ASSISTments学生在线学习数据集

ASSISTments学生在线学习数据集主要面向自适应学习关键技术研究，尤其针对"跨媒体自适应智能推荐技术"任务需求，主要记录了大规模的公开学生在线答题及行为数据。该数据集支撑了论文《BETA-CD: A Bayesian Meta-learned Cognitive Diagnosis Framework for Personalized Learning》以及专利《一种学生答题预测方法、系统、设备及存储介质》、《作答模拟和试题推荐方法、装置、电子设备和存储介质》。本数据集未来可应用于多维度教育研究，支持知识追踪模型、习题难度预测等算法的开发验证，为自适应学习系统提供行为分析依据，为跨媒体知识表示、时序行为建模等前沿方向提供实验基础。本数据集的价值体现在三个维度：其一，为构建真实场景下的学习行为模型提供保障；其二，细粒度的知识点关联体系有助于揭示知识掌握规律与教学策略的关联机制；其三，降低教育数据科学的研究门槛，可加速自适应学习技术的产学研转化。该数据集的发布预期将推动教育智能化领域的算法创新，促进教育技术与认知科学的跨学科融合，为构建下一代智能教育平台提供关键数据支撑。

The ASSISTments Student Online Learning Dataset is primarily developed for research on key adaptive learning technologies, with a specific focus on the task requirements of "cross-media adaptive intelligent recommendation technology". It primarily collects large-scale publicly available online student answer submission and behavioral data. This dataset has been utilized in the paper *BETA-CD: A Bayesian Meta-learned Cognitive Diagnosis Framework for Personalized Learning* as well as two Chinese patents: *A Student Answer Prediction Method, System, Device and Storage Medium* and *Answer Simulation and Question Recommendation Method, Device, Electronic Equipment and Storage Medium*. This dataset holds potential for application in multi-dimensional educational research, supporting the development and validation of algorithms such as knowledge tracing models and exercise difficulty prediction. It provides behavioral analysis foundations for adaptive learning systems, and experimental bases for cutting-edge research directions including cross-media knowledge representation and temporal behavior modeling. The value of this dataset is reflected in three core dimensions: First, it offers support for constructing learning behavior models in real-world scenarios; Second, its fine-grained knowledge point association system helps reveal the correlation mechanism between knowledge mastery patterns and teaching strategies; Third, it lowers the research threshold of educational data science and accelerates the industry-university-research transformation of adaptive learning technologies. The release of this dataset is expected to drive algorithmic innovation in the field of educational intelligence, promote interdisciplinary integration of educational technology and cognitive science, and provide critical data support for the construction of next-generation intelligent education platforms.

搜集汇总

背景与挑战

背景概述

ASSISTments学生在线学习数据集是一个面向自适应学习关键技术研究的大规模公开数据集，主要记录学生在线答题及行为数据，支持知识追踪、习题难度预测等算法开发。其核心价值在于构建真实学习行为模型、揭示知识点关联机制以及降低教育数据科学研究门槛，旨在推动教育智能化与跨学科融合。