Literaturnachweis - Detailanzeige
| Autor/inn/en | Chinsook, Kittipong; Khajonmote, Withamon; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jantakoon, Thada |
|---|---|
| Titel | Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture) |
| Quelle | In: Higher Education Studies, 12 (2022) 1, S.105-114 (10 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1925-4741 |
| Schlagwörter | Student Behavior; Learning Analytics; Computer Software; Rating Scales; Data Use; Data Collection; Higher Education; Privacy; Information Security; Academic Advising; Student Records; Academic Achievement; Information Systems; College Students; Universities; Tennessee (Nashville) |
| Abstract | Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts' opinions on the system architecture were at the most appropriate level. (As Provided). |
| Anmerkungen | Canadian Center of Science and Education. 1595 Sixteenth Ave Suite 301, Richmond Hill, Ontario, L4B 3N9 Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: hes@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/hes |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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