Predictive Analytics Machinery for STEM Student Success Studies

Figures & data

Table 1. List of institutional variables considered in the STEM success study.

Demographic information

Gender

Age

Ethnicity

Disability status

Honors program

Low income status

Pell grant

Military status

EOP status

On-campus housing in dorms

Learning community – specialized dorms

Compact for Success – local outreach program

Academic preparation

SAT/ACT score

Math proficiency

English proficiency

High school GPA

ELM score

First generation college student

Major status

Admission basis – first-time freshman, transfer

Enrollment status – full-time or part-time

Academic Performance

Term/Total/Campus GPA

Term/Total/Campus units earned

Probationary status – each semester

Disqualified status – each semester

STEM major indicator – each semester

Grades in each course enrolled – initial time or last time taking

Semester each course enrolled

AP credit by subject

Courses transferred into SDSU for credit

Grades in transferred courses

Semester course transferred

Graduation outcomes

4-year graduation success indicator

5-year graduation success indicator

6-year graduation success indicator

Time to graduation – number of semesters or total units earned

Figure 1. Circular migration plot for STEM students – Cohorts 2001 to 2008. Unit is 1000 per axis tick.

Department abbreviations are as follows: Psychology PSY, Aerospace Engineering and Engineering Mechanics AE & EM, Information and Decision Systems IDS; Chemistry and Biochemistry CHEMISTRY, Computer Science CS, Mathematics and Statistics MATH, Electrical and Computer Engineering E & COMP E, Mechanical Engineering ME, Civil, Construction and Environmental Engineering C & ENVR E. The circle “crust” includes an outer, thick-lined arc that represents the total volume of students moving to and/or from these departments/majors. The crust also includes an inner, thinner-lined arc that represents the number of students entering the university in these departments/majors.

Figure 2. Important course predictor to graduation success with cutoff grade: the threshold is determined from the variable importance [Algorithm 1].

Figure 3. Sequential analysis plot: quantification of student migration within STEM and out of STEM based on performance in a key major pre-requisite progression course (here Calculus for Biology II). Note that the course grades are from the students’ first attempt at the course.

Figure 4. Success score gauge plot. Red zone represents “at risk” (), orange zone represents “warning” (10–25%), yellow zone represents “safe” (25–75%), light green zone represents “on track” (75–90%), and green zone represents “succeeding” (). The black arrow pointer indicates the success score for the student under consideration. The grey arrow pointer presents a predicted student success score following a behavioral change, for example an intervention strategy in which the student partakes or a change in performance.

Figure 5. ROC curve using proposed analytic algorithms through random forest. Data was extracted for predicting the success of 1st, 2nd, 3rd, and 4th semester Biology students graduating in the same department within 6 years.

Figure 6. Conceptual representation of our analytics workflow for designing and deploying dashboards using the proposed predictive modeling outcomes.

Figure 7. Conceptual representation of our workflow for prototyping and establishing the STEM student success dashboards. Though we use Tableau as our BI solution, any BI platform may be used in those workflow bubbles.

Figure 8. Screen shot of the prototype student success dashboard from R shiny dashboard.

Figure 9. Screen shot of the student success dashboard from Tableau.