在线环境中学习活动的个性化推荐：基于模块规则的方法

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

User Modeling and User-Adapted Interaction Pub Date : 2024-04-06 DOI:10.1007/s11257-024-09396-z

Radek Pelánek, Tomáš Effenberger, Petr Jarušek

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引用次数: 0

摘要

在线学习环境中的个性化已经在不同层面上得到了广泛的研究，从任务解决过程中的自适应提示到整个课程的推荐。在本研究中，我们的重点是推荐学习活动（同质任务序列）。我们认为，这是一个重要但尚未得到充分探索的领域，尤其是在考虑到实际使用的大规模在线学习环境的要求时。为了弥补这一不足，我们提出了一个基于模块规则的推荐框架，并详细解释了该建议背后的原理。我们还讨论了该框架的具体应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Personalized recommendations for learning activities in online environments: a modular rule-based approach

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Personalized recommendations for learning activities in online environments: a modular rule-based approach

Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.

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来源期刊

User Modeling and User-Adapted Interaction 工程技术-计算机：控制论

CiteScore

8.90

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems