Social Learning Analytics in Higher Education. An experience at the Primary Education stage

Author:

Díaz-Lázaro Jose JavierORCID,Solano Fernández Isabel M.ORCID,Sánchez-Vera María del MarORCID

Abstract

The concept of Learning Analytics, as we understand it today, is relatively new but the practice of evaluating user behavior is not innovative. For years, technological development, along with other educational aspects, have encouraged, developed and facilitated this practice as a way of providing a personalized quality experience to students. The main goal of this study, carried out in the Primary Education Degree of the University of Murcia, was to research, from the perspective of Social Learning Analytics, how students learn and collaborate in online environments, specifically through their use of social media. With the idea of improving and optimizing future teaching experiences, a pilot study was conducted using weblog, Twitter and Facebook to work with different topics on the subject. The method used in this research was a participant observation and the analysis performed was both quantitative, based mainly on the data gathered from the learning analytics, and qualitative (analyzing students’ content from comments). Results show that there was greater interaction on Facebook than weblogs, where students interacted to deal with aspects related to the learning process and the topic of the subject. This exchange of information grew during the development of the experience. In addition, learning analytics shows that there is a relationship between group members and their interaction and behavior in networks.

Publisher

University of Alicante

Subject

Education

Reference19 articles.

1. Ato, M., López, J. J., & Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología. Anales de psicología, 29 (3), 1038-1059. doi:10.6018/analesps.29.3.178511

2. Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. LAK '12 (p. 134). doi:10.1145/2330601.2330636

3. Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6) 683-695. doi:10.1080/13562517.2013.827653

4. Dahlstrom, E., Walker, J. D, & Dziuban, Ch. (2013). ECAR Study of Undergraduate Students and Information Technology (Research Report). Louisville, CO: EDUCAUSE Center for Analysis and Research. Retrieved from http://www.educause.edu/ecar

5. De Laat, M., & Prinsen, F. (2014). Social Learning Analytics: Navigating the Changing Settings of Higher Education. Research and practice in assessment, 9(4), 51-60.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3