# Detection of Students' Problems in Distance Education Using Topic Modeling and Machine Learning
## 5 Discussion and Limitation
In addition, the students found enough time to spend with their family and friends. Our finding is almost the opposite of that: a lack of time and social interaction with family and friends has a negative impact on students' distance learning. This study demonstrates that lack of motivation, stress, and anxiety are social issues that prevent students from pursuing their study effectively. Similar issues were obtained by [PERSON] [28]; they found that challenges for students during online lectures were motivation and fear of collaboration. Another study by [PERSON] et al. [27] supports our finding that students require more effort to stay motivated and have self-discipline. Overall, most of the studies concluded that the most crucial issues among students were technological and education barriers. Additionally, a lack of face-to-face contact might cause more challenges such as lack of focus, lack of social interaction, and low motivation and well-being, which could also provoke a stressful situation. The current findings offer a wide picture of the experience of students during their distance education. Students have increasingly become engaged in the improvement of their learning experience. Their feedback regarding the learning process has become an indicator of educational quality. The present materials in their social media content could provide insights in which educators can make improvements to the quality of the learning process. For example, conversations about lessons and material can give insight into how to interpret and develop the concepts of the lesson, which will impact on the quality of teaching and learning. A large stream of Twitter posts that discuss sparsely events were annotated as 'others'. In this category, some tweets do not have obvious meaning; also, some do not reflect students' concerns. Since Twitter posts are considered as user-generated content, they follow the long-tail distribution [47]. Therefore, a small number of tweets that exhibit major students' problems have high occurrence, whereas the large numbers of tweets that exhibit insignificant problems have low occurrence. Investigation of this category can reveal insightful information. Certain aspects of the work uncover some limitations in achieving the goal. Firstly, we found that most of tweets' content include issues and problems; on the other hand, several tweets contain positive aspects. However, the focus of this study is on the problems because they could be the most revealing and insightful for improvement of the education process. Secondly, great numbers of tweets were classified as 'Others' due to their lack of sufficient description. There are a variety of other issues hidden in this category, but this study did not conduct further analysis into the content of it. Lastly, the study is an exploratory study to discover the main issues related to distance education. The classification was built based on label independency. In fact, we did not consider the correlation between the issues under classification. However, to gain a full understanding would be optimal to address the correlations among these problems.