Content assessment has broadly improved in e-learning scenarios in recent
decades. However, the eLearning process can give rise to a spatial and temporal
gap that poses interesting challenges for assessment of not only content, but
also students' acquisition of core skills such as self-regulated learning. Our
objective was to discover students' self-regulated learning processes during an
eLearning course by using Process Mining Techniques. We applied a new algorithm
in the educational domain called Inductive Miner over the interaction traces
from 101 university students in a course given over one semester on the Moodle
2.0 platform. Data was extracted from the platform's event logs with 21629
traces in order to discover students' self-regulation models that contribute to
improving the instructional process. The Inductive Miner algorithm discovered
optimal models in terms of fitness for both Pass and Fail students in this
dataset, as well as models at a certain level of granularity that can be
interpreted in educational terms, which are the most important achievement in
model discovery. We can conclude that although students who passed did not
follow the instructors' suggestions exactly, they did follow the logic of a
successful self-regulated learning process as opposed to their failing
classmates. The Process Mining models also allow us to examine which specific
actions the students performed, and it was particularly interesting to see a
high presence of actions related to forum-supported collaborative learning in
the Pass group and an absence of those in the Fail group.

通过对 101 名大学生使用 Moodle 2.0 平台进行自主学习课程的交互记录进行数据分析，使用归纳挖掘算法发现了学生成绩良好和学生成绩较差的自主学习过程模型，并发现良好成绩组有较多与论坛协作学习相关的行为动作，而成绩较差组则相反。

自主学习评估中的过程挖掘在电子学习中的应用

Process mining for self-regulated learning assessment in e-learning

We introduce a high-quality dataset that contains 3,397 samples comprising
(i) multiple choice questions, (ii) answers (including distractors), and (iii)
their source documents, from the educational domain. Each question is phrased
in two forms, normal and close. Correct answers are linked to source documents
with sentence-level annotations. Thus, our versatile dataset can be used for
both question and distractor generation, as well as to explore new challenges
such as question format conversion. Furthermore, 903 questions are accompanied
by their cognitive complexity level as per Bloom's taxonomy. All questions have
been generated by educational experts rather than crowd workers to ensure they
are maintaining educational and learning standards. Our analysis and
experiments suggest distinguishable differences between our dataset and
commonly used ones for question generation for educational purposes. We believe
this new dataset can serve as a valuable resource for research and evaluation
in the educational domain. The dataset and baselines will be released to
support further research in question generation.

本研究介绍了一个高质量的包含 3397 个样本的数据集，其中包含多项选择题、答案（包括干扰项）以及其来源文档，可用于问题与干扰项的生成以及探索新的挑战，同时伴随着 Bloom 的认知复杂性水平。所有问题均由专业人员生成以确保其符合教育及学习标准，该数据集对于教育域的研究和评估具有重要价值。