Literaturnachweis - Detailanzeige
| Autor/in | Friedman, Alon |
|---|---|
| Titel | Data Science Syllabi Measuring Its Content |
| Quelle | In: Education and Information Technologies, 24 (2019) 6, S.3467-3481 (15 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Zusatzinformation | ORCID (Friedman, Alon) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1360-2357 |
| DOI | 10.1007/s10639-019-09935-x |
| Schlagwörter | Course Descriptions; Comparative Analysis; Data Analysis; Educational Experience; Scoring Rubrics; Guidelines; Educational Improvement; Discussion; Computer Mediated Communication; Asynchronous Communication; Private Colleges; State Universities; Computer Science Education; Course Content |
| Abstract | Learning analytics is an emerging field in which educators and researchers are using data to improve their students' educational experiences. One of the most common courses offered by higher academic institutions in the US is data science. This paper examines the data science syllabi found in today's academic sector and compares the results to those of Friedman's, "Technology, Pedagogy and Education," 26(5), 135-148 (2017) study of Big Data syllabi. For the present study, 40 data science syllabi used in private and public academic institutions in the US were collected, and Palmer et al.'s, "To Improve the Academy," 33, 14-36 (2014) rubric was used as a framework to analyze them. The study found that the most frequently used communication engagement tool, according to the syllabi, was discussion forums (used in 53% of the syllabi), and instant message applications were second (21%). Using Palmer et al.'s rubric, the study found 95% of all data science syllabi the study examined provided very detailed articulation and scored in the top range outline by the model. In comparing data science syllabi to big data syllabi, the study found that data science syllabi provided better descriptions of the learning goals than did big data syllabi. Future studies could examine students' participation and appreciation of these courses using a machine analytics rubric. (As Provided). |
| Anmerkungen | Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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