Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

Autor/inn/en	Martínez Abad, Fernando; Chaparro Caso López, Alicia A.
Titel	Data-Mining Techniques in Detecting Factors Linked to Academic Achievement
Quelle	In: School Effectiveness and School Improvement, 28 (2017) 1, S.39-55 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Martínez Abad, Fernando) ORCID (Chaparro Caso López, Alicia A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0924-3453
DOI	10.1080/09243453.2016.1235591
Schlagwörter	Foreign Countries; Data Collection; Statistical Analysis; Evaluation Methods; Measurement Techniques; Academic Achievement; Educational Assessment; Case Studies; High School Students; Mathematics Tests; Standardized Tests; Predictor Variables; Context Effect; Surveys; Decision Making; Classification; Matrices; Data Interpretation; Goodness of Fit; Regression (Statistics); Models; Input Output Analysis; Concept Mapping; Statistical Distributions; Mexico + Suchen Sie Ihr Suchwort? Ausland; Data capture; Datensammlung; Statistische Analyse; Messtechnik; Schulleistung; Education; assessment; Bewertungssystem; Case study; Fallstudie; Case Study; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Standadised tests; Standardisierter Test; Prädiktor; Survey; Umfrage; Befragung; Decision-making; Entscheidungsfindung; Classification system; Klassifikation; Klassifikationssystem; Matrizenrechnung; Data evaluation; Datenauswertung; Regression; Regressionsanalyse; Analogiemodell; Concept Map; Wahrscheinlichkeitsverteilung; Mexiko
Abstract	In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental, cross-sectional design and a sample of 18,935 high school students from 99 educational institutions in Baja California state (Mexico). The information was collected from ENLACE tests and context surveys given to students in Baja California. Decision trees were used to apply classification techniques, and the results indicate that personal factors are most indicative of academic performance, followed by school-related and social factors. In conclusion, the paper discusses the similarities between the results obtained and those shown in literature, highlighting how simple decision trees allow a greater explanation and interpretation than other models and techniques. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01