Cite4Me: Semantic Retrieval and Analysis of Scientific Publications

data types Reasoning Englis guitarists Academic degrees Companies establishe... Cubes Internet radio Loc l authorities ad... Figure 5: Topic coverage of LAK data graph for the individual resources. 5. RELATED WORK Cobo et al.[3] presents an analysis of student participation in online discussion forums using an agglomerative hierarchical clustering algorithm, and explore the profiles to find relevant activity patterns and detect different student profiles. Barber et al. [1] uses a predictive analytic model to prevent students from failing in courses. They analyze several variables, such as grades, age, attendance and others, that can impede the student learning.Kahn et al. [7] present a long-term study using hierarchical cluster analysis, t-tests and Pearson correlation that identified seven behavior patterns of learners in online discussion forums based on their access. García-Solórzano et al. [6] introduce a new educational monitoring tool that helps tutors to monitor the development of the students. Unlike traditional monitoring systems, they propose a faceted browser visualization tool to facilitate the analysis of the student progress. Glass [8] provides a versatile visualization tool to enable the creation of additional visualizations of data collections. Essa et al. [4] utilize predictive models to identify learners academically at-risk. They present the problem with an interesting analogy to the patient-doctor workflow, where first they identify the problem, analyze the situation and then prescribe courses that are indicated to help the student to succeed. Siadaty et al.[13] present the Learn-B environment, a hub system that captures information about the users usage in different softwares and learning activities in their workplace and present to the user feedback to support future decisions, planning and accompanies them in the learning process. In the same way, McAuley et al. [9] propose a visual analytics to support organizational learning in online communities. They present their analysis through an adjacency matrix and an adjustable timeline that show the communication-actions of the users and is able to organize it into temporal patterns. Bramucci et al. [2] presents Sherpa an academic recommendation system to support students on making decisions. For instance, using the learner profiles they recommend courses or make interventions in case that students are at-risk. In the related work, we showed how different perspectives and the necessity of new tools and methods to make data available and help decision-makers. 6. CONCLUSION In this paper we presented the main features of the Cite4Me Web application. Cite4Me makes use of several data sources to provide information for users interested on scientific publications and its applications. Additionally, we provided a general framework on data discovery and correlated resources based on a constructed feature set, consisting of items extracted from reference datasets. It made possible for users, to search and relate resources from a dataset with other resources offered as Linked Data. For more information about the Cite4Me Web application refer to http://www.cite4me.com. 7. REFERENCES [1] R. Barber and M. Sharkey. Course correction: using analytics to predict course success. In Proc. of the 2nd International Conference on Learning Analytics and Knowledge, LAK ’12, pages 259–262, New York, NY, USA, 2012. ACM. [2] R. Bramucci and J. Gaston. Sherpa: increasing student success with a recommendation engine. In Proc. of the 2nd International Conference on Learning Analytics and Knowledge, LAK ’12, pages 82–83, New York, NY, USA, 2012. ACM. [3] G. Cobo, D. García-Solórzano, J. A. Morán, E. Santamaría, C. Monzo, and J. Melenchón. 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