You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction 
Chapter Title:  Machine learning techniques on addressing student dropout 
1. Abstract of Cited Paper (BIB001):  Recently, Massive Open Online Courses (MOOCs) have garnered a high level of interest in the media. With larger and larger numbers of students participating in each course, finding useful and informative threads in increasingly crowded course discussion forums becomes a challenging issue for students. In this work, we address this thread overload problem by taking advantage of an adaptive feature-based matrix factorization framework to make thread recommendations. A key component of our approach is a feature space design that effectively characterizes student behaviors in the forum in order to match threads and users. This effort includes content level modeling, social peer connections, and other forum activities. The results from our experiment conducted on one MOOC course show promise that our thread recommendation method has potential to direct students to threads they might be interested in. 
2. Abstract of Cited Paper (BIB002):  Abstract Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes. 
3. Abstract of Cited Paper (BIB003):  Abstract Predicting students performance becomes more challenging due to the large volume of data in educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor the student progress and performance is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of students in Malaysian institutions. Second is due to the lack of investigations on the factors affecting students achievements in particular courses within Malaysian context. Therefore, a systematical literature review on predicting student performance by using data mining techniques is proposed to improve students achievements. The main objective of this paper is to provide an overview on the data mining techniques that have been used to predict students performance. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a students data. We could actually improve students achievement and success more effectively in an eff i cient way using educational data mining techniques. It could bring the benefits and impacts to students, educators and academic institutions. 
4. Abstract of Cited Paper (BIB004):  24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), University College Dublin, Dublin, Ireland, 20-21 September 2016 
5. Abstract of Cited Paper (BIB005):  Accurately predicting students’ future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g., college programs) is much less studied and faces new challenges: 1) Students differ tremendously in terms of backgrounds and selected courses; 2) courses are not equally informative for making accurate predictions; and 3) students’ evolving progress needs to be incorporated into the prediction. In this paper, we develop a novel machine learning method for predicting student performance in degree programs that is able to address these key challenges. The proposed method has two major features. First, a bilayered structure comprising multiple base predictors and a cascade of ensemble predictors is developed for making predictions based on students’ evolving performance states. Second, a data-driven approach based on latent factor models and probabilistic matrix factorization is proposed to discover course relevance, which is important for constructing efficient base predictors. Through extensive simulations on an undergraduate student dataset collected over three years at University of California, Los Angeles, we show that the proposed method achieves superior performance to benchmark approaches. 
6. Abstract of Cited Paper (BIB006):  Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

Literature Review:
In the context of education on addressing student dropout prediction, the techniques for learning can be supervised or unsupervised. Supervised learning is based on learning from a set of labeled examples in the training set so that it can identify unlabeled examples in the test set with the highest possible accuracy (Erik G., 2014). The paradigm of this learning is efficient and it always finds solutions to several linear and non-linear problems such as classification, plant control, forecasting, prediction, robotics and so many others BIB001 . Several existing works have focused on supervised learning algorithms such as Naive Bayesian Algorithm, Association rules mining, ANN based algorithm, Logistic Regression, CART, C4.5, J48, (BayesNet), SimpleLogistic, JRip, RandomForest, Logistic regression analysis, ICRM2 for the classification of the educational dropout student (Kumar et al., 2017) . However, under the classification techniques, Neural Network and Decision Tree are the two methods highly used by the researchers for predicting students' performance BIB004 Joseph 2014) . The advantage of neural network is that, it has the ability to detect all possible interactions between predictors variables (Gray et al., 2014) and could also perform a complete detection without having any doubt even in complex nonlinear relationship between dependent and independent variables (Arsad, Pauziah Mohd Buniyamin, Norlida Manan, 2013), while decision tree had been used because of its simplicity and comprehensibility to uncover small or large data structure and predict the value  . Unlike supervised, unsupervised learning algorithm is used to identify hidden patterns in unlabeled input data. It refers to provide ability to learn and organize information without an error signal and be able to evaluate the potential solution. The lack of direction for the learning algorithm in unsupervised learning can sometime be advantageous, since it lets the algorithm to look back for patterns that have not been previously considered BIB001 . Several techniques have been proposed on addressing this problem of student dropout using different approaches such as Survival Analysis BIB005 were presented to perform clustering, which are extensively used in recommender systems BIB006 . Survival analysis is used to analyze data in which the time until the event is of interest (Kartal 2015) . It provides various mechanisms to handle such censored data problems that arise in modeling such as longitudinal data (also referred as time-to-event data when modeling a particular event of interest is the main objective of the problem) which occurs ubiquitously in various real-world application domains (Wang et al., 2017a) . In the context of education, the use of survival analysis modeling to study student retention was developed. Ameri et al. (2016) developed a survival analysis framework with the aim of identifying at-risk students using Cox proportional hazards model (Cox) and applied time-dependent Cox (TD-Cox). This approach captures time-varying factors and leverage those