A Data-First Approach to Learning Real-World Statistical Modeling

Autor/inn/en	Bornn, Luke; Mortensen, Jacob; Ahrensmeier, Daria
Titel	A Data-First Approach to Learning Real-World Statistical Modeling
Quelle	In: Canadian Journal for the Scholarship of Teaching and Learning, 13 (2022) 1, Artikel 6 (20 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Bornn, Luke)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1918-2902
Schlagwörter	Undergraduate Study; Cooperative Learning; Peer Influence; Undergraduate Students; Statistics Education; Experiential Learning; Teaching Methods; Data Analysis; Interdisciplinary Approach; Competition; Student Attitudes; Statistical Analysis; Models; California + Suchen Sie Ihr Suchwort? Grundstudium; Kooperatives Lernen; Experiental learning; Erfahrungsorientiertes Lernen; Teaching method; Lehrmethode; Unterrichtsmethode; Auswertung; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Wettkampf; Schülerverhalten; Statistische Analyse; Analogiemodell; Kalifornien
Abstract	This paper presents a novel design for an upper-level undergraduate statistics course structured around data rather than methods. The course is designed around curated datasets to reflect real-world data science practice and engages students in experiential and peer learning using the data science competition platform Kaggle. Peer learning is further encouraged by patterning the course after a genetic algorithm: students have access to each other's solutions, allowing them to learn from what others have done and figure out how to improve upon previous work from week to week. Implementation details for the course are provided, and course efficacy is assessed using a survey of students and a focus group. Student responses suggest that the structure of the course contributed to narrowing the perceived gap between low- and high-performing students, that desired learning outcomes were successfully achieved, and that a data-first approach to learning statistics is effective for learning. (As Provided).
Anmerkungen	University of Western Ontario and Society for Teaching and Learning in Higher Education. Mills Memorial Library Room 504, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L6, Canada. Tel: 905-525-9140; e-mail: info@cjsotl-rcacea.ca; Web site: http://www.cjsotl-rcacea.ca/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11