按科目统计作业分析数据

通过对考核对象作业完成情况的学科维度分析，构建涵盖科目学段、作业提交率、错题订正完成率、知识点错误分布、作业耗时等多维度指标体系。该数据产品可精准定位不同科目学段教学中的薄弱环节，例如数学数与代数类题目错误率高于平均值时，自动触发教学改进预警；单题耗时时长异常时，提示教学效率优化需求。创新性在于融合学科特征构建动态分析模型，支持教育机构实现精准教学优化、学情监测、个性化学习支持。1、数据采集：采集了共同申请人的智慧教育平台作业管理系统中的原始数据，包含考核科目、科目学段、 作业提交数、作业布置数、作业提交率、完成题数、每份作业耗时(秒)、错题订正数、错题数等字段，建立底层数据库。2、数据计算：首先对敏感信息进行加密处理，对数据进行加工、脱敏、筛选、统计、分析。单题耗时（秒）=每份作业耗时(秒)/完成题数。3、学做作业效率分析运用ABCD分类法，对单题耗时≤8的，给予“A”效率分级；单题耗时>8且≤15的，给予“B”效率分级；单题耗时>15且≤25的，给予“C”效率分级；单题耗时>35D ，给予“D”效率分级。效率分析从A-D依次降低，A级为最高做题效率。

Through the disciplinary dimension analysis of the assignment completion status of assessment subjects, a multi-dimensional indicator system covering subject and grade level, assignment submission rate, error correction completion rate, distribution of knowledge point errors, and assignment time consumption is constructed. This data product can accurately identify weak links in teaching across different subjects and grade levels. For example, when the error rate of math number and algebra questions exceeds the average value, it will automatically trigger a teaching improvement warning; when the per-question time consumption is abnormal, it will prompt the demand for teaching efficiency optimization. Its innovation lies in constructing a dynamic analysis model by integrating disciplinary characteristics, enabling educational institutions to achieve precise teaching optimization, learning situation monitoring, and personalized learning support.
1. Data Collection: Raw data was collected from the assignment management system of the co-applicant's smart education platform, including assessment subjects, subject and grade level, number of assigned assignments, number of submitted assignments, assignment submission rate, number of completed questions, time consumption per assignment (seconds), number of corrected errors, and number of wrong questions. An underlying database is established.
2. Data Calculation: First, sensitive information is encrypted, and the data is processed, desensitized, screened, statistically analyzed. The per-question time consumption (seconds) = time consumption per assignment (seconds) / number of completed questions.
3. Homework Efficiency Analysis: The ABCD classification method is applied for efficiency grading: grade "A" is assigned to questions with per-question time consumption ≤ 8 seconds; grade "B" to those with per-question time consumption > 8 and ≤ 15 seconds; grade "C" to those with per-question time consumption >15 and ≤25 seconds; grade "D" to those with per-question time consumption >35 seconds. Efficiency levels decrease sequentially from A to D, with grade "A" representing the highest problem-solving efficiency.

搜集汇总

背景与挑战

背景概述

该数据集为教育行业的企业数据，包含1035条记录，每周更新，用于分析学生作业完成情况，支持精准教学优化和学情监测。