"This study investigated the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. We used structural equation modeling to examine these relations. We tested three models, obtaining a model with good fit to the data. We found evidence that both the pretest and the in-game measure of student performance significantly predicted learning outcome, while the in-game measure of performance was predicted by pretest data, frustration, and engaged concentration. Moreover, we found evidence for two indirect paths from engaged concentration and frustration to learning, via the in-game progress measure. We discuss the importance of these findings, and consider viable next steps concerning the design of effective learning supports within game environments. We model relations among various student variables and learning outcome in a game.Pretest and in-game performance significantly predict learning outcome.In-game performance is predicted by pretest data, frustration, and engagement.Two indirect paths involving frustration and engagement predict learning."
Valerie J. Shute, Sidney D'Mello, Ryan Baker, Kyunghwa Cho, Nigel Bosch, Jaclyn Ocumpaugh, Matthew Ventura, and Victoria Almeda. 2015. Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Comput. Educ. 86, C (August 2015), 224-235. DOI=http://dx.doi.org/10.1016/j.compedu.2015.08.001
1. SUMMARY
The paper is a multi-method study involving an educational game called Physics Playground with data from in-game log files, observational data, on-task and off-task behaviors, performance-based trait persistence, estimates of prior knowledge, and learning outcomes. The sample population consists of 137 8th and 9th grade students with money compensations. There are 15-min qualitative pretest and isomorphic posttest. Test consists of 32 pictorial multiple choice items. For evaluation, different models were designed and based on learning as a function of knowledge and in-game performance (consisting of different states such as engaged concentration, frustration, confusion, boredom, etc.) The paper found that incoming knowledge (posttest) can predict engaged concentration, in-grame progress and learning outcome. In addition yet more interesting, eustress or helpful frustration was found to improve in-grame performance leading to better posttest results.
2. STRENGTHS
The paper leveraged many prior research works in the construction of it methodology resulting in a very well-built paper. Structural equation modeling (SEM) is also a very interesting direction leading to interesting results.
3. WEAKNESSES
While the paper was able to find interesting role that eustress plays in improving students performance, it was not able to locate the ideal range for eustress to stay in. It is largely due to differences in student personalities and how personality is linked to affect - a topic that the paper did not investigate. The paper also admits that 137 students might be a limitation for this kind of study.
