Abstract
Designing a good curriculum or an appropriate learning path for learners is challenging because it requires a very good and clear understanding of the subjects concerned as well as many other factors. One common objective of educational data mining and learning analytics is to assist learners to enhance their learning via the discovery of interesting and useful patterns from learning data. We have recently developed a technique called skill2vec, which utilizes an artificial neural network to automatically identify the relationship between skills from learning data. The outcome of skill2vec can help instructors, course planners and learners to have a more objective and data-informed decision making. Skill2vec transforms a skill to a vector in a new vector space by considering the contextual skills. Such a transformation, called embedding, allows the discovery of relevant skills that may be implicit. We conducted experiments on two real-world datasets collected from an online intelligent tutoring system. The results show that the outcome of skill2vec is consistent and reliable.
Original language | English |
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Title of host publication | Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings |
Editors | Ahmad Fauzi MOHD AYUB, Antonija MITROVIC, Jie-Chi YANG, Su Luan WONG, Wenli CHEN |
Publisher | Asia-Pacific Society for Computers in Education |
Pages | 86-91 |
Number of pages | 6 |
ISBN (Print) | 9789869401265 |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 25th International Conference on Computers in Education - Rydges Latimer Hotel, Christchurch, New Zealand Duration: 4 Dec 2017 → 8 Dec 2017 https://apsce.net/icce/icce2017/140.115.135.84/icce/icce2017/index.html |
Conference
Conference | 25th International Conference on Computers in Education |
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Abbreviated title | ICCE 2017 |
Country/Territory | New Zealand |
City | Christchurch |
Period | 4/12/17 → 8/12/17 |
Internet address |
Funding
The work described in this paper is fully supported by the grant from Research Grants Council of the HKSAR (Ref.: UGC/FDS11/E02/15).
Keywords
- Artificial intelligence
- Learning analytics
- Neural network
- Skill relationship