Transfer-Learning Methods in Programming Course Outcome Prediction

The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in educational contexts, differences between courses set huge challenges for the generalizability of these methods. For example, traditional machine-learning methods assume identical distribution in all data—in our terms, traditional machine-learning methods assume that all teaching contexts are alike. In practice, data collected from different courses can be very different as a variety of factors may change, including grading, materials, teaching approach, and the students. Transfer-learning methodologies have been created to address this challenge. They relax the strict assumption of identical distribution for training and test data. Some similarity between the contexts is still needed for efficient learning. In this work, we review the concept of transfer learning especially for the purpose of predicting the outcome of an introductory programming course and contrast the results with those from traditional machine-learning methods. The methods are evaluated using data collected in situ from two separate introductory programming courses. We empirically show that transfer-learning methods are able to improve the predictions, especially in cases with limited amount of training data, for example, when making early predictions for a new context. The difference in predictive power is, however, rather subtle, and traditional machine-learning models can be sufficiently accurate assuming the contexts are closely related and the features describing the student activity are carefully chosen to be insensitive to the fine differences.Peer reviewe

Machine learning for the prediction of psychosocial outcomes in acquired brain injury

Acquired brain injury (ABI) can be a life changing condition, affecting housing, independence, and employment. Machine learning (ML) is increasingly used as a method to predict ABI outcomes, however improper model evaluation poses a potential bias to initially promising findings (Chapter One). This study aimed to evaluate, with transparent reporting, three common ML classification methods. Regularised logistic regression with elastic net, random forest and linear kernel support vector machine were compared with unregularised logistic regression to predict good psychosocial outcomes after discharge from ABI inpatient neurorehabilitation using routine cognitive, psychometric and clinical admission assessments. Outcomes were selected on the basis of decision making for care packages: accommodation status, functional participation, supervision needs, occupation and quality of life. The primary outcome was accommodation (n = 164), with models internally validated using repeated nested cross-validation. Random forest was statistically superior to logistic regression for every outcome with areas under the receiver operating characteristic curve (AUC) ranging from 0.81 (95% confidence interval 0.77-0.85) for the primary outcome of accommodation, to its lowest performance for predicting occupation status with an AUC of 0.72 (0.69-0.76). The worst performing ML algorithm was support vector machine, only having statistically superior performance to logistic regression for one outcome, supervision needs, with an AUC of 0.75 (0.71-0.80). Unregularised logistic regression models were poorly calibrated compared to ML indicating severe overfitting, unlikely to perform well in new samples. Overall, ML can predict psychosocial outcomes using routine psychosocial admission data better than other statistical methods typically used by psychologists

Explainable AI-based identification of contributing factors to the mood state change in children and adolescents with pre-existing psychiatric disorders in the context of COVID-19-related lockdowns in Greece

The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state

A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

Computational Analysis of Developmental Disorders in Children

Early developmental disorders are common in children between the ages of 3 through 17. These developmental disorders begin at early ages and affect the day-to-day activities of children. These disorders also impact the growth and lifestyle of children. Most of the time these developmental disorders co-exist in children. The main focus of our research lies in Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, Deletion syndrome (22q) and their co-occurrences. Most child psychologists and pediatricians diagnose these disorders in children through parent-based surveys. Our research uses three different parent-based reports: (1) Autism Diagnostic Interview (ADI), (2) Behavioral Assessment Schedule for Children (BASC), and (3) Vineland Adaptive Behavior Scales. These reports are questionnaires filled by parents under the inspection of certified professionals. These examinations require substantial amount of time and yield results after at least 13 months of wait time; hence, there is a pressing need to expedite the disorder detection process. Here, we address this challenge by utilizing machine learning techniques. We utilize Machine learning to parent-reviews to help understand the relevance and importance of parental assessments in diagnosing these disorders. Furthermore, we study the co-occurrence of these disorders and identify their indicators in parental-surveys using a variety of machine learning techniques. Our main objective is to determine whether one can accurately predict the occurrence of these disorders

Course outcome prediction with transfer learning methods

In computer science, introductory programming course is one of the very first courses taken. It sets the base for more advanced courses as programming ability is usually assumed there. Finding the students that are likely to fail the course allows early intervention and more focused help for them. This can potentially lower the risk of dropping out in later studies, because of the lack of fundamental skills. One measure for programming ability is the outcome of a course and the prediction of these outcomes is the focus also in this thesis. In educational context, differences between courses set huge challenges for traditional machine learning methods as they assume identical distribution in all data. Data collected from different courses can have very different distributions as there are many factors that can change even between consecutive courses such as grading, contents, and platform. To address this challenge transfer learning methods can be used to as they make no such assumption about the distribution. In this thesis, one specific transfer learning algorithm, TrAdaBoost, is evaluated against selection of traditional machine learning algorithms. Methods are evaluated using real-life data from two different introductory programming courses, where contents, participants and grading differ. Main focus is to see how these methods perform in the first weeks of the course that are educationally the most critical moments

Inclusion of children identified as having special educational needs (SEN) within the Austrian compulsory educational system

Este artigo pretende descrever vários aspetos da segregação, integração e inclusão dentro do sistema educativo obrigatório austríaco. Após uma introdução que contém definições e um resumo internacional, será brevemente descrito o sistema educativo da Áustria. Subsequentemente, três temas de interesse serão debatidos. Primeiro será caracterizada a situação atual referente à inclusão de crianças com necessidades educativas especiais. Esta situação difere bastante entre estados federais. Serão descritos os desenvolvimentos que conduziram à situação atual. Em segundo serão apresentados estudos empíricos recentes relativos ao movimento austríaco que almeja a inclusão. Em terceiro e a título exemplificativo serão apresentados em detalhe alguns desenvolvimentos recentes, alguns planos e visões futuras de três estados federais austríacos.Abstract The present paper aims at describing various aspects of segregation, integration and inclusion within the Austrian compulsory school system. After a general introduction which includes definitions and a short international overview, the Austrian compulsory school system is described shortly. Subsequently, three subjects of interest are discussed. Firstly, the current situation of inclusion of children identified as having special educational needs (SEN) is characterized. This is very different in each one of the federal states, and the various developments which led to this situation are described. Secondly, recent empirical studies on the Austrian movement towards inclusion are shown in a brief overview. Thirdly, by way of example, recent developments, further plans and future visions of three federal states of Austria are presented in more detail.Résumé Cet article prétend décrire les différents aspects de la ségrégation, intégration et inclusion dans le système éducatif obligatoire autrichien. Après une introduction générale qui comprend des définitions et une perspective internationale, nous décrirons le système éducatif en Autriche. Ensuite, trois thèmes d’intérêt seront discutés. En premier lieu, la situation actuelle d’inclusion des enfants ayant des besoins éducatifs particuliers sera décrite. Cette situation diffère dans chacun des états fédéraux; les diverses conditions qui conduisent à la situation actuelle seront présentées. Deuxièmement, de récentes études empiriques qui abordent le mouvement vers l’inclusion seront présentées. Troisièmement et à titre d’exemple, les développements récents, plans et visions futures de trois états fédéraux seront présentés plus en détail

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine