Analítica de aprendizaje y personalización
| Main Author: | Zapata-Ros, Miguel |
|---|---|
| Format: | Preprint PeerReviewed Book |
| Bahasa: | es |
| Terbitan: |
, 2013
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| Subjects: | |
| Online Access: |
http://eprints.rclis.org/19490/1/anal%C3%ADtica_aprendizaje_zapata.pdf http://eprints.rclis.org/19490/ |
Daftar Isi:
- This paper presents a review of the big data learning analytics in Higher Education. It relates to a new task-based and achievement-based learning paradigm in line with individual capacities, but not with time, space or age. It is pertinent to raise it now because this is the first time that technological power can meet this need. Its feasibility and relevance are clearly defined by the 2 sigma problem, which raises the broad horizon ahead to a limit target learning. Currently, a strong interest in analysis of learning data is found using systems and software based on social and ubiquitous environments and on the new LMS, which have them included. The problem is that until now, commonly used consolidated tools only get data and graphs that relate individual against group performance, and both together. Moreover, those relationships only make reference to learning data taken from conventional evaluation input. However, there is a space that provides a huge amount of data not only for student evaluation which we currently ignore, at least explicitly. It is the space of connected personal work, networking with peers, with teachers, with the resources and all the material to be used, and with the registration of the methods and strategies they use. Now there is a new perspective: personalised big data analytics. Algorithms used for other media, properly guided by personalized learning theories, by teaching techniques and instructional design can, along with advances in data mining, obtain information to set educational intervention better, to improve student performance, besides their satisfaction and that of the educational program. We have analysed current theories and practices and the review has identified four major challenges that this field must address now. It also points out that it would be good to have a reference model for learning analysis. A model that gives supports providing communication tools and common work patterns for researchers. A challenge of priority nature is to use analytics to detect early indicators of dropout in online studies. Finally, we conclude that an approach involving mediation between analytics and its application in the context of student orientation or instructional design is essential.