Intelligent assistance for teachers in collaborative e-learning environments
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It was suggested that systematic modelling should analyze three dimensions in the education context: learner's variation, learning domains, and learning activities.
“It was suggested that systematic modelling should analyze three dimensions in the education context: learner's variation, learning domains, and learning activities [25,26].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [25]
Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907 · see citing works
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,498 · see citing works
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454 · see citing works
Learning and Teaching Styles in Engineering Education.
Felder, R., & Silverman, L. K. (1988) · Cited by 3,484 · see citing works
Storing and querying ordered XML using a relational database system
Tatarinov, I., Viglas, S. D., Beyer, K. S., Shanmugasundaram, J., Shekita, E. J., & Zhang, C. (2002) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/564691.564715 · Cited by 2,236 · see citing works
A Roadmap of Agent Research and Development
Jennings, N. R., Sycara, K. P., & Wooldridge, M. (1998) · Autonomous Agents and Multi-Agent Systems · doi:10.1023/a:1010090405266 · Cited by 2,163 · see citing works
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/846183.846188 · Cited by 2,106 · see citing works
Understanding Student Differences
Felder, R. M., & Brent, R. (2005) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2005.tb00829.x · Cited by 1,748 · see citing works
Finding interesting rules from large sets of discovered association rules
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H. T. T., & Verkamo, A. I. (1994) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/191246.191314 · Cited by 718 · see citing works
Evaluating Bayesian networks’ precision for detecting students’ learning styles
García, P., Amandi, A., Schiaffino, S., & Campo, M. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.11.017 · Cited by 445 · see citing works
Self-citationLieberman, H. (1997) · doi:10.1145/258549.258592 · Cited by 371 · see citing works
Surface and deep learning processes in distance education: Synchronous versus asynchronous systems
Offir, B., Lev, Y., & Bezalel, R. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.10.009 · Cited by 297 · see citing works
Analyzing the subjective interestingness of association rules
Liu, B., Hsu, W., Chen, S., et al. (2000) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.889106 · Cited by 232 · see citing works
SIGMOD 2004 : proceedings of the ACM SIGMOD International Conference on Management of Data
Weikum, G., Koenig, A. C., & Deßloch, S. (2004) · Max Planck Institute for Plasma Physics · Cited by 224 · see citing works
Coaching Web-based Collaborative Learning based on Problem Solution Differences and Participation
Constantino-González, M. D. L. Á., Suthers, D. D., & Santos, J. G. E. D. L. (2003) · doi:10.5555/1434845.1434851 · Cited by 135 · see citing works
Characterising effective eLearning resources
Littlejohn, A. H., Falconer, I., & McGill, L. (2006) · Computers & Education · doi:10.1016/j.compedu.2006.08.004 · Cited by 132 · see citing works
Group formation in computer-supported collaborative learning
Wessner, M., & Pfister, H. (2001) · International ACM SIGGROUP Conference on Supporting Group Work (GROUP) · doi:10.1145/500286.500293 · Cited by 122 · see citing works
User profiling in personal information agents: a survey
Godoy, D. L., & Amandi, A. (2005) · The Knowledge Engineering Review · doi:10.1017/s0269888906000397 · Cited by 103 · see citing works
Self-citationUser – interface agent interaction: personalization issues
Schiaffino, S., & Amandi, A. (2003) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2003.09.003 · Cited by 89 · see citing works
Self-citationPromoting Effective Peer Interaction in an Intelligent Collaborative Learning System
Soller, A., Goodman, B. A., Linton, F. N., et al. (1998) · Lecture notes in computer science · doi:10.1007/3-540-68716-5_24 · Cited by 81 · see citing works
Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work
Rodden, T. A., Ellis, C. A., & Zigurs, I. (2001) · Cited by 71 · see citing works
EVA: an interactive Web-based collaborative learning environment
Sheremetov, L., & Arenas, A. G. (2002) · Computers & Education · doi:10.1016/s0360-1315(02)00030-1 · Cited by 64 · see citing works
Generating an informative cover for association rules
Cristofor, L., & Simovici, D. A. (2003) · Proceedings · doi:10.1109/icdm.2002.1184007 · Cited by 50 · see citing works
What Makes Peer Interaction Effective? Modeling Effective Communication in an Intelligent CSCL
Soller, A., Lesgold, A. M., Linton, F. N., et al. (1999) · Cited by 43 · see citing works
Arthur: Adapting Instruction To Accommodate Learning Style.
