# Detection of Students' Problems in Distance Education Using Topic Modeling and Machine Learning
## 2 Related Work  
Extracting the topics that reflect students' concerns according to their online conversations has become an important source to enhance distance education and develop a new learning styles. Consequently, many studies have been conducted extensively. In this section, we will focus on studies related to the topic detection methods and educational data analysis. The widely used models to uncover the hidden topics in the text are the probabilistic semantic-based indexing model (PLSI) [14] and latent dirichlet allocation (LDA) [15]. PLSI underlines the topic based on the semantic indexing wherein low-dimensional representation has been utilized to evaluate the similarities. The LDA model is an unsupervised model that has been used to discover and document subject knowledge. For online communities, several models were proposed based on the LDA model to mine the online content and identify the topics. [PERSON] et al. developed a personalized recommendation system for e-learning based on the content of the text. They utilized the topic model by introducing the similarity measures [16]. [PERSON] et al. [17] designed a model for the quality of online students' comments; they constructed a set of topic features that strongly related to the quality comments. Applying such models directly on short texts will yield to data sparsity problem [18]. [PERSON] et al. [18] proposed a model to detect topics in short textual data called Biterm Topic Model (BTM); their model generates the term co-occurrence in the whole corpus to improve the topic learning. Distance education is a system of learning and teaching. Analyzing and mining the data generated in an online education setting is an effective method to mine and understand students' learning environments. Data-driven approaches have been used to analyze structured data including student performance, activity, or course discussion forums [19]. In fact, the exchange of the information about online education has been increased using online platforms, and various studies have been put forward to enhance the quality of data mining in distance learning. [PERSON] et al. [20] utilized text mining techniques to improve the educational process; they used regression techniques to predict the performance of the students. [PERSON] investigated students' opinions in the accounting department at the University of South Africa about integrating social media apps in the learning process to improve their education. They found that students have positive opinions toward using social media for academic support [21]. The authors in [22] proposed an intellectual distance education system to exchange information of artificial immune systems. The system allows learners to experience a real-time training to gain necessary skills. The opinion mining system has been widely used to measure public opinion about any topic or event. Many studies have been carried out to investigate the opinion regarding distance education. [PERSON] et al. [23] developed a multi-aspect-based system to explore students' opinion about distance education in India. They applied sentiment classification on online reviews using Naive Bayes classifier. A recent study by [PERSON] et al. [24] employed a sentiment analysis approach to investigate people's opinions about distance learning in Saudi Arabia. They utilized different classification techniques including support vector machine, Naive Bayes, logistic regression, k-nearest neighbor, and XGBoost, and the best accuracy was achieved in logistic regression with a unigram feature. Their finding demonstrates that there was a general positive opinion at the school level and negative opinion at the higher education level. A more recently study by [PERSON] [25] applied several classical machine-learning models to explore public's opinion in KSA regarding distance education. The author classified the tweet into two classes (Infavor, Against), which represent negative and positive opinion. The result of the study showed positive opinion related to distance education.