地球资源数据云——数据资源详情
学生成绩预测数据集
该数据集《Student Grades Prediction Dataset》主要用于二分类任务，数据形态以时序/信号为主，应用场景偏向环保分类。 题目说明：collection of 160 instances belonging to two classes 任务类型：时序/信号二分类。 建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 The dataset provides a collection of 160 instances belonging to two classes (pass' = 136 and fail' = 24). The data is an anonymised, statistically sound and reliable representation of the original data collected from students studying computer science modules at a UK University. Each instance is made up of 19 features plus the class label. Eight of the features represent students' online behaviour including bio information retrieved from Virtual Learning Environment. Eleven of the features represent students' neighbourhood influence retrieved from Office for Students database. The data has been compiled and made available in de - facto/de - jure standard open formats (CSV and JSON). This data was collected and used in a research study undertaken by academics and researchers at Computer Science Department, Edge Hill University, United Kingdom. To encourage reproducibility of the experiments and results reported, the data is provided in the exact training - validation - testing splits used in the experiments. Nnamoko, Nonso; Barrowclough, Joe (2022), “Student grade prediction dataset”, Mendeley Data, V1, doi: 10.17632/wf8568hxb7.1
摘要概览
该数据集《Student Grades Prediction Dataset》主要用于二分类任务，数据形态以时序/信号为主，应用场景偏向环保分类。 题目说明：collection of 160 instances belonging to two classes
任务类型：时序/信号二分类。
建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。
The dataset provides a collection of 160 instances belonging to two classes (pass' = 136 and fail' = 24). The data is an anonymised, statistically sound and reliable representation of the original data collected from students studying computer science modules at a UK University. Each instance is made up of 19 features plus the class label.
Eight of the features represent students' online behaviour including bio information retrieved from Virtual Learning Environment. Eleven of the features represent students' neighbourhood influence retrieved from Office for Students database. The data has been compiled and made available in de - facto/de - jure standard open formats (CSV and JSON).
This data was collected and used in a research study undertaken by academics and researchers at Computer Science Department, Edge Hill University, United Kingdom. To encourage reproducibility of the experiments and results reported, the data is provided in the exact training - validation - testing splits used in the experiments.
Nnamoko, Nonso; Barrowclough, Joe (2022), “Student grade prediction dataset”, Mendeley Data, V1, doi: 10.17632/wf8568hxb7.1