Introducing a new intelligent adaptive learning content generation method
Abstract
E-learning environments are being used more efficiently by the rapid growth in internet and multimedia technologies. Adaptive learning is a kind of learning environment which provides individual learning. It can customize the learning style according to the individual's personality and characteristics. Although there are a lot of e-learning systems having adaptive learning feature, they do not satisfy all adaptive learning aspects. This paper proposes a new method which tries to help learners find educational contents adapted to their personalities in an efficient manner. Our proposed method has four essential parts: 1) It finds out learner's features by Bayesian networks. 2) Then It tries to select the most appropriate adaptive learning objects with 0/1 knapsack problem in a limited amount of time determined by learner. 3) An ant colony optimization algorithm is proposed to solve 0/1 knapsack problem efficiently. 4) Selected learning objects are then sequenced in order to preserve the prerequisites. Also we created a software application based on this method called BehAmooz for learners to find and comprehend the educational contents effectively.
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In [39], Bayesian networks and ant colony optimization were used to infer learner features and select and sequence learning objects under time constraints.
“Bayesian networks are used to infer the learner's features based on some input data (probably their interactions or preferences in the e-learning system).Once these features are understood, the system uses the 0/1 knapsack problem to select the best learning objects for the learner in the given time constraint.The ACO algorithm helps to solve this knapsack problem efficiently.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [39]
Cites
(1979) · Cited by 43,841 · see citing works
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081 · see citing works
Ant colony optimization: a new meta-heuristic
Dorigo, M., & Di, G. A. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.782657 · Cited by 2,139 · see citing works
Inspiration for optimization from social insect behaviour
Bonabeau, E., Dorigo, M., & Theraulaz, G. (2000) · Nature · doi:10.1038/35017500 · Cited by 915 · see citing works
The ant system applied to the quadratic assignment problem
Maniezzo, V., & Colorni, A. (1999) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.806935 · Cited by 747 · see citing works
Using Bayesian Networks to Manage Uncertainty in Student Modeling
Conati, C., Gertner, A., & VanLehn, K. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021258506583 · Cited by 493 · see citing works
Jaszkiewicz, A. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2002.802873 · Cited by 479 · see citing works
An Algorithm for Large Zero-One Knapsack Problems
Balas, E., & Zemel, E. (1980) · Operations Research · doi:10.1287/opre.28.5.1130 · Cited by 471 · see citing works
From adaptive hypermedia to the adaptive web
Brusilovsky, P. L., & Maybury, M. T. (2002) · Communications of the ACM · doi:10.1145/506218.506239 · Cited by 415 · see citing works
Approximate Algorithms for the 0/1 Knapsack Problem
Sahni, S. K. (1975) · Journal of the ACM · doi:10.1145/321864.321873 · Cited by 351 · see citing works
Development of an adaptive learning system with two sources of personalization information
Tseng, J. C. R., Chu, H., Hwang, G., & Tsai, C. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.08.002 · Cited by 327 · see citing works
New trends in exact algorithms for the 0–1 knapsack problem
Martello, S., Pisinger, D., & Toth, P. (2000) · European Journal of Operational Research · doi:10.1016/s0377-2217(99)00260-x · Cited by 293 · see citing works
A Branch and Bound Method for the Multiconstraint Zero-One Knapsack Problem
Shih, W. (1979) · Journal of the Operational Research Society · doi:10.1057/jors.1979.78 · Cited by 255 · see citing works
Course sequencing techniques for large-scale web-based education
Brusilovsky, P. L., & Vassileva, J. (2003) · International Journal of Continuing Engineering Education and Life-Long Learning · doi:10.1504/ijceell.2003.002154 · Cited by 239 · see citing works
Efficient algorithms for solving multiconstraint zero-one knapsack problems to optimality
Gavish, B., & Pirkul, H. (1985) · Mathematical Programming · doi:10.1007/bf02591863 · Cited by 225 · see citing works
