One of the vitally important aspect in distance education is the linguistic formulation of the messages provided to the students during the course. Reading is one of the basic skills in learning and not rarely it could represent the main way of learning in distance education courses. In order to obtain an outline of the potential reader in a distance course, in the first place, we identified the virtual reader profile, i.e. the optimal level of verbal competences needed to comprehend texts presented in the course. The verbal competences are defined starting directly from texts’ lexicon and language. We assumed that the virtual reader knows and fully comprehends the meanings of the words included in texts. In the second place, we attempted to describe the real reader profile. Estimations of reading comprehension and verbal competences, periodically repeated, allowed us to produce a reliable indicator to define the different real profiles of the readers in the distance course. In order to estimate reading comprehension and verbal competences, we developed LexMeter, an automated solution to create tests. This tools allows us to create tests specifically focused on a fixed topic using a full self-sufficient system and limited human intervention, thus reducing teachers’ workload. Generally, we assumed also that the real reader has a lower level of verbal skills compared to the virtual one. This paper aims at illustrating our research about the developing of software prototypes for automated production of cloze tests (LexMeter) and for automated modulation of course texts that could match up the real reader verbal competences (Adapter). In this contribution it will be discussed what is “behind” the planning of this software illustrating both its technological and linguistic features. After the definition of the language model, based on lexical and descriptive-statistical aspects, it will be described how the automated estimator and the text adapter were designed.
Agrusti, F. (2010). From LexMeter to Adapter. Towards a match up between the Virtual and the Real Reader. CADMO, XVIII(1), 97-108.
From LexMeter to Adapter. Towards a match up between the Virtual and the Real Reader
AGRUSTI, FRANCESCO
2010-01-01
Abstract
One of the vitally important aspect in distance education is the linguistic formulation of the messages provided to the students during the course. Reading is one of the basic skills in learning and not rarely it could represent the main way of learning in distance education courses. In order to obtain an outline of the potential reader in a distance course, in the first place, we identified the virtual reader profile, i.e. the optimal level of verbal competences needed to comprehend texts presented in the course. The verbal competences are defined starting directly from texts’ lexicon and language. We assumed that the virtual reader knows and fully comprehends the meanings of the words included in texts. In the second place, we attempted to describe the real reader profile. Estimations of reading comprehension and verbal competences, periodically repeated, allowed us to produce a reliable indicator to define the different real profiles of the readers in the distance course. In order to estimate reading comprehension and verbal competences, we developed LexMeter, an automated solution to create tests. This tools allows us to create tests specifically focused on a fixed topic using a full self-sufficient system and limited human intervention, thus reducing teachers’ workload. Generally, we assumed also that the real reader has a lower level of verbal skills compared to the virtual one. This paper aims at illustrating our research about the developing of software prototypes for automated production of cloze tests (LexMeter) and for automated modulation of course texts that could match up the real reader verbal competences (Adapter). In this contribution it will be discussed what is “behind” the planning of this software illustrating both its technological and linguistic features. After the definition of the language model, based on lexical and descriptive-statistical aspects, it will be described how the automated estimator and the text adapter were designed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
