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首页> 外文会议>International conference on human-computer interaction;International conference on augmented cognition >Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment
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Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment

机译：在基于团队的学习环境中使用配对的关联任务预测学习者的表现

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In this paper, we use a computational cognitive model to make a priori predictions for an upcoming human study. Model predictions are generated in conditions identical to those that human participants will be placed in. Models were built in a computational cognitive architecture, which implements a theory of human cognition, ACT-R (Adaptive Control of Thought - Rational) (Anderson, 2007). The experiment contains three conditions: lecture, interactive lecture, and team-based learning (TBL). Team-based learning has been shown to improve performance compared to the classical non-interactive lecture. Our model predicted the same outcome. It also predicted that players in the TBL condition would perform better than players in the interactive lecture condition.
机译：在本文中，我们使用计算认知模型对即将进行的人类研究做出先验预测。在与人类参与者所处的条件相同的条件下生成模型预测。模型建立在计算认知体系结构中，该体系结构实现了人类认知理论ACT-R（自适应思想控制-理性）（Anderson，2007年） 。该实验包含三个条件：演讲，交互式演讲和基于团队的学习（TBL）。与传统的非交互式讲课相比，基于团队的学习已被证明可以提高绩效。我们的模型预测了相同的结果。它还预测，处于TBL条件下的演奏者的表现将优于处于交互式演讲条件下的演奏者。

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