Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning
With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artific...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/59172 |
| _version_ | 1848760404223721472 |
|---|---|
| author | Fenza, G. Orciuoli, F. Sampson, Demetrios |
| author_facet | Fenza, G. Orciuoli, F. Sampson, Demetrios |
| author_sort | Fenza, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artificial neural networks and reinforcement learning. The underlying idea is that tutoring rules can be, firstly, learned by observing human tutors' behavior and, then, adapted, at run-time, by observing how each learner reacts within a learning environment at different states of the learning process. The Zone of Proximal Development has been adopted as the underlying theory to evaluate efficacy and efficiency of the learning experience. |
| first_indexed | 2025-11-14T10:15:14Z |
| format | Conference Paper |
| id | curtin-20.500.11937-59172 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:15:14Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-591722018-05-09T01:41:44Z Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning Fenza, G. Orciuoli, F. Sampson, Demetrios With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artificial neural networks and reinforcement learning. The underlying idea is that tutoring rules can be, firstly, learned by observing human tutors' behavior and, then, adapted, at run-time, by observing how each learner reacts within a learning environment at different states of the learning process. The Zone of Proximal Development has been adopted as the underlying theory to evaluate efficacy and efficiency of the learning experience. 2017 Conference Paper http://hdl.handle.net/20.500.11937/59172 10.1109/ICALT.2017.124 restricted |
| spellingShingle | Fenza, G. Orciuoli, F. Sampson, Demetrios Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title | Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title_full | Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title_fullStr | Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title_full_unstemmed | Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title_short | Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning |
| title_sort | building adaptive tutoring model using artificial neural networks and reinforcement learning |
| url | http://hdl.handle.net/20.500.11937/59172 |