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Applying Probabilistic Models for Knowledge Diagnosis and Educational Game Design.

机译：应用概率模型进行知识诊断和教育游戏设计。

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摘要

Computer-based learning environments offer the potential for innovative assessments of student knowledge and personalized instruction for learners. However, there are a number of challenges to realizing this potential. Many psychological models are not specific enough to directly deploy in instructional systems, and computational challenges can arise when considering the implications of a particular theory of learning. While learners' interactions with virtual environments encode significant information about their understanding, existing statistical tools are insufficient for interpreting these interactions. This research develops computational models of teaching and learning and combines these models with machine learning algorithms to interpret learners' actions and customize instruction based on these interpretations. This approach results in frameworks that can be adapted to a variety of educational domains, with the frameworks clearly separating components that can be shared across tasks and components that are customized based on the educational content. Using this approach, this dissertation addresses three major questions: (1) How can one diagnose learners' knowledge from their behavior in games and virtual laboratories? (2) How can one predict whether a game will be diagnostic of learners' knowledge? and (3) How can one customize instruction in a computer-based tutor based on a model of learning in a domain?;The first question involves automatically assessing student knowledge via observed behavior in complex interactive environments, such as virtual laboratories and games. These environments require students to plan their behavior and take multiple actions to achieve their goals. Unlike in many traditional assessments, students' actions in these environments are not independent given their knowledge and each individual action cannot be classified as correct or incorrect. To address this issue, I develop a Bayesian inverse planning framework for inferring learners' knowledge from observing their actions. The framework is a variation of inverse reinforcement learning and uses Markov decision processes to model how people choose actions given their knowledge. Through behavioral experiments, I show that this framework can infer learners' stated beliefs, with accuracy similar to human observers, and that feedback based on the framework improves learning efficiency. To extend this framework to educational applications outside of the laboratory, I extended the inverse planning framework to diagnose students' algebra skills from worked solutions to linear equations, separating different sources of mathematical errors. I tested the framework by developing an online algebra tutor that provides students with the opportunity to practice solving equations and automatically diagnoses their understanding after they have solved sufficient equations. Preliminary experiments demonstrate that Bayesian inverse planning provides a good fit for the majority of participants' behaviors, and that its diagnoses are consistent with results of a more conventional assessment.;The results of the previous studies showed that not all tasks result in learner behavior that can be used to perfectly diagnose knowledge. In many cases, actions may be ambiguous, resulting in a diagnosis that places some probability on one possible knowledge state and some probability on another. I developed an optimal game design framework to predict how much information will be gained by observing a player or players' actions if they were to play a particular game: gaining more information from a game means that the diagnosis is less ambiguous. This framework extends optimal experiment design methods in statistics. It can limit the trial and error necessary to create games for education and behavioral research by suggesting game design choices while still leveraging the skills of a human designer to create the initial design. Behavioral results from a concept learning game demonstrate that the predicted information gain is correlated with the actual information gain and that the best designs can result in twice as much information as an uninformed design.;The final part of this dissertation considers how to personalize instruction in a computer tutor, relying on knowledge about the domain and an estimate of the students' knowledge. This builds on the idea of assessing learners' knowledge from their actions and considers more broadly how to sequence assessment and personalized instruction. In a computer-based tutor, there may be a cost to time spent on assessment, as the time could alternatively have been spent allowing the learner to work through new material; however, this time spent on assessment may also be beneficial by providing information to allow the computer to choose material more effectively. I show that partially observable Markov decision processes can be used to model the tutoring process and decide what pedagogical action to choose based on a model of the domain and the learner. The resulting automated instructional policies result in faster learning of numeric concepts than baseline policies.;My research demonstrates that applying a computational modeling approach to a diverse set of problems in computer-assisted learning results in new machine learning algorithms for interpreting and responding to complex behavioral data. The frameworks developed in this research provide a systematic and scalable way to create personalized responses to learners. These frameworks show the potential of interactive educational technologies to not only provide content to learners but to infer their understanding from innovative assessments and provide personalized guidance and instruction.
机译：基于计算机的学习环境为学生知识的创新评估和对学习者的个性化指导提供了潜力。但是，要实现这一潜力，存在许多挑战。许多心理模型不够具体，无法直接部署在教学系统中，考虑到特定学习理论的含义时，可能会出现计算难题。虽然学习者与虚拟环境的交互会编码有关其理解的重要信息，但现有的统计工具不足以解释这些交互。这项研究开发了教学和学习的计算模型，并将这些模型与机器学习算法结合起来，以解释学习者的行为并基于这些解释定制指令。这种方法产生了可以适应各种教育领域的框架，框架清楚地分隔了可以在任务之间共享的组件以及根据教育内容定制的组件。本文采用这种方法解决了三个主要问题：（1）如何通过游戏和虚拟实验室中的行为来诊断学习者的知识？ （2）如何预测游戏是否可以诊断学习者的知识？ （3）如何基于领域学习模型在基于计算机的辅导员中自定义指令？；第一个问题涉及在虚拟实验室和游戏等复杂的交互环境中，通过观察到的行为自动评估学生的知识。这些环境要求学生计划自己的行为，并采取多种行动来实现自己的目标。与许多传统评估不同，学生在这些环境中的行为由于他们的知识而不是独立的，并且每个单独的行为都不能归类为正确或错误。为了解决这个问题，我开发了一种贝叶斯逆向计划框架，用于通过观察学习者的行为来推断他们的知识。该框架是反向强化学习的一种变体，并使用马尔可夫决策过程来建模人们如何在已知知识的情况下选择行动。通过行为实验，我证明了该框架可以推断学习者的陈述信念，其准确性与人类观察者相似，并且基于该框架的反馈可以提高学习效率。为了将该框架扩展到实验室之外的教育应用，我扩展了逆向计划框架，以诊断学生的代数技能，从工作解决方案到线性方程式，从而分离出不同的数学错误源。我通过开发在线代数导师来测试该框架，该导师为学生提供了练习求解方程式的机会，并在他们解决了足够的方程式后自动诊断他们的理解。初步实验表明，贝叶斯逆向计划可以很好地适应大多数参与者的行为，并且其诊断与更常规评估的结果一致;先前的研究结果表明，并非所有任务都会导致学习者行为可以用来完美地诊断知识。在许多情况下，动作可能是模棱两可的，导致进行诊断，将某种概率置于一种可能的知识状态上，而另一种概率置于另一种可能的知识状态上。我开发了一个最佳的游戏设计框架，以预测观察一个或多个玩家在玩特定游戏时的行为将获得多少信息：从游戏中获取更多信息意味着诊断的模棱两可。该框架扩展了统计领域的最佳实验设计方法。通过建议游戏设计选择，同时仍然利用人类设计师的技能来创建初始设计，它可以限制为教育和行为研究而创建游戏所需的反复试验。一个概念学习游戏的行为结果表明，预测的信息增益与实际的信息增益相关，并且最佳设计可以产生的信息量是未获知情的设计的两倍。计算机导师，依靠有关领域的知识和对学生知识的估计。这基于从学习者的行为评估他们的知识的想法，并且更广泛地考虑了如何对评估和个性化教学进行排序。在基于计算机的导师中，评估所花费的时间可能会有所成本，因为可以选择将时间花费在允许学习者学习新材料上。然而，花费在评估上的时间也可以通过提供信息来使计算机更有效地选择材料而受益。我展示了部分可观察的马尔可夫决策过程可用于对辅导过程进行建模，并根据领域和学习者的模型来决定选择哪种教学行动。由此产生的自动化教学策略比基本策略能更快地学习数字概念。；我的研究表明，将计算建模方法应用于计算机辅助学习中的各种问题，会产生用于解释和响应复杂行为的新型机器学习算法。数据。在这项研究中开发的框架提供了系统的，可扩展的方式来创建针对学习者的个性化响应。这些框架显示了交互式教育技术的潜力，不仅可以向学习者提供内容，而且可以通过创新性评估来推断他们的理解，并提供个性化的指导和指导。

著录项

  • 作者

    Rafferty, Anna Noonan.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Computer science.;Cognitive psychology.;Educational technology.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 143 p.
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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