Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs

Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances

Improving the portability of predicting students’ performance models by using ontologies

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models’ excessive dependence on the low-level attributes used to train them, which reduces the models’ portability. To solve this issue, the use of high-level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students’ interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area

The relationship between mindfulness and emotional intelligence as a protective factor for healthcare professionals: systematic review

Emotional intelligence is an essential trait and skill for healthcare professionals. Mind fulness meditation has proved to be effective in increasing the wellbeing of those who practice it, leading to better mental health, self-care and job satisfaction. This paper aims to identify the recent evidence on the relationship between mindfulness and emotional intelligence among healthcare professionals and students. A systematic review was conducted including the databases PubMed, Cinhal, PsycINFO and Web of Science. The main variables were emotional intelligence skills and mindfulness practice. Data were extracted according to the following outcomes: authors, year of publication, country, study design, participants, mindfulness training intervention, tools used in data collection and main results. The following inclusion criteria were applied: peer-reviewed articles; published in English or Spanish; published between 2010 and 2020; quantitative methodology; a study population of healthcare professionals or students; the relationship with the aim of the study. The Joanna Briggs Institute criteria were followed for assessing the methodological quality of the selected studies. Three researchers were involved in the review. After the selection process, 10 studies were selected out of the 197 references initially identified. These studies revealed a positive relation ship between mindfulness and emotional intelligence, particularly the capacity to regulate emotions. Furthermore, mindfulness is negatively related to emotional exhaustion. Training interventions based on mindfulness have proved to be useful in promoting emotional balance, emotional awareness, emotional acceptance, emotion recognition, expressive suppression and a reduction in emotional exhaustion. This study could serve as a basis for further research on the benefits of emotional intelligence and practicing mindfulness for the bio-psycho-social welfare of healthcare professionals

Rhizobial Volatiles: Potential New Players in the Complex Interkingdom Signaling With Legumes

Bacteria release a wide range of volatile compounds that play important roles in intermicrobial and interkingdom communication. Volatile metabolites emitted by rhizobacteria can promote plant growth and increase plant resistance to both biotic and abiotic stresses. Rhizobia establish beneficial nitrogen-fixing symbiosis with legume plants in a process starting with a chemical dialog in the rhizosphere involving various diffusible compounds. Despite being one of the most studied plant-interacting microorganisms, very little is known about volatile compounds produced by rhizobia and their biological/ecological role. Evidence indicates that plants can perceive and respond to volatiles emitted by rhizobia. In this perspective, we present recent data that open the possibility that rhizobial volatile compounds have a role in symbiotic interactions with legumes and discuss future directions that could shed light onto this area of investigation

Application of reverse vaccinology for the identification of epitope candidates from Rickettsia rickettsii

643-647Rocky mountain spotted fever is a severe disease caused by Rickettsia rickettsii that frequently causes the death of the patients. As there are not effective vaccines for this disease, we employed reverse vaccinology to find epitope candidates useful for vaccine development. To apply this bioinformatics, we used the following online software: ProPred1, RANKPEP, and HLA binding, to evaluate 143 amino acid sequences in the genome of Rickettsia rickettsii (NC_009882 Sheila Smith). This strategy allowed us to identify 19 epitope sequences with affinity to HLA I alleles: A0201, A24; HLA-B: B3501, B3901

Application of reverse vaccinology for the identification of epitope candidates from Rickettsia rickettsii

Rocky mountain spotted fever is a severe disease caused by Rickettsia rickettsii that frequently causes the death of the patients. As there are not effective vaccines for this disease, we employed reverse vaccinology to find epitope candidates useful for vaccine development. To apply this bioinformatics, we used the following online software: ProPred1, RANKPEP, and HLA binding, to evaluate 143 amino acid sequences in the genome of Rickettsia rickettsii (NC_009882 Sheila Smith). This strategy allowed us to identify 19 epitope sequences with affinity to HLA I alleles: A0201, A24; HLA-B: B3501, B3901

Oesophageal varices predict complications in compensated advanced non-alcoholic fatty liver disease

Background & Aims: We aimed to evaluate the impact of oesophageal varices (OV) and their evolution on the risk of complications of compensated advanced chronic liver disease (cACLD) caused by non-alcoholic fatty liver disease (NAFLD). We also assessed the accuracy of non-invasive scores for predicting the development of complications and for identifying patients at low risk of high-risk OV. Methods: We performed a retrospective assessment of 629 patients with NAFLD-related cACLD who had baseline and follow-up oesophagogastroduodenoscopy and clinical follow-up to record decompensation, portal vein thrombosis (PVT), and hepatocellular carcinoma. Results: Small and large OV were observed at baseline in 30 and 15.9% of patients, respectively. The 4-year incidence of OV from absence at baseline, and that of progression from small to large OV were 16.3 and 22.4%, respectively. Diabetes and a ≥5% increase in BMI were associated with OV progression. Multivariate Cox regression revealed that small (hazard ratio [HR] 2.24, 95% CI 1.47–3.41) and large (HR 3.86, 95% CI 2.34–6.39) OV were independently associated with decompensation. When considering OV status and trajectories, small (HR 2.65, 95% CI 1.39–5.05) and large (HR 4.90, 95% CI 2.49–9.63) OV at baseline and/or follow-up were independently associated with decompensation compared with the absence of OV at baseline and/or follow-up. The presence of either small (HR 2.8, 95% CI 1.16–6.74) or large (HR 5.29, 95% CI 1.96–14.2) OV was also independently associated with incident PVT. Conclusion: In NAFLD-related cACLD, the presence, severity, and evolution of OV stratify the risk of developing decompensation and PVT. Impact and implications: Portal hypertension is the main driver of liver decompensation in chronic liver diseases, and its non-invasive markers can help risk prediction. The presence, severity, and progression of oesophageal varices stratify the risk of complications of non-alcoholic fatty liver disease. Easily obtainable laboratory values and liver stiffness measurement can identify patients at low risk for whom endoscopy may be withheld, and can also stratify the risk of liver-related complications