You are here:

ExplaNet: A Collaborative Learning Tool and Hybrid Recommender System for Student-Authored Explanations
Article

, Boston College, United States ; , University of Washington, United States ; , University of California at Santa Cruz, United States

Journal of Interactive Learning Research Volume 19, Number 1, ISSN 1093-023X Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

Full Text

Abstract

ExplaNet is a web-based, anonymous, asynchronous explanation-sharing network. Instructors post questions to the network and students submit explanatory answers. Students then view and rank the explanations submitted by their peers before optionally resubmitting a final and revised answer. Three classroom evaluations of ExplaNet showed that by using ExplaNet students improved comprehension and retention of difficult concepts. Students who viewed peer-authored explanations between submitting explanations showed greater improvement in submission scores and scores on individual final exam questions than students who did not. In addition, ExplaNet recommends a small subset of explanations to each individual student based on student characteristics and preferences. The recommendation algorithm successfully predicted preferences for student explanations in two classroom trials.

Citation

Masters, J., Madhyastha, T. & Shakouri, A. (2008). ExplaNet: A Collaborative Learning Tool and Hybrid Recommender System for Student-Authored Explanations. Journal of Interactive Learning Research, 19(1), 51-74. Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved August 16, 2024 from .

© 2008 Association for the Advancement of Computing in Education (AACE)

