Involvement Load Hypothesis vs. Technique Feature Analysis in Digital Game-Based Vocabulary Learning Activities

Frameworks like the involvement load hypothesis (ILH) and technique feature analysis (TFA) have proved acceptable success rates in predicting precisely the effectiveness of paper-based incidental vocabulary learning (IVL) activities. However, the prediction precision of these frameworks has not been yet studied and compared in digital game-based vocabulary learning (DGBVL) activities. To do so, 37 male and 29 female Omani-Arabic speakers (18-20 years old) were randomly assigned to a DGBVL activity that induced low, moderate, or high levels of involvement load. The results of the pretest showed that the 20 selected target words, or names of concrete inanimate objects, were new to the participants. After completing the DGBVL activities, the results showed that (1) receptive and productive knowledge gain was enhanced by DGBVL activities differentially depending on the activity type, (2) TFA was more accurate than ILH in prediction, and (3) the prediction of both frameworks was not error-free. Pedagogically, prospective teachers and researchers are recommended to use TFA for designing effective DGBVL activities; however, they must be aware that relying only on the collective score of TFA may not always lead to desired outcomes.