ALIN Open Dataset for Math Adaptive Learning

# Student Test Results Prediction based on Learning Behavior: Learning Beyond TestsDataset Part A: The Goal is to predict Test Results, in the form of averaged correctness, averaged timespent in the test, based only on the learning history (learning behavior records)Dataset Part B: The objective is to predict the last test results, points and scores, based on the learning behavior records and the first test results.# About the datasetThe raw data is provided by ALIN.ai where a large number of students participated in math learning and tests, online. Dataset_A:The feature constructed from the raw data is achieved by applying statistic functionals to the backend data sheets where learning behavior is recorded, such as 'points earned' in a 'learning session'. The final cohort we build from this learning senario consists of the predicting target: averaged correctness, and the averaged timespent, and the input features (43 dimensions). The input features consist of two parts, one contains information(5 dimensions) on the test itself, e.g. the difficulty; the other contains the rest 38 dimensions of features. Dataset_B:The dataset contains the test results including the first and the last tests, as well as the behavior learning records between the two tests.The grains of the dataset include test, sequence, topic and problem, from coarse to fine.The features extracted from the dataset are based on the sequence grain, such as the number of problems of each sequence in the first test.The target is to predict the point of each sequence and the total socre of the last test. ## github link: https://github.com/AdaptiveLearning2022/DataSetALIN2022

基于学习行为的学生测试结果预测：超越测试之学习Dataset Part A：本部分旨在预测测试结果，具体表现为平均正确率及平均测试时长，预测依据仅限于学习历史（学习行为记录）。Dataset Part B：本部分的目标是基于学习行为记录及首次测试结果，预测最后的测试成绩及得分。关于数据集：原始数据由ALIN.ai提供，其中大量学生在在线数学学习中参与测试。Dataset_A：从原始数据中构建的特征是通过应用统计函数到记录学习行为的后端数据表中获得的，例如‘学习会话’中的‘得分’。从这一学习场景中构建的最终群体包括预测目标：平均正确率、平均测试时长，以及输入特征（43个维度）。输入特征分为两部分，一部分包含关于测试本身的信息（5个维度），例如难度；另一部分包含其余38个维度的特征。Dataset_B：该数据集包含包括首次和最后一次测试在内的测试结果，以及两次测试之间的行为学习记录。数据粒度从粗到细，包括测试、序列、主题和问题。从数据集中提取的特征基于序列粒度，例如首次测试中每个序列的问题数量。预测目标是预测每个序列的得分以及最后一次测试的总分。## GitHub链接：https://github.com/AdaptiveLearning2022/DataSetALIN2022