Conception of an E-learning scheme at the University of Algarve

With the proliferation of the Internet use, a growth of e-learning courses has been verified. We arrived to the moment where it is not enough for Universities to have standard courses to offer to the students, because there is an increasing population which tends to choose his formation according to their objectives, styles, needs and learning preferences (the student profile). This way, the universities are faced with a new challenge, which is to offer, together with the standard courses, modules specially tailored to the user desires, based on the identification of the customers needs. In this paper, a model for the distance formation through Internet is discussed, that is being developed in the University of Algarve, which makes possible each individual to learn in agreement with his/her profile

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

Cognitive Neuro-Fuzzy Expert System for Hypotension Control

Hypotension; also known as low blood sugar affect gender of all sort; hypotension is a relative term because the blood pressure normally varies greatly with activity, age, medications, and underlying medical conditions.  Low blood pressure can result from conditions of the nervous system, conditions that do not begin in the nervous system and drugs. Neurologic conditions (condition affecting the brain neurons) that can lead to low blood pressure include changing position from lying to more vertical (postural hypotension), stroke, shock, lightheadedness after urinating or defecating, Parkinson's disease, neuropathy and simply fright. Clinical symptoms of hypotension include low blood pressure, dizziness, Fainting, clammy skin, visual impairment and cold sweat. Neuro-Fuzzy Logic explores approximation techniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditional procedure of the medical diagnosis of hypotension employed by physician is analyzed using neuro-fuzzy inference procedure. The proposed system which is self-learning and adaptive is able to handle the uncertainties often associated with the diagnosis and analysis of hypotension. Keywords: Neural Network, Fuzzy logic, Neuro Fuzzy System, Expert System, Hypotensio

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions

EXCEL through IoT (Exploring Cognitive and Emotional Learning through IOT)

Cognitive Learning is a process that involves learner’s knowledge into consideration. It involves the use of human brain. These days understanding students’s emotional state of mind is one of the research area where student face problems in tackling academic tasks. It has been observed that emotions are a crucial part of students' psychosomatic life, and that they may strongly influence academic motivation, cognitive strategies of learning and achieving the desired results.So, our research is to augment student learning and teacher instruction by giving the real-time reaction of students' state of mind, so that teacher can engage students in the learning processes, help them to learn or use the brain in much and far better way to relate thing with the previous one while learning something new.

Student Performance Predictive Model Mitigating Students' Performance Gap In Outcome-Based Education Systems Using Mathematical Model A Case Study

Outcomc-Based Education (OBE) model is a recurring modern means for education reforrn - a process of improving public education. It embodies the idea that best educational practice is to determine the end goals, or "outcomes", before the strategies, processes, techniques, and other nreans can be put into place to achieve them. While applications of OBE model have been continuously expanding and improving, "performanoe gap" - the gap between what students can do and what they are expected to do - still hinders its potential benefits. Mitigating this gap is among priority tasks of educators to achieve long-term goals of educational reform; and developing student performance predictive models is one way to approach this problem. Most previous studies had targeted big scope of a long-term prediction and most had used various range of educational settings as their inputs, including students' demographic profiles and behavioral contents. They had applied diff[erent techniques in order to predict students' academic performance; however, due to the nattrre of these inputs, all had adopted complex data mining models. This project, instead, was purposely narrowed down to short-term programming cowses at Universiti Teknologi PETRONAS (UTP), Malaysia Main purpose was to design a finctioning short-term predictive model which continuously assised lecturers to analpe patterns and to accurately predict students' upcoming perforrnance and final rezuh in order to provide timely intervention and adjustment. The writer introduced a unique approach by focusing on a simplified set of rnputs including (1) students' courtework breakdown and (2) users' dynamically subjective inputs. Instead of conplex data mining modcls, a straightforward mathematical model was developed and y65 highly customized to best utilize those inputs, which resulted in a high level of accuracy for predictive outputs. A fully developed system from the testrng protot)?e promises to s€rve as a relatively convenient tool for UTP lecturers to rnilize simple yet richly inforrnative coursework data into predicting students' performance, then mitigating the performalrce gap and ultimately achieving set objectives of UTP's OBE systenr

Cardiac arrhythmia classification using self organizing MAP (SOM) - based ensemble model

Many clinical decision support systems have been using data mining techniques for prediction and diagnosis of various diseases with good accuracy. This is due to its ability to distinguish various patterns of data from its background, and make conclusions about the categories of the patterns. A large number of such systems have been widely used in the diagnosis of heart diseases. One of the heart diseases in concern is cardiac arrhythmia. Most systems used in diagnosing cardiac arrhythmia uses data mining techniques, like Artificial Neural Networks, particularly in the form of a single classifier. In this project, a Self Organizing Map (SOM) - Based Ensemble model is proposed for the classification of cardiac arrhythmia disease dataset. An ensemble is a model that applies multiple learning models and combining the outputs or predictions to solve a particular problem. An ensemble is stated to predict or classify datasets more accurately than some single classifier models. The ensemble consists of three SOM classifiers trained with different number of dimension. For the ensemble, a voting technique is used to average the prediction of each single SOM classifier to obtain the final prediction. The results displayed show that the SOM ensemble model has higher classification accuracy than that of single SOM classifiers. Ensemble learning eliminates errors of single classifiers by averaging the prediction of each classifier, thus resulting in a more accurate output

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables