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Please note this is a comparison between Version 2 by Jessie Wu and Version 1 by SHUANG LIANG.
在教育领域，认知诊断对于实现个性化学习至关重要。广泛采用的In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA DINA（确定性输入、噪声和门）模型揭示了学生对正确回答问题所需的基本技能的掌握程度。然而，现有的基于 DINA 的方法忽视了知识点之间的依赖性，并且其模型训练过程对于大型数据集来说计算效率低下。 (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets.
- cognitive diagnosis
- DINA model
- bayesian networks
一、简介1. Introduction
在线教育行业的出现彻底改变了传统的教育方式。在线教育利用信息技术，为学生提供海量课程和学习资料的便捷获取，促进资源共享，保障教育公平。然而，随着可用学习资源的指数级增长，准确评估学生对特定技能和知识的掌握水平已成为一项紧迫的挑战。认知诊断模型The emergence of the online education industry has revolutionized traditional educational approaches. Leveraging information technology, online education offers students convenient access to a vast array of courses and learning materials, thus promoting resource sharing and ensuring educational equity. However, with the exponential growth of available learning resources, accurately assessing a student’s mastery level of specific skills and knowledge has become a pressing challenge. Cognitive diagnosis models (CDM) 最初由s), initially introduced by [1], 1have been developed to quantify the latent abilities that significantly impact students’ ]performance. 引入，旨在量化显着影响学生表现的潜在能力。CDMCDMs [2,3] have gained widespread recognition and interest from 2both academic and industry domains by providing insights into ,the 3 ] 通过提供对学生总分基础的认知技能的见解 [ 4cognitive ]skills ，获得了学术界和行业领域的广泛认可和兴趣。
DINA（确定性输入、噪声和门）模型[underlying 5students’ ]被认为是一种著名的认知诊断方法，它有效地整合了Q矩阵和学生的反应模式，以评估他们的掌握水平并识别每个知识点内潜在的错误模式。通过采用最大似然估计[overall 6scores [4]等统计技术，DINA模型为教育工作者提供了全面的认知诊断工具，从而能够制定个性化的教学策略，以满足个人在不同知识点上的表现。.
然而，The DINA模型在实际应用中面临着各种挑战。首先，它采用离散变量来表示学生是否掌握了某个知识点，1代表掌握，0代表未掌握。这种方法在需要持续评估的学科领域会出现问题，例如绘画和音乐等领域的艺术评估。其次， (Deterministic INA模型无法解释问题内知识点之间的相互关联性，这限制了其捕捉学生综合认知技能的有效性。第三，由于使用大型数据集，传统nputs, Noisy And gate) model [5] is recognized as a prominent cognitive diagnosis approach, which effectively integrates the Q-matrix and students’ response patterns to assess their mastery level and identify potential error patterns within each knowledge point. By employing statistical techniques such as maximum likelihood estimation [6], the DINA 模型的训练在计算上变得昂贵，这些数据集可能包含来自在线教育平台的数百万学生记录和问题项目。model equips educators with a comprehensive cognitive diagnostic tool, enabling the formulation of personalized teaching strategies that cater to individuals’ performance across diverse knowledge points.
