The authors are experimenting an innovative procedure to profile learners using an e-learning platform to predict if they will successfully end their training (or education activities) and to help tutors organize their tasks from the very beginning. Predictive learner modelling is proposed as an instrument for planning individual-oriented tutoring strategies to increase not only the probability of completion but also the return on investments of the training activities. In fact, by modelling learners’ profiles it is possible to know in advance who of them will successfully complete their courses, who will leave the training anyway and who needs more help to complete their courses, according to their profiles. Knowing where learners are more likely to succeed will also help optimizing the assessment and training phases.
|Titolo:||Learner modelling : optimizing training, assessment and testing|
|Parole Chiave:||Data mining; Evolutionary algorithms; Learner assessment; Learner modelling; Training|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||giu-2009|
|Appare nelle tipologie:||01 - Articolo su periodico|