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Titlebook: User Modeling, Adaptation, and Personalization; 17th International C Geert-Jan Houben,Gord McCalla,Massimo Zancanaro Conference proceedings

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51#
發(fā)表于 2025-3-30 08:22:35 | 只看該作者
52#
發(fā)表于 2025-3-30 16:26:04 | 只看該作者
Sensors Model Student Self Concept in the Classroominimally invasive sensor technology is mature enough to equip classrooms of up to 25 students with four sensors at the same time while using a computer based intelligent tutoring system. The sensors, which are on each student’s chair, mouse, monitor, and wrist, provide data about posture, movement,
53#
發(fā)表于 2025-3-30 17:11:57 | 只看該作者
Use and Trust of Simple Independent Open Learner Models to Support Learning within and across Coursey courses to facilitate self-assessment skills, planning and independent learning. OLMlets is used in specific courses, while UK-SpecIAL, a modular extension to OLMlets, draws on the OLMlets learner models to display progress towards achieving learning outcomes applicable across courses. User logs d
54#
發(fā)表于 2025-3-31 00:27:35 | 只看該作者
55#
發(fā)表于 2025-3-31 02:09:45 | 只看該作者
56#
發(fā)表于 2025-3-31 07:25:31 | 只看該作者
57#
發(fā)表于 2025-3-31 11:15:27 | 只看該作者
58#
發(fā)表于 2025-3-31 13:52:17 | 只看該作者
Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Dabut difficult problems is acquiring sufficient training data in various contexts/situations. Particularly, some situations require a heavy workload to set them up or to collect subjects under those situations. To avoid this, often a large amount of data in a supposed situation is collected, i.e., a
59#
發(fā)表于 2025-3-31 20:12:33 | 只看該作者
Modeling the Personality of Participants During Group Interactionsacoustic features. We designed our task as a regression one where the goal is to predict the personality traits’ scores obtained by the meeting participants. Support Vector Regression is applied to thin slices of behavior, in the form of 1-minute sequences.
60#
發(fā)表于 2025-4-1 01:05:28 | 只看該作者
Predicting Customer Models Using Behavior-Based Features in Shopsnvironments, it is important to attempt characterization of the sensor data for automatically modeling users in a ubiquitous and mobile computing environment. As described herein, we propose a method that predicts a customer model using features based on customers’ behavior in a shop. We capture the
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