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            "abstractNote": "Present MOOC and SPOC platforms do not provide teachers with precise metrics that represent the effectiveness of students with educational resources and activities. This work proposes and illustrates the application of the Precise Effectiveness Strategy (PES).\nPES is a generic methodology for defining precise metrics that enable calculation of the effectiveness of students when interacting with educational resources and activities in\nMOOCs and SPOCs, taking into account the particular aspects of the learning context. PES has been applied in a case study, calculating the effectiveness of students when watching video lectures and solving parametric exercises in four SPOCs deployed in the Khan Academy platform. Different visualizations within and between courses are presented\ncombining the metrics defined following PES. We show how these visualizations can help teachers make quick and informed decisions in our case study, enabling the whole comparison of a large number of students at a glance, and a quick comparison of the four SPOCs divided by videos and exercises. Also, the metrics can help teachers know the\nrelationship of effectiveness with different behavioral patterns. Results from using PES in the case study revealed that the effectiveness metrics proposed had a moderate negative\ncorrelation with some behavioral patterns like recommendation listener or video avoider.",
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            "abstractNote": "The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In\nthis paper, we describe ALAS-KA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-KA, which have not been available previously in the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-KA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-KA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos III de Madrid (UC3M)\nand were taken by more than 300 students.",
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            "title": "Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning",
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            "abstractNote": "Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.",
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