doi.org/10.1007/978-3-031-14756-2_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14756-2_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14755-5

  • Online ISBN: 978-3-031-14756-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation