Predicting Student Actions in a Procedural Training Environment
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911333482496000 |
|---|---|
| author | Riofrío-Luzcando, Diego Ramírez, Jaime Berrocal-Lobo, Marta |
| author_facet | Riofrío-Luzcando, Diego Ramírez, Jaime Berrocal-Lobo, Marta |
| contents | Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are firstly grouped into clusters. Then an extended automaton is created for each cluster based on the sequences of events found in the cluster logs. The main objective of this model is to predict the actions of new students for improving the tutoring feedback provided by an intelligent tutoring system. The proposed model has been validated using student logs collected in a 3D virtual laboratory for teaching biotechnology. As a result of this validation, we concluded that the model can provide reasonably good predictions and can support tutoring feedback that is better adapted to each student type. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Student Actions in a Procedural Training Environment Riofrío-Luzcando, Diego Ramírez, Jaime Berrocal-Lobo, Marta Human-Computer Interaction Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are firstly grouped into clusters. Then an extended automaton is created for each cluster based on the sequences of events found in the cluster logs. The main objective of this model is to predict the actions of new students for improving the tutoring feedback provided by an intelligent tutoring system. The proposed model has been validated using student logs collected in a 3D virtual laboratory for teaching biotechnology. As a result of this validation, we concluded that the model can provide reasonably good predictions and can support tutoring feedback that is better adapted to each student type. |
| title | Predicting Student Actions in a Procedural Training Environment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.19810 |