Whale of a crowd: Quantifying the effectiveness of crowd-sourced serious games

Tellioglu, Umit; Xie, Geoffrey G.; Rohrer, Justin P.; Charles, Prince

doi:10.1109/cgames.2014.6934151

Cited by 4 publications

(6 citation statements)

References 5 publications

(15 reference statements)

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“…One study found, analyzing interactions in an online discussion forum, that the content that best explained and predicted learning was related to uncertainty, decisionmaking, time, collaboration and communication (Hernández-Lara, Perera-Lluna, and Serradell-López 2019). In crowd-sourced serious games, three game-play metrics (active users, session counts and session time) were found to be good indicators of productivity by (Tellioglu et al 2014), while team cohesion and psychological safety may be good performance indicators in multiplayer serious games (Mayer et al 2014). Also, behaviors such as avoiding a concept indicated poor performance (Ketamo 2013).…”

Section: Results On Assessment and Student Profilingmentioning

confidence: 99%

Applications of data science to game learning analytics data: A systematic literature review

Alonso‐Fernández

Calvo-Morata

Freire

et al. 2019

Computers & Education

101

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Data science techniques, nowadays widespread across all fields, can also be applied to the wealth of information derived from student interactions with serious games. Use of data science techniques can greatly improve the evaluation of games, and allow both teachers and institutions to make evidence-based decisions. This can increase both teacher and institutional confidence regarding the use of serious games in formal education, greatly raising their attractiveness. This paper presents a systematic literature review on how authors have applied data science techniques on game analytics data and learning analytics data from serious games to determine: (1) the purposes for which data science has been applied to game learning analytics data, (2) which algorithms or analysis techniques are commonly used, (3) which stakeholders have been chosen to benefit from this information and (4) which results and conclusions have been drawn from these applications. Based on the categories established after the mapping and the findings of the review, we discuss the limitations of the studies analyzed and propose recommendations for future research in this field.

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Section: Results On Assessment and Student Profilingmentioning

confidence: 99%

Applications of data science to game learning analytics data: A systematic literature review

Alonso‐Fernández

Calvo-Morata

Freire

et al. 2019

Computers & Education

101

View full text Add to dashboard Cite

show abstract

“…After the automatic parameter tuning method was introduced, it was much easier for the users to handle complex models. Furthermore, they were able to make improvements, even for the model with a relatively high complexity (59). In the tests of the original approach, the most complex model where the value of the objective function increased had a complexity of 14, which probably also explains why the original approach was better for the low range of complexities.…”

Section: Testing Proceduresmentioning

confidence: 99%

“…Another fascinating application of the crowdsourcing concept is specialized computer games where players can have fun and solve complex scientific problems at the same time [55]. These games are called crowdsourced serious games (this term was first introduced by Tellioglu et al in 2014 [59]) and they have been employed successfully for solving problems that originate from various fields of science. One of the first crowdsourced serious games was FoldIt, which allows players to fold a protein to obtain its tertiary structure.…”

Section: Introductionmentioning

confidence: 99%

Modeling Biological Systems Using Crowdsourcing

Wasik

2018

Foundations of Computing and Decision Sciences

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Crowdsourcing is a very effective technique for outsourcing work to a vast network usually comprising anonymous people. In this study, we review the application of crowdsourcing to modeling systems originating from systems biology. We consider a variety of verified approaches, including well-known projects such as EyeWire, FoldIt, and DREAM Challenges, as well as novel projects conducted at the European Center for Bioinformatics and Genomics. The latter projects utilized crowdsourced serious games to design models of dynamic biological systems, and it was demonstrated that these models could be used successfully to involve players without domain knowledge. We conclude the review of these systems by providing 10 guidelines to facilitate the efficient use of crowdsourcing.

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“…Cagiltay et al have measured the time spent on reading and responding answers in exploring the effects of game design, on learning outcomes and motivation [29]. Tellioglu et al have used session time as an indicator of productivity in Crowd-Sourced Serious Games [30]. Along with time features, other numerical indicators are frequently examined in serious game analytics.…”

Section: Assessing Serious Games For Learningmentioning

confidence: 99%

Examining Students’ Behavior in a Digital Simulation Game for Nurse Training

Novoseltseva

Pons

Jessel

2022

IJSG

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Digital educational games have evolved in recent years due to the need to support education and training focused on non-technical skills. Data gathered through interaction with the graphical user interface are explored and exploited to analyze the players' experience. Many researchers have pointed the importance of analysis of players’ in-game behavior, which can help to enhance the learning process, identify learners' strategies, and improve the effectiveness of the serious game. This study is devoted to the analysis of students' behavior in a simulation game called CLONE, which targets work scheduling, situation awareness, and decision-making. The students’ performance and their behavioral strategies are examined based on sequences analysis of players' in-game actions. Moreover, outlier detection is proposed as an instrument for obtaining information that might help better understand students’ behavior. The findings of the study show that such indicators as time spent on planning schedule, time spent on inspecting additional information, and intensity of delegation activity are significantly higher for successful games than for lost ones. The sequences analysis and clustering reveal students' prevailing in-game strategies, which mostly consist of inspection, reading medical records, delegation, and scheduling. Eventually, outlier detection discloses the game sessions with uncertain strategies and unstructured scheduling.

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Whale of a crowd: Quantifying the effectiveness of crowd-sourced serious games

Cited by 4 publications

References 5 publications

Applications of data science to game learning analytics data: A systematic literature review

Applications of data science to game learning analytics data: A systematic literature review

Modeling Biological Systems Using Crowdsourcing

Examining Students’ Behavior in a Digital Simulation Game for Nurse Training

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