A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> We propose a system for mining videos from the web for supplementing the content of electronic textbooks in order to enhance their utility. Textbooks are generally organized into sections such that each section explains very few concepts and every concept is primarily explained in one section. Building upon these principles from the education literature and drawing upon the theory of Formal Concept Analysis, we define the focus of a section in terms of a few indicia, which themselves are combinations of concept phrases uniquely present in the section. We identify videos relevant for a section by ensuring that at least one of the indicia for the section is present in the video and measuring the extent to which the video contains the concept phrases occurring in different indicia for the section. Our user study employing two corpora of textbooks on different subjects from two countries demonstrate that our system is able to find useful videos, relevant to individual sections. <s> BIB001 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques-over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)-along with four popular classification methods-logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates. <s> BIB002 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1–4 can predict student performance up to a standard error of 13–15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results. <s> BIB003 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> Higher education for the 21st century continues to promote discoveries in the field through learning analytics (LA). The problem is that the rapid embrace of of LA diverts educators’ attention from clearly identifying requirements and implications of using LA in higher education. LA is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of LA in higher education. Few studies have synthesized previous studies to provide an overview of LA issues in higher education. To address the problem, a systemic literature review was conducted to provide an overview of methods, benefits, and challenges of using LA in higher education. The literature review revealed that LA uses various methods including visual data analysis techniques, social network analysis, semantic, and educational data mining including prediction, clustering, relationship mining, discovery with models, and separation of data for human judgment to analyze data. The benefits include targeted course offerings, curriculum development, student learning outcomes, behavior and process, personalized learning, improved instructor performance, post-educational employment opportunities, and enhanced research in the field of education. Challenges include issues related to data tracking, collection, evaluation, analysis; lack of connection to learning sciences; optimizing learning environments, and ethical and privacy issues. Such a comprehensive overview provides an integrative report for faculty, course developers, and administrators about methods, benefits, and challenges of LA so that they may apply LA more effectively to improve teaching and learning in higher education. <s> BIB004 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona. <s> BIB005 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> As part of a research project aiming to explore the notion of ‘smart school’ (especially for STEM education) in Estonia, we are developing classrooms and schools that incorporate data gathering not only from digital traces, but also physical ones (through a variety of sensors). This workshop contribution describes briefly the setting and our initial efforts in setting up a classroom that is able to generate such a multimodal dataset. The paper also describes some of the most important challenges that we are facing as we setup the project and attempt to build up such dataset, focusing on the specifics of doing it in an everyday, authentic school setting. We believe these challenges provide a nice sample of those that the multimodal learning analytics (MMLA) community will have to face as it transitions from an emergent to a mainstream community of research and practice. <s> BIB006 </s> A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction <s> Machine learning in education <s> 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. <s> BIB007
Over the past two decades, there has been significant advances in the field of machine learning. This field emerged as the method of choice for developing practical software for computer vision, speech recognition, natural language processing, robot control, and other applications (Jordan and Mitchell 2015) . There are several areas where machine learning can positively impact education. The study conducted by Center for Digital Technology and Management (2015) , reported on the growth of the use of machine learning in education, this is due to the rise in the amount of education data available through digitization. Various schools have started to create personalized learning experiences through the use of technology in classrooms. Furthermore, Massive open on-line courses (MOOCs) have attracted millions of learners and present an opportunity to apply and develop machine learning methods towards improving student learning outcomes and leveraging the data collected (Lee 2017). Owing to the advancement of the amount of data collected, machine learning techniques have been applied to improve educational quality including areas related to learning and content analytics (Lan et al., 2014;  , knowledge tracing  , learning material enhancement BIB001 and early warning systems (Beck and Davidson 2016; Brundage, 2014; US Department of Education, 2016) . The use of these techniques for educational purpose is a promising field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns BIB004 . One of the first applications of machine learning in education had been helping quizzes and tests move from multiple choice to fill in the blank answers. 2 The evaluation of students' free form answers was based on Natural Language Processing (NLP) and machine learning. Various studies on efficacy of automated scoring show better results than human graders in some cases. Furthermore, automated scoring provides more immediate scoring than a human, which helps for use in formative assessment. A few years ago, prediction has been observed as an application of machine learning in education. A research conducted by Kotsiantis (2012), presented a novel case study describing the emerging field of educational machine learning. In this study, students' key demographic characteristic data and grading data were explored as the data set for a machine learning regression method that was used to predict a student's future performance. In a similar vein, several projects were conducted including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school. 4 Springboarding from these examples, IBM's Chalapathy Neti shared IBM's vision of Smart Classrooms using cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, and observe why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges. Certainly, machine learning application in education still face several challenges that need to be addressed. There is lack of available open-access datasets especially in developing countries; more data-sets need to be developed, however cost must be acquired. Apart from that, several researchers ignore the fact that evaluation procedures and metrics should be relevant to school administrators. According to Lakkaraju et al. (2015) , the evaluation process should be designed to cater the needs of educators rather than only focused on common used machine learning metrics. In addition to that; the same study reveals that, many studies focused only on providing early prediction. While, a more robust and comprehensive early warning systems should be capable of identifying students at risk in future cohorts, rank students according to their probability of dropping and identifying students who are at risk even before they drop. Therefore, developing countries need to focus on facilitating a more robust and comprehensive early warning systems for students' dropout. Also, there is need to focus on school level datasets rather than only focusing on student level datasets; this is due to the fact that school districts often have limited resources for assisting students and the availability of these resources varies with time. Therefore, identifying at risk schools will help the authorities to plan for resource allocation before the risk. Furthermore, in the context of education data imbalance is very common classification problem in the field of student retention, mainly because the number of registered students is large compared to the number of dropout students BIB002 . According to Gao (2015) , the imbalanced ratio is about at least 1:10. Besides, the minority class usually represents the most important concept to be learned, it is difficult to identify it due to exceptional and significant cases (López et al., 2013) . Since accuracy as a widely used metric has less effect on minority class than majority class (Longadge et al., 2013; Lin and Chen, 2013) , several researchers applied other metrics such as F-measure (Mgala and Mbogho 2015; BIB005 Aulck et al., 2017) , Mean Absolute Error (MAE) (Ameri et al., 2016; Elbadrawy et al., 2016; Lakkaraju et al., 2015; BIB005 , Area Under the curve (AUC) (Liang et al., 2016; Fei and Yeung, 2015; Aulck et al., 2016; BIB006 Mgala and Mbogho, 2015; Halland et al., 2015) , mean squared error (Iam-On and Boongoen 2017; BIB007 ), Root-Mean-Square Error (RMSE) (Elbadrawy et al., 2016) , error residuals BIB003 , and misclassification rates (Hung et al., 2017) on addressing the problem of student dropout. The power of machine learning can step in building better data to help authorities draw out crucial insights that change outcomes. When students drop out of school instead of continuing with education, both students and communities lose out on skills, talent and innovation. 6 On addressing student dropout problem, several predictive models were developed in developed countries to process complex data sets that include details about enrollment, student performance, gender and socio-economic demographics, school infrastructure and teacher skills to find predictive patterns. Although on developing predictive models, developing countries need to consider other factors such as school distance which has been ignored by several researchers but matters in the developing countries' scenario. Despite the fact that, evaluation of developed predictive models tend to differ but the focus remains on supporting administrators and educators to intervene and target the most at-risk students so as to invest and prevent dropouts in order to keep young people learning.