ActiveMath: A Generic and Adaptive Web-Based Learning Environment
Erica Melis, C., Andrés, E., Büdenbender, J., Frischauf, A., Libbrecht, P., Pollet, M., Ullrich, C., Andrés, E., Büdenbender, J., & Goguadze, G. (2001) · HAL · Cited by 217 · see citing works
Chen, C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.11.026 · Cited by 167 · see citing works
Tsai, C. (2004) · British Journal of Educational Technology · doi:10.1111/j.0007-1013.2004.00411.x · Cited by 128 · see citing works
Heuristics and reduction methods for multiple constraints 0–1 linear programming problems
Fréville, A., & Plateau, G. (1986) · European Journal of Operational Research · doi:10.1016/0377-2217(86)90042-1 · Cited by 80 · see citing works
A juggling act in the classroom: Managing different learning styles
Arthurs, J. B. (2006) · Teaching and learning in nursing · doi:10.1016/j.teln.2006.10.002 · Cited by 74 · see citing works
Martinez, M., & Bunderson, C. V. (2000) · The Journal of Interactive Learning Research · doi:10.5555/351772.351780 · Cited by 64 · see citing works
Adaptive and intelligent web based education system: Towards an integral architecture and framework
Cruz, A. C., Peña‐Ayala, A., Valderrama, R. P., Sossa, H., & Tornés, A. F. G. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.08.034 · Cited by 61 · see citing works
Comparison of local search-based metaheuristics on the multiple objective knapsack problem
Jaszkiewicz, A. (2001) · Foundations of Computing and Decision Sciences · Cited by 56 · see citing works
Lo, J., Wang, H.-M., & Yeh, S.-W. (2003) · Computers & Education · doi:10.1016/s0360-1315(03)00064-2 · Cited by 53 · see citing works
The Adaptive Learning System Based on Learning Style and Cognitive State
Chen, S., & Zhang, J. (2008) · International Symposium on Knowledge Acquisition and Modeling (KAM) · doi:10.1109/kam.2008.60 · Cited by 47 · see citing works
Relational Dynamic Bayesian Networks
Sanghai, S. K., Domingos, P., & Weld, D. S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1625 · Cited by 45 · see citing works
Chen, C., Huang, T.-C., Li, T., et al. (2007) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2007.205 · Cited by 33 · see citing works
On the development of a cooperative tutoring environment on computer networks
Hwang, G.-J. (2002) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2002.804451 · Cited by 24 · see citing works
A New Approach for Solving 0/1 Knapsack Problem
Lee, C., Lee, Z.-J., & Su, S. (2006) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2006.384598 · Cited by 23 · see citing works
Structured support versus learner choice via the World Wide Web (WWW): where is the payoff?
Collis, B. A., Winnips, K., & Moonen, J. C. M. M. (2000) · The Journal of Interactive Learning Research · Cited by 23 · see citing works
Solving Large Zero-One Knapsack Problems.
Balas, E., & Zemel, E. (1977) · Defense Technical Information Center (DTIC) · Cited by 18 · see citing works
Manouselis, N., & Sampson, D. G. (2003) · International Journal of Computers and Applications · doi:10.1080/1206212x.2003.11441685 · Cited by 13 · see citing works
Automatic Generation of a Navigation Structure for Adaptive Web-Based Instruction
Masthoff, J. (2002) · Cited by 10 · see citing works
Towards a More Accurate Knowledge Level Estimation
Kardan, S., & Kardan, A. A. (2009) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2009.154 · Cited by 7 · see citing works
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
Gonaygunta, H., Nadella, G. S., Pramod Pawar, P., & Kumar, D. (2024) · 2024 Systems and Information Engineering Design Symposium (SIEDS) · doi:10.1109/sieds61124.2024.10534661 · Cited by 39 · see citing works
- Authors
- Haghshenas, E., Mazaheri, A., Gholipour, A., Tavakoli, M., Zandi, N., Narimani, H., Rahimi, F., & Nouri, S.
- Affiliations
- Isfahan University of Technology (IR)
- Sharif University of Technology (IR)
- University of Isfahan (IR)
- Venue
- The Second International Conference on E-Learning and E-Teaching (ICELET 2010)
- Publisher
- IEEE
- Published
- 2010-12
- Pages
- 65-71
- OpenAlex
- W2126608586
- Cited by
- 14
- References
- 34
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2020: 1 · 2019: 4 · 2013: 1
- FWCI
- 0.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive learning, personalized learning, Bayesian networks, student modeling, 0-1 knapsack problem, ant colony optimization, learning object selection, learning object sequencing
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