The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning. Relevant information was obtained from learning behavior data, and student behavior was modeled based on the seven indicators of engagement. Cluster analysis results were presented in the form of three scenarios: (1) grammar-focused semester, (2) vocabulary-focused semester and, (3) overall one-year course activity (grammar-focused and vocabulary-focused semester together). Each scenario helped identify four clusters and revealed different behavioral patterns (Scenario 1: “Restrained”, “Focused”, “Cautious”, and “Social”; Scenario 2: “Result-oriented”, “Restrained”, “Cautious”, and “Active”; Scenario 3: “Cautious”, “Restrained”, “Result-oriented”, and “Focused”). Some patterns are stable and present in all three scenarios (“Restrained” and “Cautious”), while others seem to be specific to a particular learning context. The subsequent expert analysis phase offers an interpretation of the identified patterns, guidelines for the adjustment of the learning process, and a consideration of pedagogical, ethical and technical implications of this approach. Depending on the level of automation, targeted adjustment might be done by human or nonhuman operator. Taking this into consideration, this paper proposes an approach to managing behavioral engagement patterns of students in a course-based virtual learning environment. The approach encompasses four steps which need to be implemented periodically depending on the course material dynamics, so the future delivery methods in the e-learning course could be adapted accordingly.

这项研究的重点是确定学生的行为模式，以支持基于数据的决策，从而改善在线英语课程的学习体验和学习成果。学习分析方法（或更具体地说聚类分析）用于识别在线学习中的参与模式。从学习行为数据中获取相关信息，并根据七个参与指标对学生行为进行建模。聚类分析结果以三种场景的形式呈现：（1）以语法为重点的学期，（2）以词汇为重点的学期，（3）整体一年的课程活动（以语法为重点和以词汇为重点的学期一起）。每个场景都有助于识别四个集群并揭示不同的行为模式（场景 1：“克制”、“专注”、“谨慎”、“社交”；情景二：“结果导向”、“克制”、“谨慎”、“积极”；情景3：“谨慎”、“克制”、“结果导向”、“专注”）。有些模式是稳定的，并且存在于所有三种场景（“克制”和“谨慎”）中，而其他模式似乎特定于特定的学习环境。随后的专家分析阶段提供了对已识别模式的解释、学习过程调整的指导方针，以及对该方法的教学、伦理和技术影响的考虑。根据自动化水平，有针对性的调整可能由人类或非人类操作员完成。考虑到这一点，本文提出了一种在基于课程的虚拟学习环境中管理学生行为参与模式的方法。