Second language acquisition (SLA) is a complex and dynamic process. Many SLA
studies that have attempted to record and analyze this process have typically
focused on a single modality (e.g., textual output of learners), covered only a
short period of time, and/or lacked control (e.g., failed to capture every
aspect of the learning process). In Project MOSLA (Moments of Second Language
Acquisition), we have created a longitudinal, multimodal, multilingual, and
controlled dataset by inviting participants to learn one of three target
languages (Arabic, Spanish, and Chinese) from scratch over a span of two years,
exclusively through online instruction, and recording every lesson using Zoom.
The dataset is semi-automatically annotated with speaker/language IDs and
transcripts by both human annotators and fine-tuned state-of-the-art speech
models. Our experiments reveal linguistic insights into learners' proficiency
development over time, as well as the potential for automatically detecting the
areas of focus on the screen purely from the unannotated multimodal data. Our
dataset is freely available for research purposes and can serve as a valuable
resource for a wide range of applications, including but not limited to SLA,
proficiency assessment, language and speech processing, pedagogy, and
multimodal learning analytics.

项目 MOSLA 是一个长期的、多模态的、多语言的和可控的数据集，通过在线教学记录参与者学习阿拉伯语、西班牙语和汉语的整个过程，并从未经注释的多模态数据中自动检测屏幕上的重点领域。该数据集可用于 SLA、熟练度评估、语言和语音处理、教学法以及多模态学习分析等广泛应用。

MOSLA 计划：记录第二语言习得的每一刻

Project MOSLA: Recording Every Moment of Second Language Acquisition

Education plays a pivotal role in alleviating poverty, driving economic
growth, and empowering individuals, thereby significantly influencing societal
and personal development. However, the persistent issue of school dropout poses
a significant challenge, with its effects extending beyond the individual.
While previous research has employed machine learning for dropout
classification, these studies often suffer from a short-term focus, relying on
data collected only a few years into the study period. This study expanded the
modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data
from kindergarten to Grade 9. Our methodology incorporated a comprehensive
range of parameters, including students' academic and cognitive skills,
motivation, behavior, well-being, and officially recorded dropout data. The
machine learning models developed in this study demonstrated notable
classification ability, achieving a mean area under the curve (AUC) of 0.61
with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9.
Further data collection and independent correlational and causal analyses are
crucial. In future iterations, such models may have the potential to
proactively support educators' processes and existing protocols for identifying
at-risk students, thereby potentially aiding in the reinvention of student
retention and success strategies and ultimately contributing to improved
educational outcomes.

教育在减轻贫困、推动经济增长和赋予个人力量方面发挥着关键作用，然而，学校辍学的持续问题带来了重大挑战，本研究在 13 年的纵向数据集基础上通过机器学习模型的开发展现出显著的分类能力，未来这样的模型有可能支持教育工作者识别处于风险之中的学生，以改善教育结果。

机器学习早在小学毕业就能预测中学辍学

Machine Learning Predicts Upper Secondary Education Dropout as Early as  the End of Primary School

We present a large longitudinal dataset of tweets from two sets of users that
are suspected to be affiliated with ISIS. These sets of users are identified
based on a prior study and a campaign aimed at shutting down ISIS Twitter
accounts. These users have engaged with known ISIS accounts at least once
during 2014-2015 and are still active as of 2021. Some of them have directly
supported the ISIS users and their tweets by retweeting them, and some of the
users that have quoted tweets of ISIS, have uncertain connections to ISIS seed
accounts. This study and the dataset represent a unique approach to analyzing
ISIS data. Although much research exists on ISIS online activities, few studies
have focused on individual accounts. Our approach to validating accounts as
well as developing a framework for differentiating accounts' functionality
(e.g., propaganda versus operational planning) offers a foundation for future
research. We perform some descriptive statistics and preliminary analyses on
our collected data to provide deeper insight and highlight the significance and
practicality of such analyses. We further discuss several cross-disciplinary
potential use cases and research directions.

本文提供了一份 ISIS 账户发布推文的时间跨度较长的数据集，并对这些账户进行了分析，描述了账户的功能 (如宣传或者行动策划)。文章还探讨了数据集的应用价值以及未来的研究方向。