Paper abstract: E-learning platforms often include assessment tools to measure the outcomes of the learning process. Such tools can be used both for self-evaluation, while the learning activity is in progress, and for external final evaluation, e.g. to automatically measure the learning outcomes, ensuring standard evaluation criteria. Assessment is also available during the learning process, for self-evaluation purpose: in this case the student gets some kind of measurement about his knowledge/skills, based on the answers, and hints on the effectiveness of the learning process. Our claim is that computer-based assessment systems can provide a large amount of information, beyond the simple answers, e.g. the time spent on each question and the changes of mind, that hide a deeper insight on students. This preliminary paper introduces a new behavioral student model, based on this concept, discusses some of the techniques useful to analyse these kinds of data and sketches an architecture able to support this analysis.

A “IN-DEEP” BEHAVIORAL MODEL FOR E-LEARNING ASSESSMENT.

MIGNANI, STEFANIA;SARTORI, CLAUDIO
2009

Abstract

Paper abstract: E-learning platforms often include assessment tools to measure the outcomes of the learning process. Such tools can be used both for self-evaluation, while the learning activity is in progress, and for external final evaluation, e.g. to automatically measure the learning outcomes, ensuring standard evaluation criteria. Assessment is also available during the learning process, for self-evaluation purpose: in this case the student gets some kind of measurement about his knowledge/skills, based on the answers, and hints on the effectiveness of the learning process. Our claim is that computer-based assessment systems can provide a large amount of information, beyond the simple answers, e.g. the time spent on each question and the changes of mind, that hide a deeper insight on students. This preliminary paper introduces a new behavioral student model, based on this concept, discusses some of the techniques useful to analyse these kinds of data and sketches an architecture able to support this analysis.
Proceedings of IADIS International Conference e-Learning 2009
160
164
S. Mignani; C. Sartori.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/82002
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact