Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals

Autor/in	Michalski, Greg V.
Institution	Association for Institutional Research
Titel	Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals
Quelle	(2011), (66 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	College Instruction; Courses; Withdrawal (Education); College Students; Student Attitudes; Responses; Information Needs; Information Seeking; Information Retrieval; Content Analysis; Information Technology; Data Processing; Pattern Recognition; Classification; Data Analysis; Enrollment Influences; State Colleges; Factor Analysis; Models; Florida + Suchen Sie Ihr Suchwort? Hochschullehre; Kursangebot; Kursabbruch; Collegestudent; Schülerverhalten; Information need; Informationsbedürfnis; Informationserschließung; Inhaltsanalyse; Informationstechnologie; Datenverarbeitung; Mustererkennung; Classification system; Klassifikation; Klassifikationssystem; Auswertung; Faktorenanalyse; Analogiemodell
Abstract	Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed on open-ended, verbatim, student comments in which students explained their reason(s) for course withdrawal. The text for all comments was extracted from the course withdrawals database of Florida State College at Jacksonville, a large, diverse, multi-campus institution located in northeast Florida. An initial set of 616 comments from the fall 2010 term was used to develop a preliminary text mining model which categorized 96.1% of all records. The model was retained and further tested using a second set of 679 comments from the spring 2011 term and found to categorize 98.7% of the term records. Combined data from both terms (n = 1,295) was used to produce a final text mining model containing eleven node categories. Model node categories were labeled referencing a framework of prior empirical work in the area of student course withdrawal. Leading academic rationales include course characteristics (especially those involving student preparedness, satisfaction, and delivery mode), faculty satisfaction, and schedule adjustments. Leading non-academic rationales include personal issues especially involving job/work, family, financial, and health. Record classification data from the model were also exported and explored to further group and summarize results. Principal Components Analysis of all data from both terms revealed four components which accounted for 45% of the total variance with the first two components involving instructional delivery and student personal issues accounting for 24% of the variance. Hierarchical Cluster Analysis and Multiple Correspondence Analysis were also used to confirm results suggesting major academic withdrawal reasons to include negative course perceptions and to a lesser degree negative faculty perceptions. Non-academic rationales were found to center on job-work, personal, and time-schedule issues. Limitations and implications for institutional research and practice are presented and discussed. Additional Model Diagrams by Primary Node Category (fall 2010) are appended. (Contains 12 tables and 9 figures.) (As Provided).
Anmerkungen	Association for Institutional Research. 1435 East Piedmont Drive Suite 211, Tallahassee, FL 32308. Tel: 850-385-4155; Fax: 850-383-5180; e-mail: air@airweb.org; Web site: http://www.airweb.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10