Erthel and Jamet (2013) undertook a quantitative study and explored the scaffolds of how educational games could enhance learners’ affordances with using KCR feedback as an intervention. The name of the study was “Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness.” The purpose of the study was to unfold the affordances of how digital game-based learning (DGBL) could be effective and employed cognitively as a medium in a deep learning and instructional environment. This study explored the effects of deep and cognitive learning from serious digital games, many researchers agreed that “digital learning games have everything it takes to become an effective learning medium” (p. 157). However, Erthel and Jamet (2013) noted a problem across the DGBL literature and contended that many other researchers have studied and demonstrated the effects of DGBL in an unsystematic formation in terms of learning and motivation effects. On the contrary, DGBL “benefits have never been systematically demonstrated” (p. 156), according to the researchers. Since DGBL research had not been systematically demonstrated, the researchers, therefore, presented a valid argument to undertake DGBL research and explore the effects of DGBL while highlighting the following constructs: learning instruction, entertainment instruction, performance goals, mastery goals, and intrinsic motivation.
In terms of evaluation, this study is a valuable source because the researchers demonstrated that DGBL is a medium that may unfold deep learning benefits in a learning instructional and entertainment instructional environment with the appropriate intervention; however, DGBL needs a systematic review of the affordances that it offers to unfold these cognitive benefits and scaffolds to learners. DGBL, according to the researchers, is a highly regarded digital learning medium with virtual and motivational learning effects that “can be regarded as an entertainment medium designed to bring about cognitive changes in its players” (p. 156). Moreover, the researchers highlight the many cognitive benefits and effects that DGBL may offer its players in terms of motivation and “engagement.” For instance, DGBL compares to other digital learning mediums because DBGL is a cognitive learning medium that have benefits for knowledge acquisition, knowledge sharing and knowledge transfer; therefore, some researchers argue that DGBL is more “beneficial than traditional classroom setting” (p. 157). On the contrary, other scholars question the benefits of DBGL. For instance, DBGL “imposes considerable constraints that make it extremely difficult to integrate deep content, strategies, and skills” (p. 157). DGBL “has raised fresh doubts about the benefits of DGBL” when compared to conventional environments, that is, DBGL has a “weak motivational benefit” (p. 157). In evaluating this study, the studies of DGBL across the literature are fragmented and posits many confounding issues and contradictory factors about the benefits of DBGL in terms of deep learning as a serious game. Finally, there is dire need to undertake additional studies about DGBL in terms of its benefits to deep and cognitive learning. The problem is that “no one has so far subjected the [DGBL] games’ instructions to scientific scrutiny…. even though they are a fundamental feature of DGBL” (p. 158). Thus, this helps in shaping my argument that DGBL and other digital learning games needs further scientific scrutiny or systematic research in terms of effects on cognition and deep learning.
In terms of reflection, the researchers do not highlight the conceptual framework, hypotheses, and research questions under a single paragraph of the study. In fact, the researchers do not even mention the conceptual framework throughout the study. Locating the conceptual framework, research questions, and hypotheses, in my opinion, is like unfolding a complex jigsaw puzzle. However, based on the title of the study, the context in the abstract, the body of the study and the general discussion, it is obvious that Table 1 comprises the constructs and measurements that make up the conceptual framework of the study. Table 1, for instance, highlights the dependent variables (performance goals, mastery goals, and intrinsic motivation) that are assessed against the independent variable (learning and entertainment). Additionally, a reader must dig deep into the general discussion to determine the underlining research question, which is “is deep learning compatible to serious games?” Scattered in the study are hypotheses surrounding this research question. Thus, this source, nevertheless, is foundational and helpful in digital based learning environments even though the critical constructs of the study is fragmented.
Erhel, S. s., & Jamet, E. e. (2013). Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness. Computers & Education, 67156-167.