Keywords

References

View References & Citations Map
  1. Allen, E., & Mourtos, N. (2000). Using learning styles preferences data to inform classroom teaching and assessment activities. In, Proceedings of the Frontiers in Education (pp. S2B6). Kansas City, MO.
  2. Baker, M., DeVries, E., & Lund, K. (1999). Designing computer-mediated epistemic interactions. In S. Lajoie & M. Vivet (Eds.), Proceedings of the 9th International Conference on Artificial Intelligence in Education (pp. 139-146). Le Mans, France.
  3. Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communication of the Association for Computing Machinery, 40(3), 66-72.
  4. Berenfeld, B. (1996). Linking students to the infosphere. Technological Horizons in Education Journal, 23(9), 76-84.
  5. Black, P., & William, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappa, 80(2), 139-149.
  6. Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial Intelligence, Proceedings of the Fourteenth Conference (pp. 43-52). Madison, WI: Morgan Kaufmann.
  7. Cambridge, D. (1999). Supporting the development of a national constellation of communities of practice in the scholarship of teaching and learning through the use of intelligent agents. In C. Hoadley & J. Roschelle (Eds.), Proceedings of the Computer Support for Collaborative Learning (pp. 81-85). Palo Alto, CA: Lawrence Erlbaum Associates.
  8. Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In Fayyad, U. M. (Ed.), Advances in knowledge discovery and data mining (pp. 153-180). Cambridge, MA: AAAI Press/MIT Press.
  9. Chi, M. T. H., & Bassok, M. (1988). Learning from examples via self-explanations. In L. Resnick (Ed.), Knowing, learning and instruction: Essays in honour of Robert Glaser (pp. 251-282). Hillsdale, NJ: Lawrence Erlbaum Associates.
  10. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182. Chi, M. T. H., DeLeeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477.
  11. Chickering, A., & Ehrmann, S. C. (1996). Implementing the seven principles: Technology as a lever. American Association for Higher Education Bulletin, October, 3-6.
  12. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. (1999). Combining content-based and collaborative filters in an online newspaper. In, Proceedings of the Association for Computing Machinery Special Interest Group on Information Retrieval Workshop on Recommender Systems. Berkeley, CA.
  13. Felder, R. (1993). Reaching the second tier: Learning and teaching styles in college science education. Journal of College Science Teaching, 23(5), 286-290. Felder, R. (1996). Matters of style. ASEE Prism, 6(6), 18-23.
  14. Geyer-Schulz, A., Hahsler, M., & Jahn, M. (2001). Educational and scientific recommender systems: designing the information channels of the Virtual University. International Journal of Engineering Education, 17(2), 153-163.
  15. Goldman, S. R. (1996). Reading, writing, and learning in hypermedia environments. In H. Van Oostendorp and S. De Mul (Eds.), Cognitive aspects of electronic text processing (pp. 7-42). Norwood, NJ: Ablex Publishing Corporation.
  16. Good, N., Schafer, J. B., Jonstand, J. A., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendations. In, Proceedings of the Conference of the American Association of Artificial Intelligence (pp. 439446). North Falmouth, MA.
  17. Hill, W., Stead, L., & Rosenstein, M. (1995), Recommending and evaluating choices in a virtual community of use. In, Proceedings of the Association for Computing Machinery Special Interest Group on Computer Human Interaction Conference on Human Factors in Computing Systems (pp. 194-201). Denver, CO: ACM Press.
  18. Hmelo, C., Guzdial, M., & Turns, J. (1997). Computer-support for collaborative learning: Learning to make it work. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago, IL. Retrieved on May 11, 2006 from: http://guzdial.cc.gatech.edu/papers/aera97/mbl.html
  19. Lang, K. (1995). NewsWeeder: Learning to filter netnews. In, Proceedings of the International Conference on Machine Learning (pp. 331-339). Lake Tahoe, CA: Morgan Kaufmann.
  20. Liberman, H. (1995). Letizia: An agent that assists web browsing. In, Proceedings of the International Joint Conference on Artificial
  21. Maes, P. (1994). Agents that reduce work and information overload. Communications of the Association for Computing Machinery, 37(7), 31-40.
  22. Masters, J. (2002). Educational applets for active learning in Properties of Materials. (Masters thesis, University of California at Santa Cruz, 2002, UCSC-CRL-02-14).
  23. Masters, J. (2005). ExplaNet: A learning tool and hybrid recommender system of studentauthored explanations. (Doctoral dissertation, University of California at Santa Cruz, 2005).
  24. Masters, J., Madhyastha, T., & Shakouri, A. (2002). Educational applets for active learning in properties of materials. In, Proceedings of the Frontiers in Education Conference (pp. TIF7TIF12). Boston, MA.
  25. Masters, J., Madhyastha, T., & Shakouri, A. (2005). Educational applets for active learning in properties of electronic materials. IEEE Transactions of Education, 48(1), 29-36. Mazur, E. (1997). Peer instruction: A user’s manual. Prentice Hall.
  26. Melville, P., Moone, R. J., & Nagarajan, R. (2001). Content-boosted collaborative filtering. In Proceedings of the Association for Computing Machinery Special Interest Group on Information Retrieval Workshop on Recommender Systems. New Orleans, LA.
  27. National Research Council. (2000). How people learn: Brain, mind, experience, and school. Washington, D. C.: National Academy Press, expanded addition.
  28. Owston, R. D. (1997). The World Wide Web: A technology to enhance teaching and learning? Educational Researcher, 26(2), 27-33.
  29. Pazzani, M., Muramatsu, J., & Billsus, D. (1996). Syskill and Webert: Identifying interesting web sites. In, Proceedings of the National Conference on Artificial Intelligence (pp. 54-61). Portland, OR: AIII Press.
  30. Pressley, M., Wood, E., Woloshyn, V. E., Martin, V., King, A., & Menke, D. (1992). Encouraging mindful use of prior knowledge: Attempting to construct explanatory answers facilitates learning. Educational Psychologist, 27(1), 92-109.
  31. Recker, M. M., & Wiley, D. A. (2000). An interface for collaborative filtering of educational resources. In, Proceedings of the International Conference on Artificial Intelligence (pp. 317323). Las Vegas, NV.
  32. Recker, M. M., & Wiley, D. A. (2001). A non-authoritative educational meta-data ontology for filtering and recommending learning objects. Journal of Interactive Learning Environments, 9(3), 255-271.
  33. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P, & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In, Proceedings of the Association for Computing Machinery Conference on Computer Supported Cooperative Work (pp. 175-186). Chapel Hill, NC.
  34. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the Association for Computing Machinery, 40(3), 56-58.
  35. Rosati, P. (1998). The learning preferences of engineering students from two perspectives. In Proceedings of the Frontiers in Education (pp. 29-32). Tempe, AZ.
  36. Rosati, P. (1999). Specific differences and similarities in the learning preferences of engineering students. In, Proceedings of the Frontiers in Education (pp. 12C1 17-22). San Juan, Puerto Rico. Sarwar, M, Konstant, J. A., Borchers, A., Herlocker, J., Miller, B., & Riedl, J. (1998). Using semiintelligent filtering agents to improve prediction quality in a collaborative filtering system. In, Proceedings of the Association for Computing Machinery Conference on Computer Supported Cooperative Work (pp. 245-254). Seattle, WA.
  37. Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth.” In, Proceedings of the Association for Computing Machinery Conference on Computer Human Interactions (pp. 210-217). Denver, CO: ACM Press.
  38. Simon, H. (2001). Learning to research about learning. In S. M. Carver & D. Klahr (Eds.), Cognition and Instruction: Twenty-five years of profess (pp. 205-226). Mahway, NJ: Lawrence Erlbaum Associates.
  39. Soloman, B. A., & Felder, R. M. (2006). Index of learning styles questionnaire. Retrieved May 11, 2006, from: http://www.engr.ncsu.edu/learningstyles/ilsweb.html
  40. Terveen, L., Hill, W., Amento, B., McDonald, D., & Creter, J. (1997). PHOAKS: A system for sharing recommendations. Communications of the Association for Computing Machinery, 40(3), 59-62.
  41. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
  42. Want, A. Y., & Newlin, M. H. (2001). Online lectures: Benefits for the virtual classroom. Technological Horizons in Education Journal, 29(1), 17-21.
  43. Zhang, K., & Carr-Chellman, A. (2001). Peer online discourse analysis. In, Proceedings of the National Convention of the Association for Educational Communications and Technology (pp. 152-174). Atlanta, GA.
  44. Foundation under Grant No. CCR 0093051 and Grant No. CCL1 0088881. Notes

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References

Cited By

View References & Citations Map

These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact info@learntechlib.org.

Also Read

Related Collections