Optimizing learning from examples using animated pedagogical agents.
Optimizing learning from examples using animated pedagogical agents.
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DOI：
10.1037/0022-0663.94.2.416
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发表时间：
2002-06
影响因子：
4.9
通讯作者：
R. Atkinson
中科院分区：
文献类型：
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作者：
R. Atkinson
这项研究试图优化一个基于计算机的学习环境，旨在通过加入一个动画教学代理来教会学习者如何解决应用题。该代理被编程为以文本或听觉方式提供教学解释，同时使用凝视和手势来引导学习者将注意力集中在示例的相关部分。在实验1中，学习者被呈现给一个以听觉方式提供解释的代理(语音加代理)，在迁移测量上优于他们的控制组同龄人。在实验2中，在语音加代理的条件下，学习者的表现优于他们的同龄人，他们被提供了关于各种测量的文本解释，包括远迁移。总而言之，被编程为以听觉方式传递指令的动画代理可以帮助优化从示例中学习。一个有效的例子是一种教学设备，它通过以循序渐进的方式展示解决方案，为解决特定类型的问题提供模型。它旨在为学习者提供专家的解决方案，学习者可以将其用作自己解决问题的模型。到目前为止，在大多数实验中，工作的例子都是视觉上固定的；也就是说，这些例子同时呈现了一个问题和一个专家的解决步骤。因此，这些工作的例子与传统的数学和科学课本中发现的类似；然而，通过多媒体计算机系统提供的教学材料不必以这种方式受到限制。例如，Stark(1999)和Renkl(1997)建议可以通过顺序呈现问题状态来增强示例处理。根据Stark和Renkl的说法，这种类型的演示鼓励学习者通过预测示例解决方案中的下一步，然后检查以确定预测的步骤是否与实际步骤相对应来向自己解释示例-Renkl将这种现象称为预期推理。根据Catrambone(1994、1996、1998)的说法，工作示例的结构应该是强调概念上相关的解决步骤(即子目标)，方法是在视觉上将它们隔离，或给它们贴上标签，或者两者兼而有之。对于需要学习者参考多种信息来源的例子，穆萨维和他的同事(Mouavi，Low，&Sweller，1995)提出了一个简单的解决方案：教学信息的某些部分应该以视觉形式呈现，而其他部分应该以听觉形式呈现(即混合模式格式)。使用计算机进行教学的一个优点是，它使教学设计者能够将多个教学原则或组件结合在一个实例中，这可能会增强其有效性。根据Mayer(1997)的多媒体学习生成理论，与基于书本的媒介相比，计算机还提供了一个更有利的环境来实施某些形式的有效教学，例如将顺序问题状态的视觉呈现与对每个状态的听觉描述相协调。例如，在Atkinson和Derry(2000)中，在基于计算机的多媒体环境中构建示例以使学习最大化的一种方法是创建一个多成分工作示例，该示例(A)是连续的，因为它包括问题状态的顺序呈现；(B)被构造为强调问题子目标(即，它是子目标导向的)；以及(C)结合了与问题状态的顺序呈现相协调的第二通道(即，视觉呈现的步骤和口头的教学解释)。在基于概念的问题解决迁移测量中，接触到这些顺序的、次目标定向的双重模式例子的学习者比那些接触到更传统的、同时的、非子目标定向的例子的学习者表现更好。此外，尽管后一种情况下的例子也是双模的，但这种差异还是发生了。
This study attempted to optimize a computer-based learning environment designed to teach learners how to solve word problems by incorporating an animated pedagogical agent. The agent was programmed to deliver instructional explanations either textually or aurally, while simultaneously using gaze and gesture to direct the learners to focus their attention on the relevant part of the example. In Experiment 1, learners presented with an agent delivering explanations aurally (voice plus agent) outperformed their control peers on measures of transfer. In Experiment 2, learners in the voice-plus-agent condition outperformed their peers presented with textual explanations on a variety of measures, including far transfer. In sum, an animated agent programmed to deliver instructions aurally can help optimize learning from examples. A worked example is an instructional device that provides a model for solving a particular type of problem by presenting the solution in a step-by-step fashion. It is intended to provide the learner with an expert’s solution, which the learner can use as a model for his or her own problem solving. To date, in most experiments, worked examples have been visually fixed; that is, the examples simultaneously presented a problem and an expert’s solution steps. As such, these worked examples are similar to those found in traditional mathematics and science texts; however, instructional materials delivered on multimedia computer systems need not be limited in this way. For example, Stark (1999) and Renkl (1997) suggested that example processing can be enhanced by sequentially presenting problem states. According to Stark and Renkl, this type of presentation encourages learners to explain the examples to themselves by anticipating the next step in an example solution, then checking to determine whether the predicted step corresponded to the actual step—a phenomenon Renkl termed anticipative reasoning. According to Catrambone (1994, 1996, 1998), worked examples should be structured so they emphasize conceptually related solution steps (i.e., subgoals) by visually isolating them, by labeling them, or both. With regard to presenting examples that require learners to reference multiple sources of information, Mousavi and his colleagues (Mousavi, Low, & Sweller, 1995) offer a simple solution: Some segments of instructional information should be presented visually, whereas other segments should be presented aurally (i.e., mixed-mode format). One advantage of using the computer to deliver instruction is that it enables instructional designers to combine multiple instructional principles or components in a worked example, which may prove to enhance its efficacy. According to Mayer’s (1997) generative theory of multimedia learning, computers—in contrast to a book-based medium—also provide a more favorable environment in which to implement some forms of effective instruction, such as the coordination of the visual presentation of sequential problem states with an auditory description of each of those states. For example, in Atkinson and Derry (2000), one way to structure an example within a computer-based multimedia environment so that learning can be maximized was to create a multicomponent worked example that (a) was sequential, in that it consisted of a sequential presentation of problem states; (b) was constructed to emphasize problem subgoals (i.e., it is subgoal oriented); and (c) incorporated a second modality that is coordinated with the sequential presentation of problem states (i.e., visually presented steps coupled with verbal instructional explanations). Learners exposed to these sequential, subgoal-oriented (SE–SO) examples with dual modes outperformed learners who were exposed to more traditional, simultaneous, non-subgoal-oriented examples on conceptually based measures of problem-solving transfer. Moreover, this difference occurred despite the fact that the examples in the latter condition were also dual mode.