基于心理类型和学习风格模型的个性化E-learning推荐模型

Mohamed Soliman Halawa, Essam M. Ramzy Hamed, M. E. Shehab
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引用次数: 20

摘要

网络学习已经成为现代教育系统的重要组成部分。在当今多样化的学生群体中,电子学习必须认识到学生个性的差异,使学习过程更加个性化,并帮助克服“一刀切”的学习模式。每个学习者都有不同的学习方式和不同的个人需求。本研究提出了一种数据驱动的推荐模型,该模型利用学生的个性和学习风格来推荐学习课程的展示方式或对象方式。数据模型基于Myers-Briggs类型指标(MBTI)理论识别学生的人格类型和优势偏好。提出的模型利用了学生使用学习管理系统(Moodle)和社交网络Facebook的数据。这种模式帮助学生意识到自己的个性,这反过来又使他们在学习习惯上更有效率。该模型还为教育工作者提供了重要的信息,使他们能够更好地了解每个学生的个性。预测的人格偏好与科尔布模型中相应的学习风格相匹配。在一个学生样本上测试了推荐模型的实验,最后对我们的学生样本数据集中收集的一些行为进行t检验来验证模型。结果表明,将研究数据驱动模型应用于电子学习系统后,学生对课程的参与度和承诺度有所提高。
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Personalized E-learning recommendation model based on psychological type and learning style models
E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized, and to help overcome “one-size-fits-all” learning model. Each learner has a different learning style and different individual needs. This study proposes a data-driven recommendation model which uses the student's personality and learning style in order to recommend the learning course presentation or objects way. The data model identifies both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI) theory. The proposed model utilizes data from student engagement with the learning management system (Moodle) and the social network, Facebook. The model helps students become aware of their personality, which in turn makes them more efficient in their study habits. The model also provides vital information for educators, equipping them with a better understanding of each student's personality. The predicted personality preference was used to match it with the corresponding learning styles from Kolb's model. An experiment of the recommendation model was tested on a sample of students, and at the end a t-test was applied on some collected behavior from our student sample dataset to validate the model. The results indicate an improvement in the students' engagement and commitment to the course after applying the research data-driven model on the e-learning system.
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