You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.

Cited Abstract(s):
[1]: Predicting the academic progress of student is an issue faced by many public universities in emerging countries. Although, those institutions stores large amounts of educational data, they fail to recognize the students that are in danger to leave the system. This paper presents a novel architecture that uses EDM techniques to predict and to identify those who are at dropout risk. This approach allows academic managers to monitor the progress of the students in each academic semester, identifying the ones in difficult to fulfill their academic requirements. This paper shows initial experimental results using real world data about of three undergraduate engineering courses of one the largest Brazilian public university. According to the experiments, the classifier Naive Bayes presented the highest true positive rate for all datasets used in the experiments.

[2]: Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.

[3]: 

[4]: The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80 . Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.

[5]: In the last two decades, number of Engineering Institutes and Universities grows rapidly in India. This causes a tight competition among these institutions and Universities while attracting the student to get admission in these Institutions Universities. Most of the institutions and courses opened in Universities are in self finance mode, so all time they focused to fill all the seats of the courses not on the quality of students. Therefore a large number of students drop the course after first year. This paper presents a data mining application to generate predictive models for student's dropout management of Engineering. Given new records of incoming students, the predictive model can produce accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.
In this section we outline perspectives on student attrition that have been explored so far in the literature on MOOCs. Much of this work successfully leverages effective feature engineering and advanced statistical methods. However, the biggest limitation of most of these emerging works is that they focus solely on discussion forum behavior or video lecture activity, but do not fuse and take them into account. Some of these works have grown out of research on predicting academic progress of students and identifying students those who are at dropout risk [3] [4] [5] [2] [1] .