The authors wanted to specifically examine what knowledge-construction behaviors are exhibited by elementary school students when using serious games and how these behaviors differ across academic performance levels.23 Nov 2023
Evaluating the Effectiveness of Educational Games
August 7, 2022
Knowledge construction refers to the ways that students solve problems and construct their own understanding of concepts, phenomena, and situations. In other words, how students learn. The current understanding of knowledge construction in game-based learning environments is limited. While studies have linked the adoption of mobile serious games (digital games for learning) and improvements in learning performance and student engagement few have conclusively shown an improvement in learning outcomes.
The authors wanted to specifically examine what knowledge-construction behaviors are exhibited by elementary school students when using serious games and how these behaviors differ across academic performance levels.
The Phases of Knowledge Construction
Academics in the field typically divide knowledge construction behavior into phases or types. The IAM model used by the researchers follows the following five phases: 1) sharing or comparing of information about problems 2) discovery and exploration of dissonance or inconsistency among ideas 3) negotiation of meaning or co-construction of knowledge 4) testing and modification of proposed syntheses or co-construction; 5) agreement statements, or applications of newly constructed meanings. Typically, knowledge construction behaviors are low among elementary school students since they are still developing self-regulation skills and have relatively weaker abstract thinking abilities.
Skills Necessary for Knowledge Construction
The study had 83 participants in classes across third, fifth, and sixth grade in an urban public elementary school in Beijing, China. All participants had more than two years of prior mobile technology-enhanced classroom learning experience. The authors and researchers developed an app that would provide a “personalized, game-like and task-driven self-paced learning environment” about the Chinese mid-Autumn festival to collect the needed data. The app was implemented as a self-paced learning material for four weeks and participants were encouraged to go explore in classes. Teachers were present in the room but did not deliver any lectures.
Performance groups were decided based on participants’ overall accuracy rates when using the app, the high-performing group included the top 25% of students, while the low-performing group the bottom 25%. Differences between the two groups were then analyzed. “The students showed a clear capacity to regulate their learning in a mobile serious game environment.” They demonstrated agency, self-monitoring, and self-evaluation skills. “Results also indicated that, if coupled with feedback, a simple game-like design can empower children to construct their knowledge independently.”
The data also illustrated an interesting difference between the two performance groups. The low-performing group rarely studied or re-studied learning material after they answered a question incorrectly. Whereas the high-performing group tended to go back to try to renegotiate meaning and re-constructed knowledge to modify errors in previous understandings. The low-performing group also tended to watch learning materials repeatedly, getting stuck in a negotiating-of-meaning cycle as they tried different answers again and again.
Creating Systems To Identify Learning Patterns
Students can self-regulate their learning, as early as elementary school, without intervention by teachers. However, low-performing students may need to adjust their learning strategies around self-monitoring and self-evaluation when in self-paced environments. Designers of such technology can facilitate this by creating systems that can identify certain learning patterns and alert users about them. In addition, they could add app features that facilitate social interaction so that students can engage in collective and shared regulation of learning.
“One limitation of empirical measurement of learning-behavior patterns is that it cannot capture how students learn in technology-enhanced environments.”
“To engage in technology-enhanced self-regulated learning effectively individuals must be able to make reasonable determinations of what, when, and how to learn.”
“…when students used self-monitoring record forms right after they started their learning and before they completed it, their learning outcomes and motivation both increased.”
Students, regardless of age, are capable of self-regulated learning and can construct knowledge through independent self-paced learning. Given that self-regulation and self-directed learning is a continuum, educators may still need to provide support to some students. This could be achieved through explicit instruction in self-monitoring and self-evaluation skills to aid the student in reflecting on their learning process.
Sun, Z., Lin, CH., Lv, K. et al. Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences. Education Tech Research Dev 69, 533–551 (2021). https://doi.org/10.1007/s11423-021-09938-x.