Using Bayesian Learning to Classify College Algebra Students by Understanding in Real-Time

Autor/in	Cousino, Andrew
Titel	Using Bayesian Learning to Classify College Algebra Students by Understanding in Real-Time
Quelle	(2013), (88 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Kansas State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3031-8997-5
Schlagwörter	Hochschulschrift; Dissertation; Mathematics Instruction; Algebra; Probability; Mathematical Models; College Mathematics; Student Needs; Mathematics Skills; Profiles; Individualized Instruction; Undergraduate Students; Classification; Grouping (Instructional Purposes); Markov Processes; Bayesian Statistics + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Mathematics lessons; Mathematikunterricht; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Mathematical model; Mathematisches Modell; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Charakterisierung; Profilanalyse; Individualisierender Unterricht; Classification system; Klassifikation; Klassifikationssystem; Grouping; Gruppenbildung; Markowscher Prozess
Abstract	The goal of this work is to provide instructors with detailed information about their classes at each assignment during the term. The information is both on an individual level and at the aggregate level. We used the large number of grades, which are available online these days, along with data-mining techniques to build our models. This enabled us to profile each student so that we might individualize our approach. From these profiles, we began to investigate what can be done in order to get students to do better, or at least be less frustrated. Regardless, the interactions with our undergraduates will improve as our knowledge about them increases. We start with a categorization of Studio College Algebra students into groups, or clusters, at some point in time during the semester. In our case, we used the grouping just after the first exam, as described by Dr. Rachel Manspeaker in her PhD. dissertation. From this we built a naive Bayesian model which extends these student clusters from one point in the semester, to a classification at every assignment, attendance score, and exam in the course. A hidden Markov model was then constructed with the transition probabilities being derived from the Bayesian model. With this HMM, we were able to compute the most likely path that students take through the various categories over the semester. We observed that a majority of students settle into a group within the first two weeks of the term. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01