A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction
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paper_title: Exploiting Academic Records for Predicting Student Drop Out: a case study in Brazilian higher education

paper_content:

Students’ dropout is a major concern of the Brazilian higher education institutions as it may cause waste of resources and decrease graduation rates. The early detection of students with high probability of dropping out, as well as understanding the underlying causes, are crucial for defining more effective actions toward preventing this problem. In this paper, we cast the dropout detection problem as a classification problem. We use a large sample of academic records of students across 76 courses from a public university in Brazil in order to derive and select informative features for the employed classifiers. We create two classification models that either consider the course to which the target student is formally committed or not consider it, respectively. We contrast both models and show that not considering the course leads to better results than considering it.
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paper_title: Data-driven system to predict academic grades and dropout

paper_content:

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.
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paper_title: Mining Videos from the Web for Electronic Textbooks

paper_content:

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.
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paper_title: Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review

paper_content:

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.
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paper_title: To what extend can we predict students' performance? A case study in colleges in South Africa

paper_content:

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.
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paper_title: A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

paper_content:

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.
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paper_title: A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs

paper_content:

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.
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paper_title: Smart School Multimodal Dataset and Challenges

paper_content:

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.
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paper_title: Time Series Clustering of Moodle Activity Data

paper_content:

24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), University College Dublin, Dublin, Ireland, 20-21 September 2016
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paper_title: Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

paper_content:

This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.
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paper_title: A Review on Predicting Student's Performance Using Data Mining Techniques☆

paper_content:

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.
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paper_title: Introduction to Probabilistic Graphical Models

paper_content:

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.
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paper_title: A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs

paper_content:

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.
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paper_title: DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

paper_content:

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.
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paper_title: Forum Thread Recommendation for Massive Open Online Courses

paper_content:

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.
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paper_title: A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

paper_content:

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.
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Title: A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction

Section 1: Introduction
Description 1: In this section, introduce the issue of student dropout rates and its impact, particularly in developing countries, and discuss the need for machine learning approaches to address this challenge.

Section 2: Method of Study
Description 2: Describe the methodology used in surveying the literature, including the types of sources consulted, the search keywords, the publication periods considered, and the types of texts searched.

Section 3: Machine Learning in Education
Description 3: Provide an overview of how machine learning has been applied in the field of education, particularly to improve educational quality, student learning outcomes, and to develop early warning systems.

Section 4: Machine Learning Techniques on Addressing Student Dropout
Description 4: Detail various machine learning techniques that have been employed to predict student dropout, including supervised and unsupervised learning methods, survival analysis, matrix factorization, and deep neural networks.

Section 5: Open Challenges for Future Research
Description 5: Highlight the existing gaps and limitations in the research on machine learning for student dropout prediction, particularly in developing countries, and discuss the future research directions that need to be undertaken.

Section 6: Conclusions
Description 6: Summarize the key findings of the survey, including the current state of research, ignored issues such as data imbalance, the need for comprehensive early warning systems, and the importance of considering school-level datasets.