2. 认知诊断的不同模型Different Models of Cognitive Diagnosis
认知诊断最初由教育心理学家提出，用于心理测量，其根源可追溯到20世纪Cognitive diagnosis, initially proposed by educational psychologists for psychological measurement, has its roots in the 1990年代。弗雷德里克森等人。s. Frederiksen et al. [7] 7were credited with formally introducing the theories and concepts related to cognitive ]diagnosis 被认为于 1993 年正式引入了认知诊断相关的理论和概念，而in 1993, while Nichols 等人。et al. [8] 8further provided a comprehensive summary and categorization of these theories and concepts ]in 1995年进一步对这些理论和概念进行了全面的总结和分类。. Leighton et al. [9] 9considered CDM as a promising evaluation model that can delve into the underlying structure of a field and identify problems and areas that need improvement ]in 2007年，. Lee et al. [10] proposed that tests informed by the Cognitive DM被认为是一种很有前途的评估模型，可以深入研究一个领域的底层结构，并找出问题和需要改进的领域。[iagnosis 10Algorithm ]提出，由认知诊断算法（(CDA）提供的测试可以指定总体测试分数背后的底层知识结构，并且该指定可以作为反馈，通过补救指导和改进指导来满足个人和群体的需求，以增强学习和能力。 2009 年的能力。作为评估的诊断方法，CDA 需要统计和数学模型来实施假设。CDM 是心理测量模型，利用项目反应模式来确定考生的认知能力) can specify the underlying knowledge structure behind the overall test score, and this specification can serve as feedback to meet individual and group needs through remedial instruction and improve instruction to enhance learning and competency in 2009. As a diagnostic approach to assessment, CDA needs statistical and mathematical models to operationalize the assumptions. CDMs are psychometric models that make use of an item response pattern in order to determine test-takers’ cognitive abilities [ 11]. ]。在所有CDA研究中，统计模型的选择是关键的一步，需要密切关注和考虑模型选择标准。然而，在大多数CDA研究中，应用预定CDM的测试是根据模型的特点和实用性问题来选择的。所以李等人。In all CDA studies, the selection of statistical models is a critical step and requires close attention and consideration of model selection criteria. However, in most CDA studies, tests applying a predetermined CDM are chosen based on the characteristics of the model and the practicality issue. So Li et al. [ 12] carefully studied the considerations ]仔细研究了阅读理解测试的CDM选择所需的考虑因素，发现当认知技能之间的关系不完全清晰时，使用饱和的（更复杂的）CDM是安全的，它可以灵活地适应不同类型的CDM。 2016 年技能之间的关系。目前，认知诊断可以有广义和狭义两种定义。从广义上讲，认知诊断利用基于计算机的测试和统计方法等现代技术来评估用户的认知能力和结构required for CDM selection for reading comprehension tests and found that when the relationship between cognitive skills is not completely clear, it is safe to use a saturated (more complex) CDM, which can flexibly adapt to different types of relationships between skills in 2016. Currently, cognitive diagnosis can be defined in both broad and narrow terms. Broadly speaking, cognitive diagnosis leverages modern technologies such as computer-based testing and statistical methods to assess users’ cognitive abilities and structures [ 13,14]. On the other hand, 14in a narrower sense, cognitive diagnosis classifies users based on their mastery level of specific knowledge points, with the classification results used for personalized educational interventions.
The application of cognitive diagnosis in the education industry has led to a shift towards personalized education in traditional online classrooms ]。另一方面，从狭义上讲，认知诊断根据用户对特定知识点的掌握程度进行分类，并将分类结果用于个性化的教育干预。
认知诊断在教育行业的应用导致了传统在线课堂向个性化教育的转变[ 15,16]. Cognitive diagnosis models can be differentiated from two perspectives. Firstly, 16they can be classified as continuous diagnosis models or discrete diagnosis models, depending on their ability to diagnose continuous scores. Secondly, cognitive diagnosis models can be classified based on their approach to handling dimensions of students’ cognitive abilities. This categorization results in one-dimensional skill diagnosis models and multidimensional skill diagnosis models. Currently, there are more than ]。认知诊断模型可以从两个角度来区分。首先，根据其诊断连续分数的能力，它们可以分为连续诊断模型或离散诊断模型。其次，认知诊断模型可以根据其处理学生认知能力维度的方法进行分类。这种分类产生一维技能诊断模型和多维技能诊断模型。目前，已有60多种认知诊断模型可用。这些模型包括基于规则的模型、属性层次模型、确定性输入、噪声与门（ cognitive diagnosis models available. These models include the rule-based model, attribute hierarchy model, Deterministic Inputs, Noisy And gate (DINA）模型以及各种变体) model, as well as various variations [ 17、18,18] such as the Fuzzy ]，例如模糊CDF模型 model [19]. Improved versions of 19the ]。DINA 模型的改进版本，例如model, such as the HO-DINA [ 20], ]、P-DINA [ 21], ]、G-DINA [ 22], ] 和增量and Incremental DINA (I-DINA) 模型model [ 23], ]are also among the existing models used in cognitive diagnosis research.
