This paper presents a work-in-progress on a learn-ing system that will
provide robotics students with a personalized learning environment. This
addresses both the scarcity of skilled robotics instructors, particularly in
community colleges and the expensive demand for training equipment. The study
of robotics at the college level represents a wide range of interests,
experiences, and aims. This project works to provide students the flexibility
to adapt their learning to their own goals and prior experience. We are
developing a system to enable robotics instruction through a web-based
interface that is compatible with less expensive hardware. Therefore, the free
distribution of teaching materials will empower educators. This project has the
potential to increase the number of robotics courses offered at both two- and
four-year schools and universities. The course materials are being designed
with small units and a hierarchical dependency tree in mind; students will be
able to customize their course of study based on the robotics skills they have
already mastered. We present an evaluation of a five module mini-course in
robotics. Students indicated that they had a positive experience with the
online content. They also scored the experience highly on relatedness, mastery,
and autonomy perspectives, demonstrating strong motivation potential for this
approach.

该研究论文介绍了一个正在进行的关于为机器人学生提供个性化学习环境的学习系统，通过与价格较低的硬件兼容的基于 Web 的界面，教育者可以免费分发教材，此方法在机器人学习领域具有潜在的教学增长可能性。

大学本科机器人教育推进的以学生为中心的个性化学习框架发展

WIP: Development of a Student-Centered Personalized Learning Framework  to Advance Undergraduate Robotics Education

One of the main tasks of Natural Language Processing (NLP), is Named Entity
Recognition (NER). It is used in many applications and also can be used as an
intermediate step for other tasks. We present ANER, a web-based named entity
recognizer for the Arabic, and Arabizi languages. The model is built upon BERT,
which is a transformer-based encoder. It can recognize 50 different entity
classes, covering various fields. We trained our model on the WikiFANE\_Gold
dataset which consists of Wikipedia articles. We achieved an F1 score of
88.7\%, which beats CAMeL Tools' F1 score of 83\% on the ANERcorp dataset,
which has only 4 classes. We also got an F1 score of 77.7\% on the
NewsFANE\_Gold dataset which contains out-of-domain data from News articles.
The system is deployed on a user-friendly web interface that accepts users'
inputs in Arabic, or Arabizi. It allows users to explore the entities in the
text by highlighting them. It can also direct users to get information about
entities through Wikipedia directly. We added the ability to do NER using our
model, or CAMeL Tools' model through our website. ANER is publicly accessible
at \url{this http URL}. We also deployed our model on HuggingFace at
this https URL, to allow developers to test and use it.

自然语言处理中的命名实体识别（NER）是一项主要任务之一，本文介绍了一个基于 BERT 模型的用于阿拉伯语和 Arabizi 语言的网页命名实体识别器。该模型可以识别 50 个不同的实体类别，并在多个数据集上取得了高准确率，系统部署在用户友好的网页界面上供用户使用，并具备了使用 CAMeL Tools 模型的能力。