Understanding College Algebra Students through Data Mining

In a College Algebra class of 1,200 students, comparable to those at most state funded universities, the greatest obstacle to providing personalized, effective education is the anonymity of the students. Data mining provides a method for describing students by looking for patterns in the large amounts of information they generate. Traditionally, educational researchers have categorized students using a pre-existing framework developed after years of qualitative research and classroom studies. However, by using computer algorithms, or “black box” data mining methods, to analyze data, one can avoid the problem of determining if preconceived frameworks are relevant or valid. In this session, we will review a study conducted at a mid-sized public university of the academic behavior of College Algebra students. The presenter will review how the data was collected, analyzed and synthesized to extract the defining characteristics of student clusters. Then, the presenter will lead discussion on ideas for implementing differentiated instruction and targeted interventions for struggling students

Using data mining to differentiate instruction in college algebra

Doctor of PhilosophyDepartment of MathematicsAndrew G. BennettThe main objective of the study is to identify the general characteristics of groups within a typical Studio College Algebra class and then adapt aspects of the course to best suit their needs. In a College Algebra class of 1,200 students, like those at most state funded universities, the greatest obstacle to providing personalized, effective education is the anonymity of the students. Data mining provides a method for describing students by making sense of the large amounts of information they generate. Instructors may then take advantage of this expedient analysis to adjust instruction to meet their students’ needs. Using exam problem grades, attendance points, and homework scores from the first four weeks of a Studio College Algebra class, the researchers were able to identify five distinct clusters of students. Interviews of prototypical students from each group revealed their motivations, level of conceptual understanding, and attitudes about mathematics. The student groups where then given the following descriptive names: Overachievers, Underachievers, Employees, Rote Memorizers, and Sisyphean Strivers. In order to improve placement of incoming students, new student services and student advisors across campus have been given profiles of the student clusters and placement suggestions. Preliminary evidence shows that advisors have been able to effectively identify members of these groups during their consultations and suggest the most appropriate math course for those students. In addition to placement suggestions, several targeted interventions are currently being developed to benefit underperforming groups of students. Each student group reacts differently to various elements of the course and assistance strategies. By identifying students who are likely to struggle within the first month of classes, and the recovery strategy that would be most effective, instructors can intercede in time to improve performance

College Mathematics: Placement and Remediation through Data Mining

Like many higher education institutions, Coker College admits a diverse student body. This diversity extends to the spectrum of mathematical ability of the newly admitted class. Traditionally, secondary education grade point averages alongside national standardized test scores were used as the primary metrics to determining the mathematics course that a student would be placed. This method of placement frequently resulted in students that were erroneously placed as indicated by poor first semester performances. Given the relatively modest size of the incoming class, this was deemed particularly troublesome. Consequently, the first of the two authors sought to improve student placement outcomes by devising a placement exam that whose questions were chosen using data mining techniques. An unsurprising byproduct of this effort was an increased number of students enrolled in remedial classes for which the institution was not well-staffed enough to handle. To aid with the newly discovered need for remediation, the second of the two authors created an online transition course that was made openly available to all students that did not achieve sufficiently high placement exam scores to place out of a basic mathematics course. By recording all student activity on the placement exam and transition course, the faculty is able to analyze trends and adapt instruction and preparation quickly to best serve incoming students. Instruction, placement, and the entrance exam will continue to be revised as more collected information creates a clearer picture of incoming Coker students. The presentation will close with a discussion exploring adaptations of these methods to other disciplines