The number of times a word appears in the input, i.e., the frequency of occurrence, is a well-known factor influencing incidental vocabulary acquisition (Webb, 2020). However, paradoxically, the frequency of occurrence has only a medium predicting power in vocabulary learning, around r = .34 (Uchihara et al., 2019). Our study proposes to look at how productive uses of the target words help understand and predict incidental vocabulary acquisition, complementing the vision of frequency of occurrence in input by looking at output too in a productive task.
We conducted a large-scale experimental study on the effectiveness of dialogue-based CALL (here, a game allowing players to discuss with computer-controlled conversational agents) for vocabulary learning. In a pretest-posttest experiment, 215 Dutch-speaking teenage learners of French were tested on their receptive and productive knowledge of target words encountered in the dialogue-based CALL interactions.
Results showed significant improvement in vocabulary knowledge for participants in the experimental condition in both the meaning-recognition (d = 1.16) and form-recall tests (d = 0.59). The frequency of occurrence in input (actual encounters for each participant) was the best predictor for receptive vocabulary learning (r = .27 ***), but the frequency of use in output (actual instances in output for each participant) was the best predictor for productive vocabulary learning (r = .28 ***). Integrating all results in mixed-effects models, we were able to determine that not only does the frequency of occurrence in output predicts more strongly the posttest scores than input encounters, it also complements it and raises the model predictive power (R² = .66). We propose a model of incidental vocabulary learning in dialogue-based CALL connecting all variables and conclude on the importance of productive practice in lexical acquisition.