Learning Management Systems (LMS) lack automated intelligent components that analyze data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaires related to a specific learning style model and cognitive psychometric tests have been used to identify such behavior. The problem with such methods is that a learner can give inaccurate information. The manual method is also time-consuming and prone to errors. Although literature reports complex models predicting learning styles, only a few have used machine learning methods such as an artificial neural network (ANN). The primary objective of this study was to design, develop, and evaluate a model based on machine learning for predicting learner behavior from LMS log records. Approximately 200,000 log records of 311 students who had accessed e-Learning courses for a 15-week semester were extracted from LMS to create a dataset. Machine learning concepts were identified from the log records. The dataset was split into training and testing sets. A model using the artificial neural network algorithm was designed and implemented using an r-studio programming language. The model was trained to predict learner behavior and classify each student. The prediction success rate of 0.63, 0.67, 0.64, 0.65, 0.26, 0.64 accuracy, precision, recall, f-score, kappa, and Area Under the Curve (AUC) respectively were recorded. This demonstrates that the model after full validation can be relied on to identify learner behavior.

学习管理系统（LMS）缺少自动智能组件，这些组件可以分析数据并根据学习者各自的特征对学习者进行分类。涉及管理与特定学习风格模型和认知心理测验有关的问卷的手动方法已用于识别此类行为。这种方法的问题在于学习者会给出不准确的信息。手动方法也很耗时并且容易出错。尽管文献报道了预测学习方式的复杂模型，但只有少数人使用了机器学习方法，例如人工神经网络（ANN）。这项研究的主要目的是设计，开发和评估基于机器学习的模型，以根据LMS日志记录预测学习者的行为。大约200 从LMS中提取了311个访问了15周学期电子学习课程的学生的000条日志记录，以创建一个数据集。从日志记录中识别出机器学习概念。数据集分为训练和测试集。使用r-studio编程语言设计和实现了使用人工神经网络算法的模型。该模型经过训练，可以预测学习者的行为并对每个学生进行分类。记录预测成功率分别为0.63、0.67、0.64、0.65、0.26、0.64，准确性，召回率，f得分，kappa和曲线下面积（AUC）。这表明完全验证后的模型可以用来识别学习者的行为。从日志记录中识别出机器学习概念。数据集分为训练和测试集。使用r-studio编程语言设计和实现了使用人工神经网络算法的模型。该模型经过训练，可以预测学习者的行为并对每个学生进行分类。记录预测成功率分别为0.63、0.67、0.64、0.65、0.26、0.64，准确性，召回率，f得分，kappa和曲线下面积（AUC）。这表明完全验证后的模型可以用来识别学习者的行为。从日志记录中识别出机器学习概念。数据集分为训练和测试集。使用r-studio编程语言设计和实现了使用人工神经网络算法的模型。该模型经过训练，可以预测学习者的行为并对每个学生进行分类。记录预测成功率分别为0.63、0.67、0.64、0.65、0.26、0.64，准确性，召回率，f得分，kappa和曲线下面积（AUC）。这表明完全验证后的模型可以用来识别学习者的行为。该模型经过训练，可以预测学习者的行为并对每个学生进行分类。记录预测成功率分别为0.63、0.67、0.64、0.65、0.26、0.64，准确性，召回率，f得分，kappa和曲线下面积（AUC）。这表明完全验证后的模型可以用来识别学习者的行为。该模型经过训练，可以预测学习者的行为并对每个学生进行分类。记录预测成功率分别为0.63、0.67、0.64、0.65、0.26、0.64，准确性，召回率，f得分，kappa和曲线下面积（AUC）。这表明完全验证后的模型可以用来识别学习者的行为。