Evaluating Player Strategies in the Design of a Hot Hand Game

The user’s strategy and their approach to decisionmakingare two important concerns when designing user-centricsoftware. While decision-making and strategy are key factors in awide range of business systems from stock market trading tomedical diagnosis, in this paper we focus on the role these factorsplay in a serious computer game. Players may adopt individualstrategies when playing a computer game. Furthermore, differentapproaches to playing the game may impact on the effectivenessof the core mechanics designed into the game play. In this paperwe investigate player strategy in relation to two serious gamesdesigned for studying the ‘hot hand’. The ‘hot hand’ is aninteresting psychological phenomenon originally studied in sportssuch as basketball. The study of ‘hot hand’ promises to shedfurther light on cognitive decision-making tasks applicable todomains beyond sport. The ‘hot hand’ suggests that playerssometimes display above average performance, get on a hotstreak, or develop ‘hot hands’. Although this is a widely heldbelief, analysis of data in a number of sports has produced mixedfindings. While this lack of evidence may indicate belief in the hothand is a cognitive fallacy, alternate views have suggested thatthe player’s strategy, confidence, and risk-taking may accountfor the difficulty of measuring the hot hand. Unfortunately, it isdifficult to objectively measure and quantify the amount of risktaking in a sporting contest. Therefore to investigate thisphenomenon more closely we developed novel, tailor-madecomputer games that allow rigorous empirical study of ‘hothands’. The design of such games has some specific designrequirements. The gameplay needs to allow players to perform asequence of repeated challenges, where they either fail or succeedwith about equal likelihood. Importantly the design also needs toallow players to choose a strategy entailing more or less risk inresponse to their current performance. In this paper we comparetwo hot hand game designs by collecting empirical data thatcaptures player performance in terms of success and level ofdifficulty (as gauged by response time). We then use a variety ofanalytical and visualization techniques to study player strategiesin these games. This allows us to detect a key design flaw the firstgame and validate the design of the second game for use infurther studies of the hot hand phenomenon

Dynamic Difficulty Balancing for Cautious Players and Risk Takers

Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the player's risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the player's response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels

Adaptive Game Input Using Knowledge of Player Capability: Designing for Individuals with Different Abilities

The application of video games has been shown to be valuable in medical interventions, such as the use of Active Video Games in physical therapy. Because patients requiring physical therapy present with both highly variable physical capabilities and unique therapeutic goals, developers of rehabilitation intervention games face the challenge of creating flexible games that they can individualize to each player’s particular needs. This thesis proposes an approach to this problem by identifying and addressing two issues concerning therapy AVG game design. First, regarding the difficulties of individualizing software, a particular complication in the development of AVGs for therapy is the increased complexity of writing input routines based on human body motion, which provides a much larger and more complex domain than traditional, discrete-input game controllers. Second, the primary difficulty in individualizing a therapy game experience to an individual player is that developers must program software with static routines that cannot be modified once compiled and released. Overcoming this aspect of software development is a prime concern that adaptive games research aims to address. The System for Unified Kinematic Input (SUKI) is a software library that addresses both of these concerns. SUKI enables games to adapt to players’ specific therapeutic goals by mapping arbitrary human body movement input to game mechanics at runtime, allowing user-defined body motions to drive gameplay without requiring any change to the software. Additionally, the SUKI library implements a dynamic profile system that alters the game’s configuration based on known physical capabilities of the player and updates this profile based on the player’s demonstrated ability during play. Within the context of the study of adaptive games, the following research presents the details of this approach and demonstrates the versatility and extensibility that it can provide in adapting AVG games to meet individual player needs.https://doi.org/10.17918/D8R94VM.S., Digital Media -- Drexel University, 201

Serious Game Berbasis Taksonomi Bloom: Sebuah Pendekatan Alternatif Penilaian Pembelajaran Matematika Berbantuan Teknologi Informasi

