Improving Computer-Assisted Language Learning Through the Lens of Cognitive Load

A contemporary review (over a 10-year period) was conducted into studies that used computer-assisted language learning (CALL) strategies to learn a second language (L2) by considering the impact of cognitive load. Twelve affordances were identified that led to enhanced learning, namely, online annotations and glosses, captioning, digital game-based language learning, videoconferencing and video feedback, visualization-based learning approaches, online instructional content and features, online machine translation tools, online interactive collaborative learning, (meta)cognitive learning strategies argument mapping, computer-mediated dictionary assisted learning, and multiple display screens. Associated with these affordances were a number of conditions and learner characteristics that modified the effectiveness of the affordances such as L2 proficiency. Most learning strategies were used to reduce cognitive load, although a limited number fostered germane cognitive load through generative learning practices. A number of issues associated with measuring cognitive load, multimedia learning, and research designs are also discussed.