Ahmadi Sadra, Nourmohamadzadeh Zahra, Amiri Babak
Cyberspace Research Institute, Shahid Beheshti University, Iran, Tehran.
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Heliyon. 2023 Jul 15;9(7):e17894. doi: 10.1016/j.heliyon.2023.e17894. eCollection 2023 Jul.
Massive Online Open Courses (MOOCs) offer free access to training in various topics in all fields. However, the low percentage of course completion by learners is a significant challenge for these platforms. Previous studies on this challenge have investigated user behavior and concerned topics in discussion forums, but these data are mostly momentary and cannot be used for long-term improvement. Thus, this study aimed to address this gap by analyzing learners' comments to identify the factors affecting user satisfaction and prioritize them to improve MOOC platforms. The purpose was to analyze the feedback and actual experiences of users shared through their comments on MOOC online platforms to explore factors affecting user satisfaction to optimize MOOC platforms. To achieve this, sentiment analysis and topic modeling techniques were applied to the user feedback on courses with popular topics, such as Skills for Data Science Teams and Data-Driven Decision Making, available on Coursera.com. The study used DEMATEL analysis, which uses a relation matrix of factors to rank them based on their interrelationships, and network analysis to prioritize the factors that should be improved to achieve the highest user satisfaction. The effect of the proposed approach was investigated through a case study on a course from Coursera. The findings demonstrate that the suggested method has the potential to assist MOOC platforms in several ways. Firstly, it enables the identification of course strengths and weaknesses. Secondly, it allows for the identification of factors that influence learner satisfaction by analyzing user feedback. Lastly, it aids in prioritizing the factors that should be enhanced to attain optimal user satisfaction, thus leading to overall improvement in the status of the MOOC platform.
大规模在线开放课程（MOOCs）提供了免费学习所有领域各种主题培训的机会。然而，学习者课程完成率较低是这些平台面临的一项重大挑战。以往针对这一挑战的研究调查了用户行为以及讨论论坛中涉及的主题，但这些数据大多是即时性的，无法用于长期改进。因此，本研究旨在通过分析学习者的评论来找出影响用户满意度的因素并对其进行优先级排序，以填补这一空白，从而改进MOOC平台。目的是分析用户在MOOC在线平台上通过评论分享的反馈和实际体验，探索影响用户满意度的因素，以优化MOOC平台。为实现这一目标，对Coursera.com上提供的热门主题课程（如数据科学团队技能和数据驱动的决策）的用户反馈应用了情感分析和主题建模技术。该研究使用了决策试验与评价实验室（DEMATEL）分析，该分析通过因素关系矩阵根据因素之间的相互关系对其进行排序，并使用网络分析对为实现最高用户满意度而应改进的因素进行优先级排序。通过对Coursera上一门课程的案例研究，对所提出方法的效果进行了调查。研究结果表明，所建议的方法有可能在多个方面帮助MOOC平台。首先，它能够识别课程的优势和劣势。其次，通过分析用户反馈，它可以识别影响学习者满意度的因素。最后，它有助于对为实现最佳用户满意度而应加强的因素进行优先级排序，从而全面提升MOOC平台的状况。
