The load-based learner profile for incidental word learning task generation

In recent years, the popularity and prosperity of mobile technologies and e-learning applications offer brand-new learning ways for people. English, as the most widely used language and the essential communication skill for people in the 'earth village' nowadays, has been widely learned by speakers of other languages. The importance of word knowledge in learning a second language is broadly acknowledged in the second language research literature. However, comparing with incidental word learning, the intentional learning method has the shortages of motivating reduction, simple acquisition and contextual deficiency. To address these problems, in this paper, we therefore proposed an incidental word learning model for e-learning. In particular, we measure the load of various incidental word learning tasks from the perspective of involvement load hypothesis so as to construct load-based learner profiles. To increase the effectiveness of various word learning activities and motivate learners better, a task generation method is developed based on the load-based learner profile. Moreover, we conduct experiments on real participants, and empirical results of which have further verified the effectiveness of the task generation method and the enjoyment of word learning.