EXP: Modeling Perceptual Fluency with Visual Representations in an Intelligent Tutoring System for Undergraduate Chemistry

“网络学习和未来学习技术计划”资助的工作旨在支持展望学习技术的未来，并推进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索（EXP）项目设计和构建新的学习技术，以探索其可行性，了解有效使用它们所面临的挑战，并研究它们促进学习的潜力。这个EXP项目旨在帮助学生在视觉表现上变得流畅（类似于流利地使用第二语言）。教师经常使用视觉来帮助学生学习（例如，分数的饼状图，或化学分子的球棒模型），并假设一旦引入视觉表示，学生可以快速识别相关信息（例如，两个视觉是否显示相同的化学物质）。但理解并不等同于流利——学生们仍然要花费大量的精力和时间来解释他们从概念上理解的视觉效果，由此产生的认知负荷可能会导致他们错过教师传授的其他重要信息。为了帮助提高学生对视觉的流畅性，本科生和化学教授将进行一系列实验，调查他们关注的视觉特征，并使用复杂的统计方法设计示例序列，以最有效地帮助学生学会关注相关的视觉特征。基于这项研究，项目团队将开发一种视觉流畅性培训，并将其纳入现有的、成功的化学在线学习技术中。潜在的教育影响将不仅仅局限于化学教学：考虑到STEM领域中视觉表现的普遍存在，以及那些难以快速处理这些视觉效果的学生的数量，这项研究的产品可以整合到其他教育技术中。pi将开发一种感知学习过程的认知建模方法，可以为感知学习任务创建自适应支持。该研究将使用智能辅导系统（ITS）将机器学习与教育心理学实验结合起来，用于本科化学。在第一阶段，度量学习将评估表征的哪些视觉特征是初学者和化学专家关注的。将度量学习应用于新手-专家实验将建立学生感知和ITS感知学习目标的技能模型。在第二阶段，该团队将使用机器学习来开发感知学习的认知模型。该团队将进行化学学习实验，并应用机器学习来测试认知模型。在第三阶段，团队将使用认知模型来逆向工程感知学习任务的最佳序列。一个实验将评估这些序列的有效性，团队将在此分析的基础上创建一个自适应版本的感知学习任务。最后的实验将评估将适应性知觉学习任务与概念聚焦教学相结合是否能提高学习效果。由于教育技术传统上侧重于导致概念能力的外显学习过程，它们目前无法评估导致感知流畅性的内隐学习过程。结合教育心理学、认知科学和机器学习将产生新的认知模型，这些模型可以改变教育技术的适应能力，以支持这种感知流畅性以及其他隐性学习形式。该项目还将产生下一代计算算法来模拟人类相似性判断，并使用自适应测量来更有效地收集感知判断的数据。

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects design and build new kinds of learning technologies in order to explore their viability, to understand the challenges to using them effectively, and to study their potential for fostering learning. This EXP project aims to help students become visually fluent with visual representations (similar to becoming fluent in a second language). Instructors often use visuals to help students learn (e.g., pie charts of fractions, or ball-and-stick models of chemical molecules) and assume that students can quickly discern relevant information (e.g., whether or not two visuals show the same chemical) once that visual representation has been introduced. But comprehension is not the same as fluency -- students still expend significant mental effort and time interpreting even visuals that they understand conceptually, and the resulting cognitive load can cause them to miss other important information that instructors are imparting. To help improve student fluency with visuals, a series of experiments with undergraduate students and chemistry professors will investigate which visual features they pay attention to and use sophisticated statistical methods to devise example sequences that will most efficiently help students learn to pay attention to relevant visual features. Based on this research, the project team will develop a visual fluency training that will be incorporated into an existing, successful online learning technology for chemistry. The potential educational impact will not be limited to chemistry instruction: given the pervasiveness of visual representations in STEM fields and the number of students who struggle with rapid processing of those visuals, the products of this research could be integrated into other educational technologies.The PIs will develop a methodology for cognitive modeling of perceptual learning processes that can create adaptive support for perceptual learning tasks. The research will combine machine learning with educational psychology experiments using an Intelligent Tutoring System (ITS) for undergraduate chemistry. In Phase 1, metric learning will assess which visual features of representations novice students and chemistry experts focus on. Applying metric learning to a novice-expert experiment will establish a skill model of student perceptions and perceptual learning goals for the ITS. In Phase 2, the team will use machine learning to develop a cognitive model of perceptual learning. The team will conduct a chemistry learning experiment and apply machine learning to test cognitive models. In Phase 3, the team will use the cognitive model to reverse-engineer optimal sequences of perceptual learning tasks. An experiment will evaluate the effectiveness of these sequences, and the team will build on this analysis to create an adaptive version of perceptual learning tasks. A final experiment will evaluate whether incorporating adaptive perceptual learning tasks with conceptually focused instruction enhances learning. Because educational technologies have traditionally focused on explicit learning processes that lead to conceptual competencies, they cannot currently assess the implicit learning processes that lead to perceptual fluency. Combining educational psychology, cognitive science, and machine learning will yield new cognitive models that could transform the adaptive capabilities of educational technologies to support such perceptual fluency as well as other implicit forms of learning. The project will also yield next-generation computational algorithms to model human similarity judgments and to use adaptive surveying to collect data on perceptual judgments more efficiently.