RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
May 24, 2017
Hassan Khosravi
Kendra Cooper
Kirsty Kitto
http://orcid.org/0000-0001-8664-6117
Abstract
Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.
How to Cite
Khosravi, H., Cooper, K., & Kitto, K. (2017). RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests. Journal of Educational Data Mining, 9(1), 42–67. https://doi.org/10.5281/zenodo.3554627
##plugins.themes.bootstrap3.article.details##
Keywords
collaborative filtering, matrix factorization, peer-learning environments, recommender systems
References
BAKER, R., CORBETT, A. T., AND ALEVEN, V. 2008. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In International Conference on Intelligent Tutoring Systems. Springer, 406–415.
BARNES, T. 2005. The q-matrix method: Mining student response data for knowledge. In American Association for Artificial Intelligence 2005 Educational Data Mining Workshop. 1–8.
BARNES, T. 2010. Novel derivation and application of skill matrices: The q-matrix method. Handbook on educational data mining, 159–172.
BATES, S. P., GALLOWAY, R. K., AND MCBRIDE, K. L. 2012. Student-generated content: Using peerwise to enhance engagement and outcomes in introductory physics courses. In AIP Conference Proceedings. Vol. 1413. AIP, 123–126.
BECK, J. AND CHANG, K.-M. 2007. Identifiability: A fundamental problem of student modeling. User Modeling 2007, 137–146.
BESWICK, G., ROTHBLUM, E. D., AND MANN, L. 1988. Psychological antecedents of student procrastination. Australian psychologist 23, 2, 207–217.
BETTS, B. 2013. Towards a method of improving participation in online collaborative learning: Curatr. Teaching and Learning Online: New Models of Learning for a Connected World 2, 260–273.
BIGGS, J. 1999. What the student does: teaching for enhanced learning. Higher education research & development 18, 1, 57–75.
BOUD, D., COHEN, R., AND SAMPSON, J. 2014. Peer learning in higher education: Learning from and with each other. Routledge.
CAZELLA, S., REATEGUI, E., AND BEHAR, P. 2010. Recommendation of learning objects applying collaborative filtering and competencies. In Key Competencies in the Knowledge Society. Springer, 35–43.
CECHINEL, C., SICILIA, M.-´A ., S´A NCHEZ-ALONSO, S., AND GARC´IA-BARRIOCANAL, E. 2013. Evaluating collaborative filtering recommendations inside large learning object repositories. Information Processing & Management 49, 1, 34–50.
CHEN, C.-M. 2008. Intelligent web-based learning system with personalized learning path guidance. Computers & Education 51, 2, 787–814.
CHIN, C. AND BROWN, D. E. 2002. Student-generated questions: A meaningful aspect of learning in science. International Journal of Science Education 24, 5, 521–549.
COETZEE, D., LIM, S., FOX, A., HARTMANN, B., AND HEARST, M. A. 2015. Structuring interactions for large-scale synchronous peer learning. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 1139–1152.
DENNY, P., HAMER, J., LUXTON-REILLY, A., AND PURCHASE, H. 2008. Peerwise: students sharing their multiple choice questions. In Proceedings of the fourth international workshop on computing education research. ACM, 51–58.
DESMARAIS, M. C. 2012. Mapping question items to skills with non-negative matrix factorization. ACM SIGKDD Explorations Newsletter 13, 2, 30–36.
DESMARAIS, M. C., BEHESHTI, B., AND NACEUR, R. 2012. Item to Skills Mapping: Deriving a Conjunctive Q-matrix from Data. Springer Berlin Heidelberg, Berlin, Heidelberg, 454–463.
DESMARAIS, M. C. AND NACEUR, R. 2013. A Matrix Factorization Method for Mapping Items to Skills and for Enhancing Expert-Based Q-Matrices. Springer Berlin Heidelberg, Berlin, Heidelberg, 441–450.
DESMARAIS, M. C. AND PELCZER, I. 2010. On the faithfulness of simulated student performance data. In Educational Data Mining (EDM 2010), Pittsburgh, PA, USA, June 11-13. 21–30.
