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Titlebook: Serious Games; 10th Joint Internati Jan L. Plass,Xavier Ochoa Conference proceedings 2025 The Editor(s) (if applicable) and The Author(s),

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31#
發(fā)表于 2025-3-26 22:26:34 | 只看該作者
Students’ Use of an LLM-Powered Virtual Teaching Assistant for Recommending Educational Applicationsevel course on games and learning in Spring 2024. Specifically, we describe how JeepyTA was integrated in two “play journal” assignments where twenty-two students documented their observations of playing classic and contemporary digital games, shared insights from assigned readings, and proposed edu
32#
發(fā)表于 2025-3-27 02:24:29 | 只看該作者
A Taxonomy for Enhancing Metacognitive Adaptivity and Personalization in Serious Games Using Multimoerious games by leveraging multimodal trace data. By integrating diverse sources of multimodal trace data, such as eye tracking, log files, concurrent verbalizations, facial expressions of emotions, physiological sensors, and screen recordings, the taxonomy aims to capture nuanced insights into lear
33#
發(fā)表于 2025-3-27 08:33:20 | 只看該作者
An Architecture for Repeatable, Large-Scale Educational Game Data Analysis: Building on Open Game Dae-grained telemetry data, there exists an unprecedented opportunity to investigate gameplay behaviors, outcomes, and their relationships to learning processes. However, with these opportunities come the need for technical infrastructures to manage the collection and analysis of massive amounts of ga
34#
發(fā)表于 2025-3-27 09:57:15 | 只看該作者
Identifying When and Why Students Choose to Quit Jobs in a Science Exploration Gameucators and system designers may want to know which of these pathways are most effective for engagement, learning, or other desirable outcomes. In this paper, we investigate which prior jobs and factors are associated with higher rates of student quitting behavior in an educational science explorati
35#
發(fā)表于 2025-3-27 16:26:14 | 只看該作者
36#
發(fā)表于 2025-3-27 19:44:34 | 只看該作者
Identifying Player Strategies Through Segmentation: An Interactive Process Visualization Approachext replays, and statistical analysis often necessitate labor-intensive manual iterations to configure data appropriately and typically focus only on predominant trends. To improve our understanding of learner behaviors, this paper introduces a novel interactive visualization system that leverages .
37#
發(fā)表于 2025-3-28 01:06:11 | 只看該作者
38#
發(fā)表于 2025-3-28 02:10:49 | 只看該作者
39#
發(fā)表于 2025-3-28 07:03:53 | 只看該作者
40#
發(fā)表于 2025-3-28 13:48:26 | 只看該作者
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