School-leavers' Transition to Tertiary Study: a Literature Review.

The theoretical and empirical literature relating to factors and problems in the transition of students from secondary to tertiary level education is reviewed here. Studies on persistence and attrition, and on the analysis and prediction of academic performance of students, generally and in particular discipline areas, are included.Transition to university; student performance.

Student dropout risk detection at University of Évora

Currently, student dropout is a global problem in higher education affecting the results of education systems. In addition to providing state-of-the-art education, any institution needs to maintain its student flow rate, which means that predicting dropout is critical to measuring the success of an education system. This work focuses on identifying the risk of dropout at the University of Évora based on students’ academic performance. We propose a set of aca- demic information as predictive attributes and present machine learning models that have a precision of 96.8% and f1-measure of 94.8% as perfor- mance in identifying students at risk of dropping out. In this regard, 13 years of academic data were collected from four different academic programs (the academic years 2006/2007 to 2018/2019 and Man- agement, Biology, Informatics Engineering and Nursing programs). After collecting the students’ academic records, anonymizing the information and pre-processing the data, an engineering and attribute selection process was conducted, building the data sets. Various machine learning algorithms were applied and their performance was compared; models were built with Deci- sion Trees (DT), Naïve Bayes (NB), Support Vector Machines (SVM) and Random Forest (RF), with the latter algorithm having obtained the best performance in terms of recall; Sumário: Detecção de Risco de Abandono de Alunos na Universidade de Évora Atualmente, o abandono escolar é um problema global no ensino superior que afeta os resultados dos sistemas educativos. Além de fornecer educação de ponta, qualquer instituição precisa manter a taxa de fluxo de alunos, o que significa que a previsão do abandono escolar é essencial para medir o sucesso de um sistema de ensino. Este trabalho centra-se na identificação do risco de abandono escolar na Uni- versidade de Évora com base no desempenho escolar dos alunos. Propomos um conjunto de informação académica como atributos preditivos e apresen- tamos modelos de aprendizagem automática que apresentam uma precisão de 96.8% e f1-medir de 94.8% como desempenho na identificação de alunos em risco de desistência. Nesse sentido, foram recolhidos 13 anos de dados académicos de quatro cursos diferentes (anos letivos de 2006/2007 a 2018/2019 e cursos de Gestão, Bi- ologia, Engenharia Informática e Enfermagem). Após a recolha do percurso académico dos alunos, a anonimização da informação e o pré-processamento dos dados, foi conduzido um processo de engenharia e seleção de atributos, construindo assim os conjuntos de dados. Foram aplicados vários algoritmos de aprendizagem automática e o seu desempenho foi comparado; foram con struídos modelo com Árvores de Decisão (DT), Naïve Bayes (NB), Máquinas de Vetores de Suporte (SVM) e Random Forest (RF), tendo este último al- goritmo obtido o melhor desempenho no que respeita à cobertura

Latent deep sequential learning of behavioural sequences

The growing use of asynchronous online education (MOOCs and e-courses) in recent years has resulted in increased economic and scientific productivity, which has worsened during the coronavirus epidemic. The widespread usage of OLEs has increased enrolment, including previously excluded students, resulting in a far higher dropout rate than in conventional classrooms. Dropouts are a significant problem, especially considering the rising proliferation of online courses, from individual MOOCs to whole academic programmes due to the pandemic. Increased efficiency in dropout prevention techniques is vital for institutions, students, and faculty members and must be prioritised. In response to the resurgence of interest in the student dropout prediction (SDP) issue, there has been a significant rise in contributions to the literature on this topic. An in-depth review of the current state of the art literature on SDP is provided, with a special emphasis on Machine Learning prediction approaches; however, this is not the only focus of the thesis. We propose a complete hierarchical categorisation of the current literature that correlates to the process of design decisions in the SDP, and we demonstrate how it may be implemented. In order to enable comparative analysis, we develop a formal notation for universally defining the multiple dropout models examined by scholars in the area, including online degrees and their attributes. We look at several other important factors that have received less attention in the literature, such as evaluation metrics, acquired data, and privacy concerns. We emphasise deep sequential machine learning approaches and are considered to be one of the most successful solutions available in this field of study. Most importantly, we present a novel technique - namely GRU-AE - for tackling the SDP problem using hidden spatial information and time-related data from student trajectories. Our method is capable of dealing with data imbalances and time-series sparsity challenges. The proposed technique outperforms current methods in various situations, including the complex scenario of full-length courses (such as online degrees). This situation was thought to be less common before the outbreak, but it is now deemed important. Finally, we extend our findings to different contexts with a similar characterisation (temporal sequences of behavioural labels). Specifically, we show that our technique can be used in real-world circumstances where the unbalanced nature of the data can be mitigated by using class balancement technique (i.e. ADASYN), e.g., survival prediction in critical care telehealth systems where balancement technique alleviates the problem of inter-activity reliance and sparsity, resulting in an overall improvement in performance

SAFE: A Neural Survival Analysis Model for Fraud Early Detection

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201