Abstract

At the end of the first decade of the 21st century, the rapid growth in the MOOC offering brought about a new educational landscape, posing new challenges to teaching and learning, mainly due to massive participation, ubiquity and free enrollment. These courses embody a confluence of technological and pedagogical mediations yet to be fully explored. This study, applying an instrument of educational and interactive indicators on a MOOC (INdiMOOC-EdI) designed by the authors, seeks to ascertain the pedagogical components of MOOCs delivered in the Spanish language in order to establish which course features are platform dependent. An exploratory sequential mixed methods study was carried out on a total of 117 courses within 10 different platforms via an instrument developed and validated ad hoc. The data obtained is subjected to a content analysis for a qualitative perspective; while a quantitative perspective is obtained through a statistical analysis complemented with classification algorithms typical in data mining. The results suggest that the different platform providers condition the pedagogical design of the MOOC in five key areas: learning, activities and tasks, means and resources, interactivity and assessment. It concludes with a series of descriptive, educational and interactive indicators that can be a pedagogical benchmark for future MOOCs.

Abstract

Air pollutants affect both human health and the environment. For this reason, environmental managers and urban planners focus their efforts in monitoring air pollution. In this context, complete information is required to support the decision-making process to improve the quality of life in urban zones. Hence, it is important to extract knowledge not only on concentration levels but associations between air pollutants. Based on the Cross-industry standard process for data mining, this paper presents an approach which leads to identify correlations and incidence between the most harmful pollutants in the Andean Region: Ozone, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide and, Particulate material. This paper describes an experiment using a real dataset from a monitoring station in Cuenca, Ecuador located in the Andean region.  The results show that the proposed approach is effective to extract knowledge useful to support the evaluation of air quality in urban zones. In addition, this approach provides a starting point for future data mining applications for the analysis of air pollution in the context of the Andean region.

Abstract

The detection of outliers in the field of data mining (DM) and the process of knowledge discovery in databases (KDD) is of great interest in areas that require support systems for decision making. A straightforward application can be found in the financial area, where DM can potentially detect financial fraud or find errors produced by the users. Thus, it is essential to evaluate the veracity of the information, through the use of methods for the detection of unusual behaviors in the data. This paper proposes a method to detect values ​​that are considered outliers in a database of nominal type data. The method implements a global algorithm of "k" closest neighbors, a clustering algorithm called k-means and a statistical method called chi-square. These techniques have been implemented on a database of clients who have requested a financial credit. The experiment was performed on a data set with 1180 tuples, where, outliers were deliberately introduced. The results showed that the proposed method is able to detect all the outliers entered.

Abstract

The volume of air traffic in the National Air Space has been growing at a very fast pace during recent years, and increasing demand for air travel in coming years is predicted. Strategic Planning is a necessary tool to guarantee a safe and efficient increase of Air Traffic. An Aggregate Model for Strategic Planning of Air Traffic Management is the framework of this project.Aggregate models require the creation of a network that is representative of the expected flow and which will serve as a platform to solve flow optimization problems. We have integrated an automatic method for route clustering - Smax method - to generate the required network. Automatic clustering is necessary in order to efficiently cluster many individual scenarios.Some available alternatives to determine the number of clusters are tested. The studied alternatives provide success rates that oscillates between 49% and 68%. In addition to the relatively low success rates, these methods require one user-input parameter, and they are highly sensitive to it.A different method, based on the Silhouette Score and the Dip Test measures, is developed. The Smax method requires no user-input parameters, and it consistently provides rates of success that approximately oscillate between the 72% and the 81%.The presented clustering approach noticeably improves the rate of correct clustering cases for the specific scenario of route clustering.

Abstract

Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. Document type: Article

Abstract

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hours separately. The peak-hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off-peak-hour dwell times are estimated using a non-parametric regression model, in particular, the k-nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%-88.5% during peak hours and 80.1% during off-peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible.

Abstract

We offer some thoughts on the need for the field of open data to evolve from the production to exploitation stage. We detail the process of generating a simple display: knowledge of sources and data extraction, local analysis or in the cloud, and using the tools needed to present the data. Information is offered on free tools readily available on the Net that an information specialist should know. Finally, knowledge acquisition in the field of data is reinterpreted, and illustrated with an example, London: the information capital, that provides advanced visualizations based on open data.

Abstract

Data mining is the process of analyzing large data sets to discover behaviour patterns and predict future actions. Data mining has many useful applications, but also has an exploratory approach that makes questionable the validity of certain deductions. Using personal information for predictive purposes has a direct impact on the lives of people and therefore requires acting in a framework of liability. A code of ethics is necessary.