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Titlebook: Serious Games Analytics; Methodologies for Pe Christian Sebastian Loh,Yanyan Sheng,Dirk Ifenthal Book 2015 The Editor(s) (if applicable) an

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樓主: SPIR
31#
發(fā)表于 2025-3-26 21:59:04 | 只看該作者
Examining Through Visualization What Tools Learners Access as They Play a Serious Game for Middle Scata sets that can facilitate the interpretation of the relationships among data points at no cost to the complexity of the data. Design implications and future applications of serious games analytics and data visualization to the serious game are discussed.
32#
發(fā)表于 2025-3-27 01:07:16 | 只看該作者
33#
發(fā)表于 2025-3-27 07:03:48 | 只看該作者
34#
發(fā)表于 2025-3-27 13:15:44 | 只看該作者
A Meta-Analysis of Data Collection in Serious Games Researchlect data without influencing its generation, and more fundamentally, how to collect and validate data from humans where a primary emphasis is on what people are thinking and doing. This chapter presents a meta-analysis of data collection activities in serious games research. A systematic review was
35#
發(fā)表于 2025-3-27 14:03:08 | 只看該作者
Guidelines for the Design and Implementation of Game Telemetry for Serious Games Analyticss’ learning and performance. Measuring performance in serious games is often difficult because seldom do direct measures of the desired outcome exist in the game. Game telemetry is conceived as the fundamental element from which measures of player performance are developed. General psychometric issu
36#
發(fā)表于 2025-3-27 20:38:59 | 只看該作者
37#
發(fā)表于 2025-3-28 00:42:01 | 只看該作者
Measuring Expert Performance for Serious Games Analytics: From Data to Insightss for performance measurement and improvement purposes. Instead of a Black box approach (such as pretest/posttest), we can approach serious games as a White box, assessing performance of play-learners by manipulating the . directly. In this chapter, we describe the processes to obtain user-generated
38#
發(fā)表于 2025-3-28 04:58:41 | 只看該作者
Cluster Evaluation, Description, and Interpretation for Serious Gamesver. Calculated variables were extracted from these logs in order to characterize players. Using circular statistics, we show how measures can be extracted that enable players to be characterized by the mean and standard deviation of the time that they interacted with the server. Feature selection w
39#
發(fā)表于 2025-3-28 06:32:06 | 只看該作者
40#
發(fā)表于 2025-3-28 10:36:55 | 只看該作者
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