DRACHSLER, H., VERBERT, K., SANTOS, O. C., AND MANOUSELIS, N. 2015. Panorama of recommender systems to support learning. In Recommender systems handbook. Springer, 421–451.
DRASGOW, F. AND HULIN, C. L. 1990. Item response theory.
ERDT, M., FERN´A NDEZ, A., AND RENSING, C. 2015. Evaluating recommender systems for technology enhanced learning: A quantitative survey. IEEE Transactions on Learning Technologies 8, 4, 326– 344.
FAZELI, S., LONI, B., DRACHSLER, H., AND SLOEP, P. 2014. Which recommender system can best fit social learning platforms? In European Conference on Technology Enhanced Learning. Springer, 84–97.
GANTNER, Z., RENDLE, S., FREUDENTHALER, C., AND SCHMIDT-THIEME, L. 2011. Mymedialite: a free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 305–308.
GOMEZ-ALBARRAN, M. AND JIMENEZ-DIAZ, G. 2009. Recommendation and students authoring in repositories of learning objects: A case-based reasoning approach. International Journal of Emerging Technologies in Learning (iJET) 4, 2009, 35–40.
HARDY, J., BATES, S. P., CASEY, M. M., GALLOWAY, K. W., GALLOWAY, R. K., KAY, A. E., KIRSOP, P., AND MCQUEEN, H. A. 2014. Student-generated content: Enhancing learning through sharing multiple-choice questions. International Journal of Science Education 36, 13, 2180–2194.
IMRAN, H., BELGHIS-ZADEH, M., CHANG, T.-W., GRAF, S., ET AL. 2016. Plors: a personalized learning object recommender system. Vietnam Journal of Computer Science 3, 1, 3–13.
KOPEINIK, S., LEX, E., SEITLINGER, P., ALBERT, D., AND LEY, T. 2017. Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. ACM, 409– 418.
KOREN, Y. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426–434.
LEMIRE, D., BOLEY, H., MCGRATH, S., AND BALL, M. 2005. Collaborative filtering and inference rules for context-aware learning object recommendation. Interactive Technology and Smart Education 2, 3, 179–188.
LUMEZANU, C., LEVIN, D., AND SPRING, N. 2007. Peerwise discovery and negotiation of faster paths. In HotNets.
MANGINA, E. AND KILBRIDE, J. 2008. Evaluation of keyphrase extraction algorithm and tiling process for a document/resource recommender within e-learning environments. Computers & Education 50, 3, 807–820.
MANOUSELIS, N., DRACHSLER, H., VUORIKARI, R., HUMMEL, H., AND KOPER, R. 2011. Recommender systems in technology enhanced learning. In Recommender systems handbook. Springer, 387–415.
PARDOS, Z. A. AND HEFFERNAN, N. T. 2010. Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research W & CP.
PURCHASE, H., HAMER, J., DENNY, P., AND LUXTON-REILLY, A. 2010. The quality of a peerwise mcq repository. In Proceedings of the Twelfth Australasian Conference on Computing Education-Volume 103. Australian Computer Society, Inc., 137–146.
RICCI, F., ROKACH, L., AND SHAPIRA, B. 2011. Introduction to Recommender Systems Handbook. Springer US, Boston, MA, 1–35.
ROSENSHINE, B., MEISTER, C., AND CHAPMAN, S. 1996. Teaching students to generate questions: A review of the intervention studies. Review of educational research 66, 2, 181–221.
SALEHI, M. 2013. Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation. Data & Knowledge Engineering 87, 130–145.
THAI-NGHE, N., DRUMOND, L., HORV´ATH, T., KROHN-GRIMBERGHE, A., NANOPOULOS, A., AND SCHMIDT-THIEME, L. 2011. Factorization techniques for predicting student performance. Educational Recommender Systems and Technologies: Practices and Challenges, 129–153.
THAI-NGHE, N., HORV´A TH, T., AND SCHMIDT-THIEME, L. 2010. Factorization models for forecasting student performance. In Educational Data Mining 2011.
VERBERT, K., DRACHSLER, H., MANOUSELIS, N., WOLPERS, M., VUORIKARI, R., AND DUVAL, E. 2011. Dataset-driven research for improving recommender systems for learning. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 44–53.
