Orji Fidelia A, Vassileva Julita
Multi-User Adaptive Distributed Mobile and Ubiquitous Computing (MADMUC) Laboratory, Computer Science Department, University of Saskatchewan, Saskatoon, SK, Canada.
Front Artif Intell. 2022 Nov 3;5:1015660. doi: 10.3389/frai.2022.1015660. eCollection 2022.
Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.
学生特征会影响他们获取新知识的意愿和能力。评估和识别学生特征的影响对于在线教育系统而言至关重要。机器学习（ML）在利用学习数据进行学生建模、决策支持系统、自适应系统和评估系统方面正变得愈发重要。在线教育系统中对学生特征进行动态评估的需求不断增长，这促使机器学习方法被应用于特征建模。在学习过程中能够自动对学生特征进行建模，对于教学和学习根据每个学生的需求进行动态和持续的调整至关重要。本文回顾了8年（从2015年到2022年）以来关于应用机器学习方法对各种学生特征进行自动建模的文献。该综述发现了六种可以自动建模的学生特征，并突出了用于对其进行建模的数据类型、收集方法和机器学习技术。研究人员、教育工作者和在线教育系统设计师将从这项研究中受益，因为它可以作为为自适应教育系统创建学生模型时决策的指南。这样的系统可以在学习过程中检测学生的需求，并根据检测到的需求调整学习干预措施。此外，该研究揭示了机器学习在学生特征自动建模应用方面取得的进展，并为该领域提出了新的未来研究方向。因此，机器学习研究人员可以从这项研究中受益，因为他们可以通过研究新的、未探索的技术进一步推进该领域，并找到提高所创建学生模型准确性的新方法。
