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Distance learning is used in medical education, even if some recent meta-analyses indicated that it is no more effective than traditional methods. To exploit the technological capabilities, adaptive distance learning systems aim to bridge the gap between the educational offer and the learner’s need. A decrease of lexical competence has been noted in many western countries, so lexical competence could be a possible target for adaptation. The “Adaptive message learning” project (Am-learning) is aimed at designing and implementing an adaptive e-learning system, driven by lexical competence. The goal of the project is to modulate texts according to the estimated skill of learners, to allow a better comprehension. Lexmeter is the first of the four modules of the Am-learning system. It outlines an initial profile of the learner’s lexical competence and can also produce cloze tests, a test based on a completion task. A validation test of Lexmeter was run on 443 medical students of the 1st, 3rd and 6th year at the University “Sapienza” of Rome. Six cloze tests were automatically produced, with ten gaps each. The tests were different for each year and with varying levels of difficulty. A last cloze test was manually created as a control. The difference of the mean score between the easy tests and the tests with a medium level of difficulty was statistically significant for the 3rd year students but not for 1st and 6th year. The score of the automatically generated tests showed a slight but significant correlation with the control test. The reliability (Cronbach alpha) of the different tests fluctuated under and above .60, as an acceptable level. In fact, classical item analysis revealed that the tests were on the average too simple. Lexical competence is a relevant outcome and its assessment allows an early detection of students at risk. Cloze tests can also be used to assess specific knowledge of technical jargon and to train reasoning skill.

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LexMeter: Validation of an Automated System for the Assessment of Lexical Competence of Medical Students as a Prerequisite for the Development of an Adaptive e-learning System