Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Zohaib, Mohammad

doi:10.1155/2018/5681652

Cited by 147 publications

(69 citation statements)

References 26 publications

(32 reference statements)

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“…Several researchers claim that early interventions should also try to reduce distress at failure, and preserve confidence in ability to solve math problems (Dowker, Cheriton, Horton, & Mark, 2019). Adaptive games might reduce frustrations, hence possibly reducing anxiety as children experience more success and less failure due to the integrated adaptivity (Hubalovsky et al , 2019; Jansen et al , 2013; Zohaib, 2018). Only a limited number of intervention studies investigated this assumption.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

The effectiveness of an adaptive digital educational game for the training of early numerical abilities in terms of cognitive, noncognitive and efficiency outcomes

Vanbecelaere¹,

Cornillie²,

Sasanguie³

et al. 2020

Brit J Educational Tech

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Adaptive educational games provide new opportunities to train early numerical skills. However, empirical evidence for the effectiveness of adaptive educational games is scarce. This study investigated the effectiveness of an adaptive game compared to a nonadaptive game in terms of cognitive, noncognitive and efficiency outcomes. In total, 84 children were randomly assigned to a condition in which children trained early numerical skills with an adaptive version of the Number Sense Game (NSG), or to a condition in which they trained with a nonadaptive version. Early numeracy was evaluated before the training, immediately after the training and 3 weeks after the training. Math anxiety (MA) was assessed before and 3 weeks after the training. The time children practiced with the NSG was used to assess efficiency. Results revealed that children in both conditions improved on early numerical ability, with sustained effects 3 weeks after the training. In both conditions, children’s MA scores were lower after the training. Children in the adaptive condition learned more efficiently compared to the nonadaptive condition, and the interaction between prior knowledge and condition has shown that children with low prior knowledge benefited more from a nonadaptive training while children with high prior knowledge benefited more from an adaptive training in terms of learning efficiency. These results confirm that adaptive educational games can offer solace in terms of the need for differentiation.

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Section: Theoretical Backgroundmentioning

confidence: 99%

The effectiveness of an adaptive digital educational game for the training of early numerical abilities in terms of cognitive, noncognitive and efficiency outcomes

Vanbecelaere¹,

Cornillie²,

Sasanguie³

et al. 2020

Brit J Educational Tech

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show abstract

“…The goal of the "Monopolist" player was to keep the player in the Csikszentmihalyi 's "Flow zone" [16], where the player's skills correlate with the difficulty of the challenge. The player should be as difficult to play with as possible, but keeping the human player away from the too-difficult or too-easy zone, thus ensuring that his/her entertainment is at optimal level.…”

Section: The "Monopolist" Playermentioning

confidence: 99%

“…The use of Dynamic Difficulty Adjustment techniques allows us to match player ability and the challenge difficulty in real time, while he/she is playing, therefore maximizing the reward and acquisition of knowledge, skill or competence [16].…”

Section: Introductionmentioning

confidence: 99%

Creating Competitive Opponents for Serious Games through Dynamic Difficulty Adjustment

et al. 2020

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Competition is a basic element of our society. It drives us to rise above previously perceived limitations, increases our engagement and makes the world more interesting. Competition rewards our existing skills and prompts us to identify and improve our weaker skills. In games, player engagement is achieved, at least in part, by providing him/her with competition at the right amount of difficulty. Achieving and maintaining this exact level of challenge is one of the most difficult tasks for a game designer. The use of Dynamic Difficulty Adjustment techniques allows the game to dynamically adjust the challenge according to player performance, therefore keeping him/her always on edge, immersed and fully active. New information can then be more easily acquired, which is especially important in Serious Games. This paper describes how DDA techniques were used to create two strategic, goal-oriented computer-controlled (CC) players in order to deliver a higher level of competitiveness for the user in Transform@, a Serious Game aimed at developing entrepreneurship skills. As a result, the strength of the computer controlled player increased by more than 100%. By developing a good strategy for the AI and using DDA the game includes now a powerful opponent which has increased the engagement level of the player.

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“…On the other hand, dynamic difficulty adjustment or dynamic difficulty balancing changes the game behavior according to the skill level of the players. For this purpose, the dynamic difficulty adjustment requires evaluation of the player's performance (through game scores, time, number of errors, player's decisions, etc) and adjustment of a set of game variables that regulate difficulty [ 10 ]. It has been shown that a dynamic approach that uses gamer behavior data presents better learning outcomes than an incremental difficulty approach [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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

Mitre‐Hernandez¹,

Carrillo²,

Lara–Alvarez³

2021

JMIR Serious Games

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Background A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. Objective This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. Methods We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. Results We observed that the proposed filter better estimates a baseline. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ 2 14 =0.05; P =.001); therefore, a Greenhouse-Geisser correction was used (ε=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter ( F 5,78 =30.965; P <.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC ( z =−2.15; P =.03) and peak dilation ( z =−3.58; P <.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. Conclusions The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games.

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Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Cited by 147 publications

References 26 publications

The effectiveness of an adaptive digital educational game for the training of early numerical abilities in terms of cognitive, noncognitive and efficiency outcomes

The effectiveness of an adaptive digital educational game for the training of early numerical abilities in terms of cognitive, noncognitive and efficiency outcomes

Creating Competitive Opponents for Serious Games through Dynamic Difficulty Adjustment

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

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