WINTERS, T. E. 2006. Educational data mining: collection and analysis of score matrices for outcomes based assessment. Ph.D. thesis, University of California, Riverside.
WRIGHT, J. R., THORNTON, C., AND LEYTON-BROWN, K. 2015. Mechanical ta: Partially automated high-stakes peer grading. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 96–101.
BARNES, T. 2005. The q-matrix method: Mining student response data for knowledge. In American Association for Artificial Intelligence 2005 Educational Data Mining Workshop. 1–8.
BARNES, T. 2010. Novel derivation and application of skill matrices: The q-matrix method. Handbook on educational data mining, 159–172.
BATES, S. P., GALLOWAY, R. K., AND MCBRIDE, K. L. 2012. Student-generated content: Using peerwise to enhance engagement and outcomes in introductory physics courses. In AIP Conference Proceedings. Vol. 1413. AIP, 123–126.
BECK, J. AND CHANG, K.-M. 2007. Identifiability: A fundamental problem of student modeling. User Modeling 2007, 137–146.
BESWICK, G., ROTHBLUM, E. D., AND MANN, L. 1988. Psychological antecedents of student procrastination. Australian psychologist 23, 2, 207–217.
BETTS, B. 2013. Towards a method of improving participation in online collaborative learning: Curatr. Teaching and Learning Online: New Models of Learning for a Connected World 2, 260–273.
BIGGS, J. 1999. What the student does: teaching for enhanced learning. Higher education research & development 18, 1, 57–75.
BOUD, D., COHEN, R., AND SAMPSON, J. 2014. Peer learning in higher education: Learning from and with each other. Routledge.
CAZELLA, S., REATEGUI, E., AND BEHAR, P. 2010. Recommendation of learning objects applying collaborative filtering and competencies. In Key Competencies in the Knowledge Society. Springer, 35–43.
CECHINEL, C., SICILIA, M.-´A ., S´A NCHEZ-ALONSO, S., AND GARC´IA-BARRIOCANAL, E. 2013. Evaluating collaborative filtering recommendations inside large learning object repositories. Information Processing & Management 49, 1, 34–50.
CHEN, C.-M. 2008. Intelligent web-based learning system with personalized learning path guidance. Computers & Education 51, 2, 787–814.
CHIN, C. AND BROWN, D. E. 2002. Student-generated questions: A meaningful aspect of learning in science. International Journal of Science Education 24, 5, 521–549.
COETZEE, D., LIM, S., FOX, A., HARTMANN, B., AND HEARST, M. A. 2015. Structuring interactions for large-scale synchronous peer learning. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 1139–1152.
DENNY, P., HAMER, J., LUXTON-REILLY, A., AND PURCHASE, H. 2008. Peerwise: students sharing their multiple choice questions. In Proceedings of the fourth international workshop on computing education research. ACM, 51–58.
DESMARAIS, M. C. 2012. Mapping question items to skills with non-negative matrix factorization. ACM SIGKDD Explorations Newsletter 13, 2, 30–36.
DESMARAIS, M. C., BEHESHTI, B., AND NACEUR, R. 2012. Item to Skills Mapping: Deriving a Conjunctive Q-matrix from Data. Springer Berlin Heidelberg, Berlin, Heidelberg, 454–463.
DESMARAIS, M. C. AND NACEUR, R. 2013. A Matrix Factorization Method for Mapping Items to Skills and for Enhancing Expert-Based Q-Matrices. Springer Berlin Heidelberg, Berlin, Heidelberg, 441–450.
DESMARAIS, M. C. AND PELCZER, I. 2010. On the faithfulness of simulated student performance data. In Educational Data Mining (EDM 2010), Pittsburgh, PA, USA, June 11-13. 21–30.
DRACHSLER, H., VERBERT, K., SANTOS, O. C., AND MANOUSELIS, N. 2015. Panorama of recommender systems to support learning. In Recommender systems handbook. Springer, 421–451.
DRASGOW, F. AND HULIN, C. L. 1990. Item response theory.
ERDT, M., FERN´A NDEZ, A., AND RENSING, C. 2015. Evaluating recommender systems for technology enhanced learning: A quantitative survey. IEEE Transactions on Learning Technologies 8, 4, 326– 344.