Gilbert, J. E., & Han, C. Y. (1999) · WebNet · Cited by 42 · see citing works
Adaptive feedback generation to support teachers in web-based distance education
Kosba, E., Dimitrova, V. G., & Boyle, R. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9031-z · Cited by 42 · see citing works
An Adaptive, Collaborative Environment to Develop Good Habits in Programming
Vizcaíno, A., Contreras, J., Favela, J., & Prieto, M. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_30 · Cited by 38 · see citing works
Adaptive Bayes for a Student Modeling Prediction Task Based on Learning Styles
Castillo, G., da Gama, J. M. P., & Breda, A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44963-9_44 · Cited by 28 · see citing works
An instructional framework supporting personalized learning on the Web
Papanikolaou, K. A., & Grigoriadou, M. (2004) · doi:10.1109/icalt.2003.1215039 · Cited by 27 · see citing works
Learner models for supporting awareness and collaboration in a CSCL environment
Gerardo, A., & Yano, Y. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61327-7_112 · Cited by 24 · see citing works
Qu, C., & Nejdl, W. (2001) · ACM SIGUCCS Conference on User Services (SIGUCCS) · doi:10.1145/500956.500991 · Cited by 23 · see citing works
Brusilovsky, P. L., Stock, O., & Strapparava, C. (2000) · Cited by 23 · see citing works
Learner profile management for collaborating adaptive eLearning applications
Alrifai, M., Dolog, P., & Nejdl, W. (2006) · doi:10.1145/1149933.1149938 · Cited by 13 · see citing works
CollabSS: a Tool to Help the Facilitator in Promoting Collaboration among Learners
Borges, M. A. F., & Baranauskas, M. C. C. (2003) · Educational Technology & Society · Cited by 10 · see citing works
Can a CSCL environment promote effective interaction?
Plantamura, P., Roselli, T., & Rossano, V. (2004) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2004.1357617 · Cited by 10 · see citing works
Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Goettl, B. P., Halff, H. M., Redfield, C. L., et al. (1998) · Cited by 9 · see citing works
Automated Personalization of Internet News
Sunderam, A. V. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47952-x_36 · Cited by 6 · see citing works
AMADeUs: A Framework To Support Multi-Dimensional Learner Evaluation
Tedesco, P. C. D. A. R., & Gomes, A. S. (2002) · doi:10.1142/9789812776747_0021 · Cited by 6 · see citing works
Distance learning education using the web
Akhtar, S., & Wyne, M. F. (2006) · Cited by 3 · see citing works
Proceedings of the 19th annual ACM SIGUCCS conference on User services
Vaught, R. S. (1991) · Cited by 2 · see citing works
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Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · see citing works
“It was suggested that systematic modelling should analyze three dimensions in the education context: learner’s variation, learning domains, and learning activities [25, 26].”
BackgroundArtificial Intelligence in Education: A Systematic Literature Review
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124167 · Cited by 977 · see citing works
Empowering Educators to Be AI-Ready
Luckin, R., Cukurova, M., Kent, C., & du Boulay, B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100076 · Cited by 422 · see citing works
Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education
Lameras, P., & Arnab, S. (2021) · Information · doi:10.3390/info13010014 · Cited by 291 · see citing works
Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field
Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2011.2 · Cited by 231 · see citing works
Van Leeuwen, A. (2015) · Journal of Learning Analytics · doi:10.18608/jla.2015.22.11 · Cited by 84 · see citing works
- Authors
- Casamayor, A., Amandi, A., & Campo, M.
- Affiliations
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Corresponding author
- Casamayor, Agustin
- ORCID
- Campo, Marcelo
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 53
- Issue
- 4
- Pages
- 1147-1154
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2013016061
- Cited by
- 106
- References
- 42
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 12 · 2022: 10 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 4 · 2016: 2 · 2015: 8 · 2014: 8 · 2013: 1 · 2012: 11
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- teacher support, cooperative learning, online learning, intelligent agents, conflict detection, rule-based systems, group work, distance education, personalized assistance
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