Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions

Context-awareness and user modeling pervasive systems

Today, we are already on the transition from the traditional desktop-based computing technologies towards ubiquitous computing environments that will enfold us in almost all our daily situations and activities. Simultaneously, there exists an increased tendency of putting the user into the center of service delivery. This means that the services in the ubiquitous environments should be adapted to the context, the needs and the preferences of users. Two of the key-concepts, based on which the delivery of ubiquitous personalized services is realized, are context-awareness and user modeling. The emergence of ubiquitous computing and ubiquitous user modeling has created new expectations and challenges for the delivery of personalized services in a considerable amount of application domains, among which is the healthcare domain. Healthcare provision changes direction becoming protective, proactive and more reachable (e.g. at home or at work), accompanied by continuous and persistent provision of personalized high-quality health advice and assistance. Modern healthcare services are expected to be available around the clock, seven days a week and delivered in a personalized manner addressing the specific needs and preferences of each individual. The present dissertation presents a methodology of providing ubiquitous services at advanced telecommunication networks, which combines context-awareness, user modeling and social networks. This methodology was implemented in the healthcare domain and more specifically in anxiety disorders. In particular, in this dissertation, the contextual aspects that are directly associated with anxiety disorders were defined and an ontology-based context model was proposed. In addition, the user models’ structure was determined and the techniques for the processing of their content were developed. Furthermore, the use of social networks in anxiety disorders and the role of their members were studied. Finally, the architecture of a context-aware system that integrates all the above technologies was proposed, a part of which was developed, implemented and evaluated by professional psychiatrists. During the implementation of the proposed methodology in anxiety disorders, the PerMed application that provides medical experts with a tool for archiving and processing the patient’s personal data and four treatment supportive services were developed. The three of them focus on the discovery of possible associations between the patient’s contextual data and the last service aims at predicting the stress level a patient might suffer from, in a given context. The feedback received from expertized psychiatrists was very encouraging and we hope that the proposed approach will constitute a powerful treatment supportive tool for anxiety disorders.Σήμερα, βρισκόμαστε ήδη στο στάδιο μετάβασης από τις παραδοσιακές επιτραπέζιες υπολογιστικές τεχνολογίες στα διάχυτα (ubiquitous) υπολογιστικά περιβάλλοντα που θα μας υποστηρίζουν σχεδόν σε κάθε καθημερινή μας λειτουργία ή δραστηριότητα. Παράλληλα, υπάρχει μία αυξανόμενη τάση για τοποθέτηση του χρήστη στο κέντρο των υπηρεσιών. Αυτό σημαίνει ότι οι υπηρεσίες θα προσαρμόζονται με βάση το στενό και ευρύτερο περιβάλλον διαβίωσης (context), τις ανάγκες και τις προτιμήσεις των χρηστών. Δύο από τις βασικότερες έννοιες στις οποίες βασίζεται η προσφορά διάχυτων εξατομικευμένων υπηρεσιών είναι η γνώση πλαισίου (context awareness) και η μοντελοποίηση χρηστών (user modeling). Η έλευση του διάχυτου υπολογισμού και η χρησιμοποίηση της διάχυτης μοντελοποίησης χρηστών (ubiquitous user modeling) έχει δημιουργήσει νέες προσδοκίες και προκλήσεις για την παροχή εξατομικευμένων υπηρεσιών σε πολλούς τομείς εφαρμογών μεταξύ των οποίων είναι και ο τομέας της υγείας. Η ιατρική αντιμετώπιση αλλάζει πλέον κατεύθυνση και γίνεται προστατευτική, προληπτική και εύκολα προσεγγίσιμη (π.χ. στη δουλειά, στο σπίτι, κλπ.), συνοδευόμενη από συνεχή και εμμένουσα παροχή υψηλής ποιότητας εξατομικευμένης ιατρικής συμβουλής και υποστήριξης. Οι σύγχρονες ιατρικές υπηρεσίες αναμένονται να είναι διαθέσιμες κάθε στιγμή, 7 ημέρες την εβδομάδα και να παρέχονται με έναν εξατομικευμένο τρόπο ώστε να απευθύνονται στις ιδιαίτερες ανάγκες και απαιτήσεις κάθε ατόμου. Η παρούσα διατριβή πραγματεύεται μία μεθοδολογία παροχής διάχυτων υπηρεσιών σε εξελιγμένα τηλεπικοινωνιακά δίκτυα που συνδυάζει τη γνώση πλαισίου, τη μοντελοποίηση χρηστών και τα κοινωνικά δίκτυα (social networks). Η μεθοδολογία αυτή εφαρμόστηκε στον ιατρικό χώρο και ειδικότερα στις διαταραχές άγχους. Πιο συγκεκριμένα, στη διατριβή καθορίστηκαν πλήρως οι παράμετροι πλαισίου που σχετίζονται άμεσα με τις διαταραχές άγχους και προτάθηκε ένα μοντέλο πλαισίου που βασίζεται σε οντολογίες. Επίσης, μελετήθηκε η δομή και οι τεχνικές κατασκευής και ανανέωσης των μοντέλων χρηστών, ενώ μελετήθηκε η χρήση των κοινωνικών δικτύων για την παροχή ιατρικής φροντίδας και οι ρόλοι των μελών τους στις διαταραχές άγχους. Τέλος, προτάθηκε η αρχιτεκτονική ενός συστήματος γνώσης πλαισίου που ενσωματώνει τις ανωτέρω τεχνολογίες, τμήμα του οποίου αναπτύχθηκε, υλοποιήθηκε και αξιολογήθηκε από επαγγελματίες ιατρούς. Κατά την εφαρμογή της παραπάνω μεθοδολογίας στις διαταραχές άγχους αναπτύχθηκε η εφαρμογή PerMed που αποτελεί ένα εργαλείο αρχειοθέτησης και επεξεργασίας των προσωπικών πληροφοριών των ασθενών και τέσσερις υπηρεσίες που στοχεύουν στην υποστήριξη της θεραπείας των διαταραχών άγχους. Οι τρεις εστιάζουν στην ανακάλυψη πιθανών συσχετίσεων στα δεδομένα πλαισίου, ενώ η τέταρτη στοχεύει στην πρόβλεψη του άγχους που θα παρουσιάσει ένας ασθενής σε ένα δεδομένο πλαίσιο. Τα σχόλια που λάβαμε από επαγγελματίες ψυχιάτρους είναι αρκετά ενθαρρυντικά και ευελπιστούμε ότι η προτεινόμενη προσέγγιση θα αποτελέσει ένα ισχυρό εργαλείο υποστήριξης της θεραπείας των διαταραχών άγχους

Forecasting Air Flight Delays and Enabling Smart Airport Services in Apache Spark

Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceIn light of the rapidly growing passenger and flight volumes, airports seek for sustainable solutions to improve passengers’ experience and comfort, while maximizing their profits. A major technological solution towards improving service quality and management processes in airports comprises Internet of Things (IoT) systems that realize the concept of smart airports and offer interconnection potential with other public infrastructures and utilities of smart cities. In order to deliver smart airport services, real-time flight delay data and forecasts are a critical source of information. This paper introduces an essential methodology using machine learning techniques on Apache Spark, a cloud computing framework, with Apache MLlib, a machine learning library to develop and implement prediction models for air flight delays that could be integrated with information systems in order to provide up-to-date analytics. The experimental results have been implemented with various algorithms in terms of classification as well as regression, thus manifesting the potential of the proposed framework

RF-EMF Exposure Assessments in Greek Schools to Support Ubiquitous IoT-Based Monitoring in Smart Cities

Everyday living environments concentrate a growing amount of wireless communications leading to increased public concern for radiofrequency (RF) electromagnetic fields (EMF) exposure. Recent technological advances are turning the focus on Internet of Things (IoT) systems that enable automated and continuous real-time EMF monitoring, facing however several challenges mainly stemming from infrastructural costs. This paper seeks to provide a comprehensive view of RF-EMF levels in Greece and evidence-based decision support for a spatially prioritized deployment of an IoT RF-EMF monitoring system. We applied the stratified sampling method to estimate Electric Field Strength (EFS) in the 27MHz-3GHz range in 661 schools. Three different residential areas were considered, i.e. urban, semi-urban and rural. Results showed that the 95% confidence interval for the EFS is (0.40, 0.44) with central value equal to the sample mean 0.42 V/m. We obtained strong evidence that the mean EFS value for all Greek schools is 0.42, which is 52 times lower than the Greek safety limit and equal to 1% of international limits. Mean EFS values of individual residential areas were also significantly below safety limits. Rural areas displayed the highest EFS peaks comprising the strongest candidate to start the deployment of an IoT RF-EMF monitoring system from

Applying Machine Learning to Predict Whether Learners Will Start a MOOC After Initial Registration

Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceOnline learning has developed rapidly in the past decade, leading to increased scientific interest in e-learning environments. Specifically, Massive Open Online Courses (MOOCs) attract a large number of people with respective enrollments meeting an exponential growth during the COVID-19 pandemic. However, only a small number of enrolled learners successfully complete their studies creating an interest in early prediction of dropout. This paper presents the findings of a study conducted during a MOOC for smart city professionals, in which we analyzed demographic and personal information on their own and in tandem with a small set of interaction data between learners and the MOOC, in order to identify factors influencing the decision of starting the MOOC or not. We also applied different models for predicting whether a person previously registered to a MOOC will eventually start it or not, as well as for identifying the most informative attributes for the prediction process. Results show that prediction reached 85% accuracy based only on the number of the first days’ logins in the MOOC and few demographic data such as current job role or occupation and number of study hours that the learner estimates he/she can devote on a weekly basis. This information can be exploited by MOOC providers to implement learner engagement strategies in a timely fashion

Requirements and Solutions for Advanced Telemedicine Applications

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