Data underlying the BSc thesis: The influence of assessment type on students' learning gain in k-means clustering

This study aimed to investigate the influence of assessment types on students' learning gain in k-means clustering. The learning gain was calculated using the difference between the pre- and post-test scores, where 7 was the highest possible score on the tests (normalised gain, adapted from Hake's formula). Data was collected from 20 participants, the complete dataset includes the individual learning gain of participants.

本研究旨在探究评估类型对K均值聚类（k-means clustering）场景下学生学习增益的影响。学习增益通过前测与后测成绩的差值计算得出，测试满分设定为7分，该标准化增益得分改编自Hake公式。本研究共采集20名参与者的数据，完整数据集包含所有参与者的个体学习增益数据。