Zuoxi Jin
ABSTRACT
The adaptive learning system develops gradually, but most attention is paid to the construction of student model and domain model. In this paper, a recommendation algorithm based on students' current knowledge level is proposed to match suitable exercises and avoid homogenization of learning content for all students, for the purpose of achieving so-called "adaptative". It is worth noting that the learning system recommendation is different from the general recommendation. Not only the method, the evaluation standard of recommendation result is also different. We should not simply recommend to students the exercises they must or must not mastered, but recommend to them the learning resources they should have within the range of their abilities according to the theory of proximal development zone. We also use the bayesian knowledge tracing model to judge students' mastery of knowledge as the evaluation standard of this algorithm.
- Murray, T., and Arroyo, I. 2002. Toward Measuring and Maintaining the Zone of Proximal Development in Adaptive Instructional Systems. intelligent tutoring systems, 749--758.DOI= http://dx.doi.org/10.1007/3-540-47987-2_75Google Scholar
- Rollinson, J., and Brunskill, E. 2015. From Predictive Models to Instructional Policies. In Proceedings of the Eighth International Conference on Educational Data Mining, 1--8.Google Scholar
- Wang, Z., Zhu, J., Li, X., Hu, Z., and Zhang, M.. 2016. Structured Knowledge Tracing Models for Student Assessment on Coursera. Acm Conference on Learning. ACM. DOI= http://dx.doi.org/10.1145/2876034.2893416Google ScholarDigital Library
- Heffernan, N. T., & Heffernan, C. L.. 2014. The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470--497. DOI= http://dx.doi.org/10.1007/s40593-014-0024-xGoogle ScholarCross Ref
- Ma, C.C. 2008. A guide to singular value decomposition for collaborative filtering. Computer (Long Beach, CA), 1--14.Google Scholar
- Koren, Y., Bell, R., and Volinsky, C.. 2009. Matrix factorization techniques for recommender systems. Computer, 42(8), 30--37. DOI= http://dx.doi.org/10.1109/mc.2009.263Google ScholarDigital Library
Index Terms
- Exercises Recommendation in Adaptive Learning System
Recommendations
Adaptive personalized recommendation based on adaptive learning
Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-...
Parallel Ratio Based CF for Recommendation System
ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking TechnologiesWith the increase in E-commerce, Recommendation Systems are getting popular to provide recommendations of various items (movies, books, music) to users. To build the Recommendation System (RS), Collaborative Filtering (CF) techniques are proven ...
Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach
ACM SE '19: Proceedings of the 2019 ACM Southeast ConferenceNowadays, recommendation systems are widely deployed to suggest a variety of products and services for target users. Practical examples of recommendation systems that we daily encounter include social, educational, and political services such as ...
Comments