Southern Federal University, Rostov-on-Don, Russian Federation.
Don State Technical University, Rostov-on-Don, Russian Federation.
Behav Res Methods. 2022 Oct;54(5):2463-2478. doi: 10.3758/s13428-021-01743-x. Epub 2022 Feb 7.
Accuracy in estimating knowledge with multiple-choice quizzes largely depends on the distractor discrepancy. The order and duration of distractor views provide significant information to itemize knowledge estimates and detect cheating. To date, a precise and accurate method for segmenting time spent for a single quiz item has not been developed. This work proposes process mining tools for test-taking strategy classification by extracting informative trajectories of interaction with quiz elements. The efficiency of the method was verified in the real learning environment where the difficult knowledge test items were mixed with simple control items. The proposed method can be used for segmenting the quiz-related thinking process for detailed knowledge examination.
多选题测验中对知识的估计准确性在很大程度上取决于干扰项差异。干扰项查看的顺序和持续时间为详细列出知识估计和检测作弊提供了重要信息。迄今为止,还没有开发出一种精确和准确的方法来分割单个测验项目所花费的时间。本研究通过提取与测验元素交互的信息轨迹,提出了用于测试策略分类的过程挖掘工具。该方法在真实学习环境中得到了验证,在该环境中,困难的知识测验项目与简单的控制项目混合在一起。所提出的方法可用于分割与测验相关的思维过程,以进行详细的知识检查。