Competitive Agents and Adaptive Difficulty Within Educational Video Games

Abstract

The entity players compete with is an important element of competitive mechanisms. However, this crucial element is barely investigated within educational video games, as educational psychology research focuses mainly on supportive role models (e.g., pedagogical agents, intelligent tutorial systems). Nevertheless, the influence on learning must be explored, as interaction with an opponent might accompany the whole learning process. Thus, an experiment was conducted comparing three forms of social competition with an emphasis on external valid applications. More specifically, playing against a human competitive agent, playing against an artificial competitive agent, and playing against an artificial leaderboard were compared. Additionally, methods of adaptive difficulty adjustment were included within these groups to harness the potential of artificial systems. The results of the study (N = 102) revealed a beneficial effect of adaptive mechanisms on learning performance and efficiency. Furthermore, a difference in play behavior could be observed. The participants reported a lowered feeling of shame, increased empathy, and behavioral engagement when facing competitive agents. In contrast, calculations revealed no significant impact on mental strains by potentially demanding social competitors. These results highlight the potential for the future development of adaptive game systems and help choose the optimal implementation of social competition within different educational video games.

The effect of adaptivity in digital learning technologies. Modelling learning efficiency using data from an educational game

“…Yet the way we operationalised learning efficiency, differs from previous studies (Molenaar et al, 2019;Nebel et al, 2020). First, we modelled moment-by-moment learning at the more fine-grained level of the responses to items while, for example, Nebel et al (2020) assessed learning efficiency based on the mean number of correct answers and the average time students needed to complete a task. Second, we assessed learning efficiency on a continuous dimension that is directly related to the progress made over time.…”

“…The results yielded that the variation in trajectories obtained by the growth curves could explain the variation in literacy performance better than tests taken at a single timepoint. Finally, Nebel et al (2020) combined the time learners needed to respond to questions and the number of correct responses to calculate learning efficiency using a very specific formula. They concluded that an adaptive competitive element increased efficiency more than competing against human opponents.…”

“…their learning rate) which can be used as a determinant of students’ learning efficiency. Third, in comparison to using a wide range of tests that are administered before and after an intervention, analysing log‐data is a non‐intrusive way to assess students without requiring a transfer for the tasks in the learning environment to the (standardised) tests (Nebel et al, 2020; Thomson et al, 2020).…”

Competition in a Household Energy Conservation Game

“…The competition feature is displayed as a simulated leaderboard where, through displays of rank, comparisons with other virtual households are presented (see Figure 8). Nebel et al [69] describe this design as 'artificial social competition' where opponents offer humanlike features, by means of real-looking scores and family names, without actually being human but simulated by a computer algorithm. The energy conservation data of virtual households follow a logical pattern based on the real-time energy conservation results of the real household (see Figure 9).…”

Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study

“…Video game difficulty refers to the amount of skill required by the player to progress through the game experience. Studying how to set an adequate difficulty level has attracted particular attention in the educational video games field [ 7 , 8 ]. Basic approaches to setting difficulty include allowing users to manually select levels and increasing the difficulty at a steady rate over the course of the game, with earlier levels being easier and later levels being harder [ 9 ].…”