Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics

Christian Sebastian Loh; Yanyan Sheng

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Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics

机译：测量专家和新手行为之间的（不相似）作为严肃的游戏分析

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The behavioral differences between expert and novice performance is a well-studied area in training literature. Advances in technology have made it possible to trace players' actions and behaviors within an online gaming environment as user-generated data for performance assessment. In this study, we introduce the use of string similarity to differentiate likely-experts from a group of unknown performers (mixture of novices and experts) based on how similar their in-game actions are to that of experts. Our findings indicate that string similarity is viable as an empirical assessment method to differentiate likely-experts from novices and potentially useful as the first performance metric for Serious Games Analytics (SEGA).

机译：专家和新手表现之间的行为差异是培训文献中一个经过充分研究的领域。技术的进步使得在在线游戏环境中追踪玩家的行为和行为成为用户生成的用于绩效评估的数据成为可能。在这项研究中，我们基于字符串在游戏中的行为与专家的相似程度，介绍了使用字符串相似性来区分可能的专家与一群未知执行者（新手和专家的混合物）的方法。我们的研究结果表明，字符串相似度作为一种经验评估方法是可行的，可以将可能的专家与新手区分开来，并且有可能作为严重游戏分析（SEGA）的第一个性能指标。

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《Education and information technologies》 |2015年第1期|5-19|共15页
作者
Christian Sebastian Loh; Yanyan Sheng;
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作者单位

Virtual Environment Lab (V-LAB), Department of Curriculum & Instruction, Southern Illinois University, Carbondale, IL 62901, USA;

Department of Educational Measurement & Statistics, Southern Illinois University, Carbondale, IL 62901, USA;

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关键词
Information Trails~®; String similarity; Serious games assessment; Performance metrics; Serious games analytics (SEGA);

机译：Information Trails〜®;字符串相似度;认真的游戏评估;性能指标;严肃游戏分析（SEGA）;

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专利

1. Maximum Similarity Index (MSI): A metric to differentiate the performance of novices vs. multiple-experts in serious games [J] . Christian Sebastian Loh, Yanyan Sheng Computers in Human Behavior . 2014,第octa期

机译：最大相似度指数（MSI）：一种区分新手和多才多艺游戏在严重游戏中的表现的指标
2. Predicting expert-novice performance as serious games analytics with objective-oriented and navigational action sequences [J] . Loh Christian Sebastian, Sheng Yanyan, Li I-Hung Computers in Human Behavior . 2015,第auga期

机译：通过面向目标和导航操作序列，将专家级新手表现预测为严肃的游戏分析
3. Comparison of similarity measures to differentiate players' actions and decision-making profiles in serious games analytics [J] . Loh Christian Sebastian, Li I-Hung, Sheng Yanyan Computers in Human Behavior . 2016,第nova期

机译：在严肃游戏分析中比较相似性措施以区分玩家的行为和决策概况
4. Asymmetry in Dominant / Non-Dominant Hand Performance Differentiates Novices from Experts on an Arthroscopy Virtual Reality Serious Game [C] . Robert PEDOWITZ, Gregg NICANDRI, Stefan TUCHSCHMID Medicine Meets Virtual Reality . 2016

机译：占主导地位/非主导手术性能的不对称区分新手在关节镜下虚拟现实严重游戏中的专家
5. PROBLEM SOLVING BEHAVIORS AND CONCEPTS OF NOVICES AND EXPERTS DURING INTERACTION WITH A GENETICS COMPUTER SIMULATION [D] . SIMMONS, PATRICIA ELISABETH. 1987

机译：与遗传计算机仿真交互时解决新手和专家的行为和概念的问题
6. Joint action coordination in expert-novice pairs: Can experts predict novices’ suboptimal timing? [O] . Thomas Wolf, Natalie Sebanz, Günther Knoblich -1

机译：新手对中的联合行动协调：专家可以预测新手的最佳时机吗？
7. We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of an in-depth case study of the Rett syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with network analysis to identify the most creative scientists. First, alternative measures of generative and combinatorial creativity are compared. Then, we generalize our results and present a stochastic model of socio-semantic network evolution. The model predictions are tested with multiple networks of rare disease specialties. We find that new scientific collaborations among experts in a field enhance combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By extending the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm. [O] . Massimo Riccaboni, Maria Laura Frigotto 100

机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。

Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics

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