Collaborative Research: Open Player and Community Modeling as a Learning Tool

在智能辅导系统和基于游戏的学习中，一个活跃的研究领域是个性化学习。在这些环境中，计算机实时分析学习者的行为，并建立学习关键方面的个体模型（例如，参与水平，知识获取）以适应系统。虽然这些玩家模型捕获了学习者的基本元素，但它们包含的有价值的信息通常是不透明的，并且对学习者是隐藏的。因此，适应性学习游戏的玩家不知道游戏是如何对他们进行分类的，也不知道游戏为什么会发生变化。这会影响他们的学习，尤其是在学习复杂概念的时候。为了解决这个问题，该项目将研究一种个性化学习环境的新方法，为学习者提供一个名为“开放玩家和社区模型”的工具集，使学习者能够看到自己的行动、战术和战略以及其他人的行动、战术和战略。还将创建一个带有标签工具集的回放系统，允许教师和其他学习者注释他们看到其他人使用的模式，这将阐明解决方案、错误、纠正和误解的策略和心理模型。该项目将探索学生如何通过工具学习解决问题，这些工具允许他们反思自己的实践以及其他人如何解决类似问题。该研究将研究两个问题：首先，当用户/玩家模型变得透明并用于反思学习时，人们如何参与其中？其次，设计师如何为学习者和教师创造有意义的可视化，以支持对用户/玩家模型的反思和对问题解决行为的众包注释？支持收集和可视化开放玩家和社区模型的工具将被开发并提供给公众。将对这些工具进行研究，以确定它们如何支持复杂领域的学习。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

An active research area in intelligent tutoring systems and game-based learning is personalized learning. In these environments, the computer analyzes learner behaviors in real time and builds individual models of critical aspects of learning (e.g., level of engagement, knowledge acquisition) to adapt the system. While these player models capture essential elements of a learner, the valuable information they contain is usually opaque and hidden from the learners. As a result, players of adaptive learning games do not know how the game categorizes them and why the game changes. This then affects their learning, especially when learning complex concepts. To address this, the project will investigate a novel approach to personalized learning environments by providing learners with a toolset, called Open Player and Community Model, which allows learners to see their actions, tactics, and strategies as well as those of others. A playback system with a labeling toolset will also be created to allow teachers and other learners to annotate patterns they see others use, which will shed light on strategies and mental models of solutions, errors, corrections, and misconceptions. The project will explore how students learn problem-solving with tools that allow them to reflect on their practices and on how others solved similar problems. The research will study two questions: First, how do people engage with user/player models when they are made transparent and used to reflect on learning? Second, how can designers create meaningful visualizations for learners and instructors to support reflection on user/player models and crowdsourced annotations of problem solving behavior? Tools to support collecting and visualizing open player and community models will be developed and made available to the public. Studies of the tools will be conducted to determine how they support learning in complex domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.