Cognitive Load Optimization of Context and Digital Game-Based Vocabulary Learning and Retention

Incidental vocabulary learning (IVL) is essential for long-term retention. Techniques like multiple-choice glossing and digital game-based vocabulary learning (DGBVL) support IVL through contextual inferencing. However, combining multiple-choice glossing with DGBVL may lead to cognitive overload and learning hindrance. In this study, grounded in cognitive load theory (CLT), the authors explored whether context optimization consistent with CLT affects IVL and retention. They randomly placed 70 A2-level English as a foreign language learners into experimental (with cognitive load optimization of context) and control groups (without this optimization). Unaware of upcoming posttests, both groups completed a DGBVL activity for learning 10 target pseudo-words. In immediate and delayed posttests, the experimental group outperformed the control group, particularly in productive and receptive knowledge gains of meaning and orthography, with minor forgetting. Pedagogically, optimizing the context of DGBVL activities consistent with CLT—by reducing element interactivity and search demand—may promote large, more stable IVL gains.