The history of Bayesian networks dates back 也属于现有模型用于认知诊断研究。
贝叶斯网络的历史可以追溯到to 20the 世纪 80 年代初。1988 年，early 1980s. In 1988, Pearl 等人。et al. [ 24] ]在其开创性论文中首次介绍了贝叶斯网络的基本概念和推理方法。值得注意的是，Pearl 的工作还引入了“因果图”的概念，它扩展了概率图模型以纳入因果关系，从而为进一步发展奠定了基础。在 20 世纪 90 年代，该领域的研究从表示问题扩展到了推理和学习first introduced the fundamental concepts and inference methods of Bayesian networks in their seminal paper. Notably, Pearl’s work also introduced the concept of the “causal graph”, which expanded probabilistic graph models to incorporate causal relationships, thereby establishing the groundwork for further development. In the 1990s, research in the field expanded from representation issues to encompass inference and learning [ 25], making Bayesian networks more practical in various applications. With the advancement of computational power ]，使得贝叶斯网络在各种应用中更加实用。随着21世纪计算能力的进步和数据的指数级增长，贝叶斯网络在医学and the exponential growth of data in the 21st century, Bayesian networks have found widespread application in diverse domains such as medicine [ 26], ]、金融finance [ 27], and ]和自然语言处理natural language processing [ 28]. ]等不同领域得到了广泛的应用。该领域的研究在推理、学习和贝叶斯网络的应用方面也取得了重大进展Research in the field has also made significant progress in reasoning, learning, and the application of Bayesian networks [ 29].
In recent ]。
在最近的研究中，贝叶斯网络和research, the integration of Bayesian networks and the DINA 模型的集成引起了人们的关注，在学生建模、知识追踪和技能拓扑方面有着显着的应用。例如，科纳蒂等人。model has gained attention, with notable applications in student modeling, knowledge tracing, and skill topology. For instance, Conati et al. [ 30] ]将贝叶斯网络应用于Andes项目applied Bayesian networks to the Andes project [ 31], an intelligent educational system focused on Newtonian physics, to model uncertainty within students’ reasoning ]，这是一个专注于牛顿物理学的智能教育系统，用于模拟学生推理和学习过程中的不确定性。在知识追踪领域and learning processes. In the domain of knowledge tracing [ 32], ]，Pelánek [ 33] ]引入了贝叶斯知识追踪（BKT），它利用贝叶斯网络来推断知识追踪模型中的潜在学生变量。此外，introduced Bayesian Knowledge Tracing (BKT), which employed Bayesian networks to infer latent student variables within knowledge-tracing models. Furthermore, Käser 等人。et al. [ 32] ]利用动态贝叶斯网络（DBN）对知识跟踪中的技能拓扑进行建模。虽然这些工作对贝叶斯网络的应用做出了重大贡献，但它们的主要重点在于学生建模、知识追踪和技能拓扑。与此同时，以utilized dynamic Bayesian networks (DBN) to model skill topology in knowledge tracking. While these works have made significant contributions to the application of Bayesian networks, their main focus lies in student modeling, knowledge tracing, and skill topology. In parallel, recent breakthroughs in asynchronous federated meta-learning, exemplified by AFMeta 为代表的异步联邦元学习的最新突破有效解决了落后和过度拟合等问题，从而显着提高了模型性能并显着减少了学习时间, have effectively addressed issues such as straggler and over-fitting, resulting in a substantial improvement in model performance and a notable reduction in learning time [3434]. In the field of education-based information analysis, the examination of student learning assessment methods based on text data has emerged as a crucial research area. Liu et al. ]。在基于教育的信息分析领域，基于文本数据的学生学习评估方法的检验已成为一个重要的研究领域。刘等人。[ 35] ]引入了一种基于实时文本数据属性的创新学习评估方法，克服了传统评估方法的局限性。结果凸显了利用实时属性文本数据在衡量学生学习成果方面的卓越有效性。
introduced an innovative learning evaluation method based on real-time text data attributes, overcoming the limitations of traditional evaluation methods. The outcomes highlight the superior effectiveness of utilizing real-time attribute text data in measuring students’ learning outcomes.
