Title: DK-PRACTICE: An Intelligent Educational Platform for Personalized Learning Content Recommendations Based on Students Knowledge State Show Chinese title

Title: DK实践：基于学生知识状态的个性化学习内容推荐智能教育平台

Abstract: This study introduces DK-PRACTICE (Dynamic Knowledge Prediction and Educational Content Recommendation System), an intelligent online platform that leverages machine learning to provide personalized learning recommendations based on student knowledge state. Students participate in a short, adaptive assessment using the question-and-answer method regarding key concepts in a specific knowledge domain. The system dynamically selects the next question for each student based on the correctness and accuracy of their previous answers. After the test is completed, DK-PRACTICE analyzes students' interaction history to recommend learning materials to empower the student's knowledge state in identified knowledge gaps. Both question selection and learning material recommendations are based on machine learning models trained using anonymized data from a real learning environment. To provide self-assessment and monitor learning progress, DK-PRACTICE allows students to take two tests: one pre-teaching and one post-teaching. After each test, a report is generated with detailed results. In addition, the platform offers functions to visualize learning progress based on recorded test statistics. DK-PRACTICE promotes adaptive and personalized learning by empowering students with self-assessment capabilities and providing instructors with valuable information about students' knowledge levels. DK-PRACTICE can be extended to various educational environments and knowledge domains, provided the necessary data is available according to the educational topics. A subsequent paper will present the methodology for the experimental application and evaluation of the platform. Show Chinese abstract

Abstract: 本研究介绍了DK-PRACTICE（动态知识预测与教育内容推荐系统），这是一个智能在线平台，利用机器学习根据学生知识状态提供个性化的学习建议。 学生参与一个针对特定知识领域关键概念的简短、自适应评估，采用问答方法。 系统根据学生之前回答的正确性和准确性，动态选择每个学生的下一个问题。 测试完成后，DK-PRACTICE分析学生的历史互动，以推荐学习材料，从而增强学生在已识别知识缺口中的知识状态。 问题选择和学习材料推荐均基于使用真实学习环境中匿名数据训练的机器学习模型。 为了提供自我评估和监控学习进度，DK-PRACTICE允许学生参加两次测试：一次是课前测试，另一次是课后测试。 每次测试后，都会生成包含详细结果的报告。 此外，该平台还提供基于记录的测试统计数据可视化学习进度的功能。 DK-PRACTICE通过赋予学生自我评估能力，并为教师提供有关学生知识水平的有价值信息，促进适应性与个性化学习。 DK-PRACTICE可根据教育主题提供的必要数据，扩展到各种教育环境和知识领域。 后续论文将介绍该平台的实验应用和评估方法。