Pada penelitian ini dikembangkan sebuah pendekatan baru dalam penilaian pembelajaran matematika berbantuan serious game dengan melibatkan komponen pengetahuan geometri bangun datar jajar genjang dan komponen pedagogi yakni taksonomi belajar menurut Bloom. Pendekatan penilaian ini diusulkan sebagai alternatif baru merekam data pembelajar yang representatif mewakili karakteristik individu mereka. Serious game yang diimplementasikan dikembangkan mengikuti kerangka teknis serious game yang konstruktivis. Tantangan di serious game didistribusikan ke dalam tiga level domain kognitif Bloom yang dimplementasikan di SD: kemampuan mengingat (C1), memahami (C2), dan menerapkan (C3). Serious game juga mengatur level kesukaran tantangan secara dinamis berdasarkan pengalaman pemain pada tantangan sebelumnya. Pengaturan level kesukaran secara dinamis ditujukan agar pemain tidak cepat frustrasi atau bosan dalam permainan. Serious game yang diimplementasikan dalam penilaian sudah melalui uji penerimaan dan uji tanggapan dari pengguna. Klasifikasi data permainan dilakukan melalui metode Bayes Net (BN), Naïve Bayes (NB), dan J48. Dalam melakukan klasifikasi, penerapan tiga metode digabungkan dengan dua opsi tes: crossvalidation dan percentage split. Klasifikasi di masing-masing perlakukan dikerjakan dalam sepuluh ulangan melibatkan sub data yang dipilih acak sebagai data pengujian. Hasil pengujian menunjukkan: (1) dari delapan skenario pengujian penerimaan pengguna diperoleh bahwa keseluruhan masukan skenario pengujian memberikan luaran yang sesuai harapan; (2) rata-rata skor respon pengguna yang dikumpulkan menggunakan kuesioner skala Likert dengan lima opsi terletak dalam interval kategori respon positif (59,93); (3) persentase kebenaran klasifikasi tertinggi yang dihasilkan pada klasifikasi data permainan adalah 88,5% yang dihasilkan melalui penerapan metode J48 yang digabungkan dengan opsi tes percentage split = 85%. Kategori agreement pada penerapan metode J48 dengan opsi tes percentage split adalah Baik (78%). Dari tiga bentuk pengujian disimpulkan bahwa selayaknya metode J48 dipilih dalam melakukan klasifikasi data permainan serta penilaian melibatkan serious game berbasis taksonomi Bloom dijadikan alternatif dalam penilaian pembelajaran materi geometri bangun datar untuk siswa SD kelas 5. ==================================================================================== We developed a new approach for mathematics' learning assessment applying a serious game which is called BoTySeGa. This approach was proposed as an alternative way for recording learners' data which are representative to understand the characteristic of learners. The game implemented in assessment involves the three aspects of a serious game: game, knowledge, and pedagogy. We involve the plane geometry of parallelogram for the 5th elementary students and Bloom's taxonomy successively as knowledge and pedagogy aspects of the game. The serious game was developed following the serious game constructivist framework for children’s learning. Inside the game; the challenges are distributed into the first three of Bloom's cognitive domain which are implemented in elementary school: remember, understand, and apply. The game system adjusts dynamically challenge's level of difficulty in consideration with players' experience on the previous challenge. This approach is designed to bring players far away of boredom and frustration. The serious game applied in the proposed assessment has been tested through user acceptance testing and players' respond to the usage of the game in assessment. Gameplay data are classified through three methods i.e.: Bayes Net, Naïve Bayes, and J48. Each method is conducted with two testing options: crossvalidation and percentage split. The classification in each treatment is done in ten times of repetition. Test results show that: (1) user acceptance testing involving 85 learners shows that BoTySeGa has fulfilled the learning assessment requirement, (2) the average score of players' response recorded utilizing five-points Likerttype of questionnaire is 59,93, it falls within "Positive" category, (3) the highest percentage of correctly classification of gameplay data is 88,5% which is calculated through classification applying method J48 with percentage split testing option 85%. Level agreement of classification is 0.78 which is Good. Based on testing results, we suggest the use of J48 method for the classification of gameplay data and support the implementation of mathematics’ learning assessment utilizing Bloom's taxonomy-based serious game as an alternative assessment in learning

Ajuste dinâmico de dificuldade híbrido em um jogo do gênero plataforma

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Conforme a indústria de videogames cresce, novos cenários surgem e os jogos devem se manter divertidos para distintos perfis de consumidor, com variados níveis de habilidades e preferências. Assim, surge um campo de pesquisa a partir da percepção e de mecanismos de ajuste da dificuldade nos jogos eletrônicos. Ou seja, um jogo pode ser tedioso quando muito fácil ou frustrante quando muito difícil, precisando oferecer um desafio contínuo e condizente ao jogador para mantê-lo motivado. A implementação de um sistema de dificuldade, ao se adaptar automaticamente à performance do jogador, pode melhorar a experiência geral do jogador com o jogo. Esses sistemas são comumente lineares, seguindo um padrão médio do público almejado. No entanto, a dificuldade pode ser adaptada de acordo com o desempenho do jogador, com seu estado afetivo ou a conjunção de ambos os modelos. No âmbito deste trabalho, objetiva-se investigar um método de estimação da dificuldade de níveis de jogos do gênero plataforma e se um mecanismo híbrido do Ajuste Dinâmico de Dificuldade (ADD) possibilita adequar a dificuldade ao jogador e mantê-lo em estado de fluxo, além de comparar sua eficiência com os outros modelos. Para isso, um jogo foi desenvolvido para se adaptar com base aos dados extraídos por algoritmos de análise de desempenho do jogador correlacionados aos obtidos por um sensor de captura de dados fisiológicos, mais especificamente pela Atividade Eletrodérmica (EDA). Além de jogar com os distintos modelos de ADD, cada participante respondeu questionários e teve seus dados coletados para averiguação dos objetos de pesquisa. O modelo híbrido demonstrou-se capaz de manter o jogador em estado de fluxo e adequar a dificuldade ao jogador, com resultados superiores aos demais modelos.As the video game industry grows, new scenarios arise and games should be entertaining for different consumer profiles with varying skill levels and preferences. Thus, a field of research emerges from the perception and mechanisms of adjustment of the difficulty in electronic games. That is, a game can be tedious when very easy or frustrating when very difficult, needing to offer a continuous and appropriate challenge to the player to keep him motivated. The implementation of a difficulty system, when adapting automatically to the performance of the player, can improve the overall experience of the player in the game. These systems are commonly linear, following the average pattern of the target audience. However, the difficulty can be adapted according to the player’s performance, his affective state or the conjunction of both models. In the scope of this work, the objective is to investigate a method that estimates the difficulty of game levels of the platform genre and if a hybrid Dynamic Difficulty Adjustment (DDA) mechanism makes it possible to adapt the difficulty to the player and keep him in a state of flow, besides comparing its efficiency with the other models. For this, a game was developed to adapt based on the data extracted by analysis algorithms of the player’s performance correlated to those obtained by a sensor that captures physiological data, more specifically by the Electrodermal Activity (EDA). In addition to playing with the different DDA models, each participant answered questionnaires and had their data collected for inquiry purposes. The hybrid model was able to keep the player in a state of flow and to adapt the difficulty to the player, with superior results compared to other models