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

Revision behavior in adaptive writing support systems is an important and
relatively new area of research that can improve the design and effectiveness
of these tools, and promote students' self-regulated learning (SRL).
Understanding how these tools are used is key to improving them to better
support learners in their writing and learning processes. In this paper, we
present a novel pipeline with insights into the revision behavior of students
at scale. We leverage a data set of two groups using an adaptive writing
support tool in an educational setting. With our novel pipeline, we show that
the tool was effective in promoting revision among the learners. Depending on
the writing feedback, we were able to analyze different strategies of learners
when revising their texts, we found that users of the exemplary case improved
over time and that females tend to be more efficient. Our research contributes
a pipeline for measuring SRL behaviors at scale in writing tasks (i.e.,
engagement or revision behavior) and informs the design of future adaptive
writing support systems for education, with the goal of enhancing their
effectiveness in supporting student writing. The source code is available at
this https URL

本文提出了一种新的管道，用于量化自适应写作支持系统中的修订行为，揭示出使用这些工具的学生如何进行修订，并研究其对学生写作和学习过程的影响，为将来设计更有效支持学生写作的自适应写作支持工具提供了启示。

教育中自适应写作支持系统中的修订行为研究

Understanding Revision Behavior in Adaptive Writing Support Systems for  Education

Future activity anticipation is a challenging problem in egocentric vision.
As a standard future activity anticipation paradigm, recursive sequence
prediction suffers from the accumulation of errors. To address this problem, we
propose a simple and effective Self-Regulated Learning framework, which aims to
regulate the intermediate representation consecutively to produce
representation that (a) emphasizes the novel information in the frame of the
current time-stamp in contrast to previously observed content, and (b) reflects
its correlation with previously observed frames. The former is achieved by
minimizing a contrastive loss, and the latter can be achieved by a dynamic
reweighing mechanism to attend to informative frames in the observed content
with a similarity comparison between feature of the current frame and observed
frames. The learned final video representation can be further enhanced by
multi-task learning which performs joint feature learning on the target
activity labels and the automatically detected action and object class tokens.
SRL sharply outperforms existing state-of-the-art in most cases on two
egocentric video datasets and two third-person video datasets. Its
effectiveness is also verified by the experimental fact that the action and
object concepts that support the activity semantics can be accurately
identified.

本文提出了一种基于自我调节学习的框架来解决标准递归序列预测在 egocentric 视觉中存在的误差积累问题，同时通过多任务学习进行联合特征学习，明显优于现有的最新技术，可以精确地识别支持活动语义的动作和对象概念。