FAZELI, S., LONI, B., DRACHSLER, H., AND SLOEP, P. 2014. Which recommender system can best fit social learning platforms? In European Conference on Technology Enhanced Learning. Springer, 84–97.
GANTNER, Z., RENDLE, S., FREUDENTHALER, C., AND SCHMIDT-THIEME, L. 2011. Mymedialite: a free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 305–308.
GOMEZ-ALBARRAN, M. AND JIMENEZ-DIAZ, G. 2009. Recommendation and students authoring in repositories of learning objects: A case-based reasoning approach. International Journal of Emerging Technologies in Learning (iJET) 4, 2009, 35–40.
HARDY, J., BATES, S. P., CASEY, M. M., GALLOWAY, K. W., GALLOWAY, R. K., KAY, A. E., KIRSOP, P., AND MCQUEEN, H. A. 2014. Student-generated content: Enhancing learning through sharing multiple-choice questions. International Journal of Science Education 36, 13, 2180–2194.
IMRAN, H., BELGHIS-ZADEH, M., CHANG, T.-W., GRAF, S., ET AL. 2016. Plors: a personalized learning object recommender system. Vietnam Journal of Computer Science 3, 1, 3–13.
KOPEINIK, S., LEX, E., SEITLINGER, P., ALBERT, D., AND LEY, T. 2017. Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. ACM, 409– 418.
KOREN, Y. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426–434.
LEMIRE, D., BOLEY, H., MCGRATH, S., AND BALL, M. 2005. Collaborative filtering and inference rules for context-aware learning object recommendation. Interactive Technology and Smart Education 2, 3, 179–188.
LUMEZANU, C., LEVIN, D., AND SPRING, N. 2007. Peerwise discovery and negotiation of faster paths. In HotNets.
MANGINA, E. AND KILBRIDE, J. 2008. Evaluation of keyphrase extraction algorithm and tiling process for a document/resource recommender within e-learning environments. Computers & Education 50, 3, 807–820.
MANOUSELIS, N., DRACHSLER, H., VUORIKARI, R., HUMMEL, H., AND KOPER, R. 2011. Recommender systems in technology enhanced learning. In Recommender systems handbook. Springer, 387–415.
PARDOS, Z. A. AND HEFFERNAN, N. T. 2010. Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research W & CP.
PURCHASE, H., HAMER, J., DENNY, P., AND LUXTON-REILLY, A. 2010. The quality of a peerwise mcq repository. In Proceedings of the Twelfth Australasian Conference on Computing Education-Volume 103. Australian Computer Society, Inc., 137–146.
RICCI, F., ROKACH, L., AND SHAPIRA, B. 2011. Introduction to Recommender Systems Handbook. Springer US, Boston, MA, 1–35.
ROSENSHINE, B., MEISTER, C., AND CHAPMAN, S. 1996. Teaching students to generate questions: A review of the intervention studies. Review of educational research 66, 2, 181–221.
SALEHI, M. 2013. Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation. Data & Knowledge Engineering 87, 130–145.
THAI-NGHE, N., DRUMOND, L., HORV´ATH, T., KROHN-GRIMBERGHE, A., NANOPOULOS, A., AND SCHMIDT-THIEME, L. 2011. Factorization techniques for predicting student performance. Educational Recommender Systems and Technologies: Practices and Challenges, 129–153.
THAI-NGHE, N., HORV´A TH, T., AND SCHMIDT-THIEME, L. 2010. Factorization models for forecasting student performance. In Educational Data Mining 2011.
VERBERT, K., DRACHSLER, H., MANOUSELIS, N., WOLPERS, M., VUORIKARI, R., AND DUVAL, E. 2011. Dataset-driven research for improving recommender systems for learning. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, 44–53.
WINTERS, T. E. 2006. Educational data mining: collection and analysis of score matrices for outcomes based assessment. Ph.D. thesis, University of California, Riverside.
WRIGHT, J. R., THORNTON, C., AND LEYTON-BROWN, K. 2015. Mechanical ta: Partially automated high-stakes peer grading. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 96–101.
Section
EDM 2017 Journal Track
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
- No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.