Abstract
Research concerning mining data from learning management systems have been consistently been appearing in the literature. However, in many situations there is not a clear path on the data mining procedures that lead to solid conclusions. Therefore, many studies result in ad-hoc conclusions with insufficient generalization capabilities. In this article, we describe a methodology and report our findings in an experiment which one online course which involved more than 150 students. We used the Moodle LMS during the period of one academic semester, collecting all the interactions between the students and the system. These data scales up to more than 33K records of interactions where we applied data mining tools following the procedure for data extraction, cleaning, feature identification and preparation. We then proceeded to the creation of automatic learning models based on decision trees, we assessed the models and validate the results by assessing the accuracy of the predictions using traditional metrics and draw our conclusions on the validity of the process and possible alternatives1. © 2017 Association for Computing Machinery.

Abstract
Predicting whether a student will pass or fail is one of the most important actions to take while giving lectures. Usually, the experienced teacher is able to detect problematic situations at early stages. However, this is only true for classes up to a hundred students. For bigger ones, automatic methods are needed. In this paper, we present a predictive system based on three criteria retrieved and computed from the logs of the learning management system. We built fast frugal decision trees to help predict and prevent student failures, using data retrieved from their resource usage patterns. Evaluation of the decision system shows that the system's accuracy is very high both in train and test phases, surpassing logistic regression and CART. © 2017 IEEE.

Abstract
Annotated data is one of the most important components for supervised learning tasks. To ensure the reliability of the models, this data is usually labeled by several human annotators through volunteering or using Crowdsourcing platforms. However, such approaches are unfeasible (regarding time and cost) in datasets with an enormous number of entries, which in the specific case of journalistic relevance detection in social media posts, is necessary due to the wide scope of topics that can be considered relevant. Therefore, with the goal of building a relevance detection model, we propose an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets). This methodology is based on the predictability of the content in Twitter accounts. Next, we used the retrieved dataset and build relevance detection models, combining text, entities, and sentiment features. Finally, we validated the best model through a smaller manually annotated dataset with posts from Facebook and Twitter. The F1-measure achieved in the validation dataset was 63% which is still far from excellent. However, given the characteristics of the validation data, these results are encouraging since 1) our model is not affected by content from other social networks and 2) our validation dataset was restrained to a specific time interval and specific keywords (which can affect the performance of the model). © 2017 IEEE.

Abstract
The organizational impacts of adopting social media have been on the top key concerns of organizations entering these environments. Organizations are, in fact, allocating time, effort, skills, human resources and technology and this raises the constant need to measure the ROI and legitimize the use of social media in the context of organizational development. However, how can organizations attempt to measure the efficiency and return on investments on a social media content approach that has not been strategically designed? In this paper, we report on previous research which we have further developed into a more comprehensive and solid analysis of types of social media content strategies that are being implemented in the Higher Education Sector, using clustering to group analogue content strategies and social media KPI to measure the efficiency of each of the main i. This work is based on a previously proposed editorial model for the design of social media content strategies for Higher Education Institutions, and results show which are the most relevant strategic areas of communication and corresponding return, in terms of publics' engagement, that organizations can obtain. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Social networks can often be considered the main stage of news, so detecting newsworthy information in this media is a relevant subject of study. Labeling automatically messages shared in social networks is an area of study that can be used directly to detect newsworthy information or to serve as training data for other projects. The solution presented in this work is to use the news as the base knowledge for the classification of messages. The results of this application were promising, with an accuracy of over 90% in detecting news related messages in our datasets. © 2017 IEEE.

Abstract
Measuring the return on communication investments on social media has become one of the top key issues for organizations joining social networks. However, this field has been lacking articulation between what is conveyed as social media key performance indicators and the alignment of strategic organizational goals. Therefore, we propose a methodology to measure the performance of each organization on social media, to determine their positioning in the sector and to evaluate which are the content strategies used to boost the highest performing organizations. Thus, we identify how to determine which organizations should be closely monitored within the sector and which type content strategies can foster higher organizational performance on social media. © 2017 Copyright is held by the owner/author(s).