Raquel Martínez-España

Analysis of student behavior in learning management systems through a Big Data framework

Abstract In recent years, learning management systems (LMSs) have played a fundamental role in higher education teaching models. A new line of research has been opened relating to the analysis of student behavior within an LMS, in the search for patterns that improve the learning process. Current e-learning platforms allow for recording student activity, thereby enabling the exploration of events generated in the use of LMS tools. This paper presents a case study conducted at the Catholic University of Murcia, where student behavior in the past four academic years was analyzed according to learning modality (that is, on-campus, online, and blended), considering the number of accesses to the LMS, tools employed by students and their associated events. Given the difficulty of managing the large volume of data generated by users in the LMS (up to 70 GB in this study), statistical and association rule techniques were performed using a Big Data framework, thus speeding up the statistical analysis of the data. The obtained results are demonstrated using visual analytic techniques, and evaluated in order to detect trends and deficiencies in the use of the LMS by students.

Deep Learning Approach for Classifying Papanicolau Cervical Smears

Cervical cancer is the third neoplasm in frequency worldwide between women. Screening techniques in general population have demonstrated clear effectiveness as its implementation has decreased cervical cancer incidence and mortality more than 70% in several countries. This benefit is related with detection of early pre-malignant asymptomatic lesions, that can be treated to avoid their progression to invasive cancer. Papanicolau cervical smear is the most common cancer screening technique worldwide used since described by Giorgios Papanicolau on 1928. Sampling techniques have been improved in last years, based on simplifying and automatizing procedures. However, after preparing the samples, an expert review of the microscopic images is needed. There are few automatic diagnostic methods published, but their results are not as good as an expert examination. In this paper, we develop a microscopic cervical cells database using Papanicolau cervical smears from our patients, sampled few minutes before performing a cone biopsy on them. With this procedure, we have both the cervical smear and the biopsy diagnostics, tagged as Gold Standard. Then, a deep-learning methodology is performed for the automatic categorization of pre-malignant and benign cervical cells. We use the the Caffe deep-learning framework to leverage NVIDIA GPU computing architectures to deal with this real patient database in a reduced time-frame. Our results reveal the deep learning methodology is robust in this biomedical classification, reaching up to 78% Accuracy.

A More Realistic K-Nearest Neighbors Method and Its Possible Applications to Everyday Problems

Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous and noisy data, that is, imperfect information.Although this imperfect information is inevitable, most applications have traditionally ignored the need for developing appropriate approaches for representing and reasoning with such data imperfections. The soft computing field has dealt with the development of techniques that can work with this kind of information as discipline whose main characteristic is tolerance to inaccuracy and uncertainty.In this work, we extend the k-Nearest Neighbors technique using concepts and methods provided by Soft Computing. The aim is to carry out the processes of instance selection and classification in everyday problems from imperfect information making the technique more realistic.

Bioinspired Architecture Selection for Multitask Learning

Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL), which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.