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

协作研究：开放玩家和社区建模作为学习工具

基本信息

批准号：
1917982
负责人：
Magy Seif ElNasr
金额：
$ 48.59万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2021-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917982&HistoricalAwards=false
关键词：
Collaborative Research Open Player Community

项目摘要

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.

个性化学习是智能教学系统和基于游戏的学习中的一个活跃的研究领域。在这些环境中，计算机在真实的时间内分析学习者的行为，并建立学习的关键方面的个体模型（例如，参与程度、知识获取）以适应系统。虽然这些玩家模型捕捉到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，对学习者来说是隐藏的。因此，自适应学习游戏的玩家不知道游戏如何对他们进行分类，也不知道游戏为什么会改变。这会影响他们的学习，特别是在学习复杂概念时。为了解决这个问题，该项目将研究一种新的方法来个性化的学习环境，通过为学习者提供一个工具集，称为开放式玩家和社区模型，它允许学习者看到他们的行动，战术和策略以及其他人。还将创建一个带有标签工具集的回放系统，以允许教师和其他学习者注释他们看到其他人使用的模式，这将揭示解决方案，错误，纠正和误解的策略和心理模型。该项目将探讨学生如何学习解决问题的工具，让他们反思自己的做法，以及其他人如何解决类似的问题。该研究将研究两个问题：第一，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建有意义的可视化，以支持对用户/玩家模型和问题解决行为的众包注释的反思？将开发支持收集和可视化开放玩家和社区模型的工具，并向公众提供。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。