Automated free-text assessment: Some lessons learned

Abstract : Most e-learning systems engage successively students in reading, writing and assessment activities. In the third phase, the teacher gives feedback on student comprehension, which is often processed a long time after the others, letting the students alone with their difficulties. Thus, there is room to devise automated assessment systems on course comprehension, based on NLP techniques such as latent semantic analysis (LSA). The aim of this paper is to present some systems devised to complete this aim, which implement LSA to model learners' comprehension and/or to compare reading material (e.g., course text) with learners' summaries about it, select reading materials and predict student processes from their summaries.
Document type :
Journal articles
Complete list of metadatas

Cited literature [52 references]  Display  Hide  Download
Contributor : Philippe Dessus <>
Submitted on : Monday, June 18, 2018 - 10:52:10 AM
Last modification on : Wednesday, June 19, 2019 - 1:09:04 AM
Long-term archiving on : Wednesday, September 26, 2018 - 6:14:09 PM


Files produced by the author(s)




Philippe Dessus, Benoît Lemaire, Mathieu Loiseau, Sonia Mandin, Emmanuelle Villiot Leclercq, et al.. Automated free-text assessment: Some lessons learned. International Journal of Continuing Engineering Education and Life-Long Learning, Inderscience, 2011, 21 (2/3), pp.140-154. ⟨10.1504/IJCEELL.2011.040195⟩. ⟨hal-00843588⟩



Record views


Files downloads