# Detection of Students' Problems in Distance Education Using Topic Modeling and Machine Learning
## 1 Introduction  
The coronavirus pandemic has wreaked havoc on every aspect of our modern society. The world was challenged on an unprecedented scale. In response, most governments around the world authorized containment measures including social distancing and the temporary suspension of schools and educational institutions. Educational pedagogies were moved to online education. In some countries, distance education was adopted to maintain the educational activities [1; 2; 3]. As a result of the vital need to replace face-to-face learning, educational institutions had to adopt a digital approach where teaching is delivered on digital platforms [4; 5; 6; 7; 8]. Contrary to general belief, distance education is not a new phenomenon. The emergence of distance education dates back to the 18 th century. Countries such as the USA, England, and Australia have provided distance education in the form of correspondence study to reach learners who are geographically dispersed [9]. With the rapid development in technology, several models in distance education have developed such as online learning in which students use Internet and associated technologies to learn from their homes [10] and blended learning that combines online and in-person learning [11]. All these models use technology in learning but adapt different learning processes. While there has been an increase in the use of online distance learning in various countries, some challenges have been posed for students, parents, teachers, and institutions due to the abrupt transition. As the use of online distance learning continues to increase, the need for quality data to detect underlying trends in order to optimize the efforts of improving the educational experience is essential. Currently, existing research largely relies on surveys throughinterviews or questionnaires [8; 10; 12]. The collected data from these surveys are critical in identifying the themes regarding to the perspectives about distance learning experience. However, their reliability relied on the respondents. Furthermore, generating the result from these data commonly takes time; meanwhile, the observed trends might be changed by the time the result is reported. Hence, alternative methods are needed, including analyzing the data of social media platforms such as Twitter, Facebook, and YouTube. Such platforms offer open sources to students to share their experience and seek social support. This vast amount of data provides educational researchers with implicit knowledge and new perspectives to better understand the students' experiences that inform of the improvement of education quality [13]. Despite the challenges associated with analyzing this massive amount of data to obtain valuable insight, the research collaborations and improvement increased. In response, this study presents a new approach using Twitter data in order to gain insights of distance education issues. Our focus is on Arabic Twitter content posted by users related to distance education in Saudi Arabia at all educational levels. The aim of this study is to detect and classify the potential topics in social media content related to distance education to identify the major issues students face through their distance education experience. To attain this goal, the objectives of this study are as follows:  
* Collect Arabic tweets regarding distance education in Saudi Arabia and perform filtering and preprocessing process. * Apply and train biterm topic model (BTM), an unsupervised machine learning approach on the cleaned tweets to identify the underlying topics and cluster them into themes. * Annotate the resulting themes into major categories and sub-categories that represent the issues of the distance education. * Use multi-class SVM classifier model to classify the entire dataset into the generated categories from the previous step to identify the major issues.