FLEAT VI

Since its very beginning, CALL has often been identified with closed exercises such as multiple choice, fill-in-the-blank or drag-and-drop, allowing for one perfectly predictable and automatically gradable answer. Beatty (2003:11) still argues that “many programs being produced today feature little more than visually stimulating variations on the same gap-filling exercises used 40 years ago”. Meanwhile, the rise of CMC, serious gaming or social media has radically altered the type of communicative activities and tasks digital learning environments can offer. In most cases, we are dealing now with completely open activities allowing for unpredictable and spontaneous production.

However important may be the recent possibilities offered by computer augmented interaction with real world environments or by communication in immersive virtual worlds, one cannot deny that item-based exercise and test platforms allowing amongst others for focus-on-form activities haven’t lost anything of their relevance.

One of the main actual challenges is to make these item-based language learning environments more effective and attractive. This explains why there is for instance a growing interest in adaptivity in order to adjust one or more characteristics of the environment in function of the learner’s needs and preferences and/or the context.

Another challenging approach is to examine to what extent we can further diversify the types of exercises we offer. This presentation offers first of all a consistent typology of all possible exercise types based on such parameters as the degrees of freedom of input, the number of correct answers or the type of correction offered.

We then focus on three exercise types we designed, implemented and evaluated in order to move beyond the closed exercises. We first present “select text” as an example of a half-closed exercise type characterized by a limited degree of freedom of input and a limited number of correct answers but where possible answers are not given in beforehand. Next, we deal with half-open exercises such as “translate” or “reformulate” allowing for many answers, but that can still be automatically graded. We examine to what extent the analysis of learner output using NLP-approaches makes it possible to go beyond (more limited) approximate string matching techniques. We finally tackle the supported open exercise type which combines complete freedom of input with half-automated correction.