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为支持设想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的努力提供资金。网络学习探索（EXP）项目设计和建立新的学习技术，以探索其可行性，了解有效使用它们的挑战，并研究其促进学习的潜力。这个EXP项目旨在帮助学生在视觉上流利地使用视觉表示（类似于流利地使用第二语言）。教师经常使用视觉材料来帮助学生学习（例如，分数的饼图，或化学分子的球棒模型），并假设学生可以快速辨别相关信息（例如，无论两个视觉图像是否显示相同的化学物质）。但是理解并不等同于流利--学生们仍然要花费大量的脑力和时间来解释他们在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效果的流畅性，本科生和化学教授将进行一系列实验，调查他们关注哪些视觉特征，并使用复杂的统计方法来设计示例序列，以最有效地帮助学生学习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限于化学教学：鉴于STEM领域中视觉表征的普遍性以及快速处理这些视觉表征的学生数量，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提供自适应支持。该研究将联合收割机与教育心理学实验相结合，使用本科化学智能辅导系统（ITS）。在第一阶段，度量学习将评估哪些视觉特征的表征新手学生和化学专家的重点。应用度量学习新手-专家实验将建立一个技能模型的学生的看法和感性学习目标的ITS。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对感知学习任务的最佳序列进行逆向工程。一个实验将评估这些序列的有效性，该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。最后一个实验将评估是否将自适应感知学习任务与概念集中的指令增强学习。 由于教育技术传统上专注于导致概念能力的外显学习过程，因此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式学习形式。该项目还将产生下一代计算算法，以模拟人类相似性判断，并使用自适应测量更有效地收集感知判断数据。

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.