基于NLP术语提取的学生时间特征分析与网络学习资源推荐

André Picado, A. Finamore, Ana Moura Santos, C. Antunes
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摘要

在线教育在过去几年中具有重要意义,大流行的形势证明,在线教育在当今发挥着重要作用。然而,尽管越来越多的学生参加了在线课程,但这些课程仍然没有提供足够的个性化,这往往导致学生失去动力并辍学。更好地适应学生的在线课程的目标是以包容和公平的方式支持他们,因为学习者通常是来自不同背景的学生。对在线学习的持续需求,以及根据学生的个人资料对其进行定制的需求,导致了对推荐系统的一系列尝试。然而,它们中的许多完全基于协同过滤,几乎忽略了分析需求。在本文中,我们提出了一个基于混合架构的推荐系统,并将其集成到mooc (Massive Open Online Courses)中。在我们的建议中,学习资源由一组术语来描述,这些术语直接从MOOC的支持文本中提取。从这些术语中,那些包含在练习中的术语将被用来指定学习者必须获得的重要技能,并且每个学习者在其中获得的结果被用来表征特定学生在给定时刻的状态。然后使用这些状态进行协作推荐,允许对每个特定学生进行不同的推荐。该系统在多个mooc上进行了验证。
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Students Temporal Profiling and e-Learning Resources Recommendation Based on NLP's Terms Extraction
Online education has gained significant relevance over the last few years, and the pandemic situation has brought evidence that it plays a fundamental role nowadays. However, even with the increasing number of students enrolled in online courses, these still do not allow for enough personalization, often leading students to become demotivated and dropping out. The goal of better adapting online courses to students aims to support them in an inclusive and equitable way, since the learners are often students from quite diverse backgrounds. The continuous demand for online learning, and the need to customize it according to the students' profile has led to a succession of attempts at recommendation systems. Nevertheless, many of them were entirely based on collaborative filtering, almost ignoring profiling requirements. In this paper, we propose a recommendation system to be integrated into MOOCs (Massive Open Online Courses), following a hybrid architecture. In our proposal, learning resources are described by a set of terms, extracted directly from the supporting texts in the MOOC. From these terms, those which are included in the exercises will be used to specify the important skills learners must acquire, and the results achieved by each learner in them are used to characterize the particular student's state, at a given moment. Those states are then used to make the recommendation collaboratively, allowing for different recommendations for each particular student over time. The system is validated across several MOOCs.
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