information to provide more accurate prediction of student dropout, using the dataset of students enrolled at Wayne State University (WSU) starting from 2002 until 2009. Certainly, subjects in survival analysis are usually followed over a specified period of time and the focus is on the time at which the event of interest occurs (Li et al., 2016) . Thus, the benefit of using survival analysis over other methods is the ability to add the time component into the model and also effectively handle censored data. In spite of the success of survival analysis methods in other domains such as health care, engineering, etc., there is only a limited attempt of using these methods in student retention problem (Bani and Haji, 2017) . Matrix factorization is a clustering machine learning methods that can accommodate framework with some variations BIB002 . The study presented by Hu and Rangwala (2017); Elbadrawy et al. (2016) , described matrix factorization. In Elbadrawy et al. (2016) study, two classes of methods for building the prediction models were presented. The aim of the conducted study was to facilitate a degree planning and determine who might be at risk of failing or dropping a class. The first class builds models using linear regression approaches and the second class used matrix factorization approaches. Regression-based methods describe course-specific regression (CSpR) and personalized linear multi-regression (PLMR) while matrix factorization based methods associate standard Matrix Factorization (MF) approach. The mentioned approach was applied on the dataset generated from George Mason University (GMU) transcript data, University of Minnesota (UMN) transcript data, UMN LMS data, and Stanford University MOOC data. One limitation of the standard MF method is that, it ignores the sequence in which the students have taken the various courses. Besides, the latent representation of a course can potentially be influenced by the performance of the students in courses that were taken afterward. Furthermore, the work present in Iam-On and Boongoen (2017) study, proposed a new data transformation model, which is built upon the summarized data matrix of link-based cluster ensembles (LCE). The aim of the conducted study was to establish the clustering approach as a practical guideline for exploring student categories and characteristics. This was accomplished using educational dataset obtained from the operational database system at Mae Fah Luang University, Chiang Rai, Thailand. Like several existing dimension reduction techniques such as Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA), this method aims to achieve high classification accuracy by transforming the original data to a new form. However, the common limitation of these new techniques is the demanding time complexity, such that it may not scale up well to a very large dataset. Whilst worst-Case Traversal Time (WCT-T) is not quite for a highly time-critical application, it can be an attractive candidate for those quality-led works, such as the identification of those students at risk of under achievement. Deep neural network (DNN) is an approach based on Artificial Neural Networks (ANN) with multiple hidden layers between the input and output layers (Deng and Yu, 2014) . While, Probabilistic Graphical Model (PGM) combine probability theory and graph theory to offer a compact graph-based representation of joint probability distributions exploiting conditional independences among the random variables BIB003 . Similar to shallow ANNs, DNNs can model complex non-linear relationships BIB007  . Different deep learning architecture such as Recurrent Neural Network (RNN) and other probabilistic graphical model such as Hidden Markov Model (HMM) have been employed on the problem of student dropout (Fei and Yeung 2015) . The study presented by Fei and Yeung (2015) , considered two temporal models which are state space models and recurrent neural networks. These approaches were applied in two MOOCs datasets, one offered on the Coursera platform, called "The Science of Gastronomy", and the other on the edX platform, called "Introduction to Java Programming". The aim of the conducted study was to identify students at risk of dropping out. State space models describe two variants of Input Output Hidden Markov Model (IOHMM) with continuous state space while, recurrent neural networks describe vanilla RNN and RNN with Long Short Term Memory (LSTM) cells as hidden units. IOHMM was proposed by for learning problems involving sequentially structured data. As it was originated from HMM, it learned to map input sequences to output sequences. Moreover, unlike the standard discrete-state HMM, the state space in described IOHMM formulation is continuous so the state space can bear more representation power compared with enumerating discrete states. Furthermore, Vanilla Recurrent Neural Network (Vanilla RNN), unlike feed forward neural networks such as the Multi Layer Perceptron (MLP), allows the network connections to form cycles. The limitation of the conducted study was vanishing gradient problem. While an important property of RNNs is their ability to use contextual information in learning the mapping between the input and output sequences. A subtlety is that, for basic RNN models, the range of temporality that can be accessed in practice is usually quite limited so that the dynamic states of RNNs are considered as short term memory. This is because of the influence of a given input on the hidden layer. Therefore, on the network output it will either decays or blows up exponentially so as to cycles around the recurrent connections. To handle short-term memory of RNNs last for longer so as to tackle the vanishing gradient problem, Long Short-Term Memory RNN (LSTM Network) was introduced. On addressing the problem of student dropout, machine learning techniques have been applied in various platforms such as Massive Open On-line Course (MOOC) (Chen et al., 2017; Liang et al., 2016; Fei and Yeung 2015; Prieto et el., 2017) and other Learning Management System (LMS) such as Moodle (Elbadrawy et al., 2016; Hung et al., 2017;  . These platforms generated datasets which contain information that can be categorized into academic performance, socio-economic and personal information (Lei and Li, 2015) . MOOC platforms such as Coursera and edX is among popular used platforms for generating datasets to be used in student dropout prediction (Chen et al., 2017) . While, Moodle as a popular Learning Management System  , provides public datasets such as UMN LMS (Elbadrawy et al., 2016) . Furthermore, on identifying at risk students for early interventions, other researchers collected data from an on-line graduate program in the United States and validation was conducted by using Fall 2014 data set (Hung et al., 2017) .