A Data-Driven and Model-Based Point of Departure to
Student Success Initiative 2025
Introduction. CSUN has a flourishing image in the Quality-to-Cost space. However, the
situation is not the same when the space is expand to
the Quality-to-Cost-to-Time dimension. CSUN does
not sustain a good position in time-to-graduation
(TtG) and graduation-rate (GR) dimensions. In a
system-wide effort to address the problem, the CSU
has established a plan to remove obstacles to
receiving a baccalaureate degree. Graduation Initiative
2025 (Student Success Initiative 2025 in CSUN’s
vocabulary) established a series of objectives, which
include increasing the 4-year and 6-year GRs to 40% and 70%, respectively [1, 2, 3].
Aim and Expected Outcome. In God, we trust; all others must bring data. W. Edwards Deming
1900-1993. We will have an in-depth data-driven analysis on 16-years performance of David
Nazarian College of Business and Economics (DNCBE). We will link the current and potential
academic resources and learning processes in a total system [4,5]. The performance of all
subsystems will be linked to the overall performance of the total system on TtG and GR space.
On these foundations, we develop Descriptive (Statistical), Diagnostic (Cause-Effect), Predictive
(Forecasting), and Prescriptive (Optimization) quantitative models to understand the binding
constraints of CSUN students as well as the governing constraints of the total system. We avoid
simple shortsighted solutions for such a complicated problem. We provide student-centered,
data-driven, model-based insight into the strategic and operational decisions to increase GR and
reduce TtG at DNCBE.
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Methodology. Count what is countable. Measure what is measurable. What is not measurable,
make it measurable. Galileo Galilei, 1564 -1642. Using
Descriptive Analytics, we transform data into
information and will learn what has happened in the
past 16 years. Diagnostic Analytics will be utilized to
transform information into knowledge and understand why those outcomes happened. Predictive
Analytics is to then transform knowledge into understanding. It allows us to foresee the future
and predict what is likely to happen.
Prescriptive Analytics enable us to transform
our understanding, by using quantitative
optimization techniques, into a wisdom to pave
a better road of TtG and GR toward the future.
Our modes of communication are Tabular, Graphical, Schematic, and Quantitative
representation. State of the art software such as Excel: Computation Tool, Tableau: Visualization
Tool [6], and Frontline Solver: Analytics for Spreadsheets and Web [7] will be utilized in our
computations. Students will play key roles throughout this process. Our models are configurable.
They can be replicated at other colleges, as well as at departmental, university, and CSU levels.
Task 1. Prescriptive
Analytics. We will study
the past 16-year’s
performance of firsttime-freshmen students (FTF) and first-time-transfer students (FTT) at DNCBE on TtG and GR
dimensions. We will implement prescriptive quantitative tools and software to provide insight to
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picture the governing constraints. Descriptive statistics such as categorical and numerical
histograms, measures of centralization, coefficients of variations, skewnesses, percentiles and
quartiles, 5-number box-plots, cross tabulations, goodness of fit-to-known probability
distributions, correlation analysis, confidence intervals, test of hypothesis, and analysis of
variance will be prepared to understand the past.
Task 2. Predictive Analytics. We implement predictive analytics tools and techniques such as
exponential smoothing,
linear and non-linear
regression, two and
multiple variable
regression, logistic regression [8], and data mining tools [9] such as classification trees,
discriminant analysis, and k-nearest neighbors to study the relationships between different
inputs, foresee the future, and estimate GtT and GR as our variables of interest.
Task 3.Early-Alert at Milestones. The data of the past 16 years indicates that attention in the
first 2-years needs to be focused on GR, while the second 2-year analysis period needs to focus
on TtG. We define lower division (LD) courses
(mainly Micro- and Macro- Economic, Financial- and
Managerial- Accounting, Introduction to Computers,
and Statistics) as the convergence links to our
Gateway course, and the first milestone to measure
GR and TtG. Alternative data-driven spreadsheet
modeling and Decision Analysis tools [8,10] will be utilized to assess past performance and
foresee the future. Quantitative analysis of the same nature, as described throughout this
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proposal, will assess the performance of students in the upper division (UD) course (mainly
Operations Management, Financial Management, and
Marketing Management) when they complete the last UD
course. We calibrate the modes based on the past data,
and predict for the future. We have already accessed
200,000 pieces of DNCBE data from the past 16 years to
be utilized in our predictive analytics for GR and TtG.
Task 4. Data Envelopment Analysis (DEA). An institution may be interested in evaluating how
efficiently various entities operate. DNCBE may be interested in the evaluation of how
efficiently various departments or courses operate. DEA is an optimization-based methodology
for performing this type of analysis. DEA determines how efficiently an operational unit
transforms inputs into outputs when compared with other units. We will work with different
entities to develop a configurable, well-defined set of inputs and outputs for a linear
programming-based DEA. Our DEA model, similar to our other modes, is configurable. It can be
replicated at other college, university, and CSU levels.
Task 5. Preparation of the paper. A paper will be submitted to a leading pedagogical journal.
Timeline. The schedule of the main activities of the research is given below. In addition to my
research assistants, the students in my classes will also be involved in this process.
Sep.17 Oct.17 Nov.17 Dec.17 Jan.18 Feb.18 Mar.18 Apr.18 18-May Jun.18 Jul.18 Aug.18
Prescriptive Analythics.
Predictive Analythics.
Erly Alert At Milestones.
Data Enveloement Analysis.
Preparation of the Paper.
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Potential Significance. This project has potential practical significance to create value in the
sense that it may lead to increase in GR and decrease in TtG. In addition, its theoretical concepts
and models will be built into two courses. We believe that the interest of the students will
increase in quantitative and analytical courses, if the course material is tied to real-life
applications. What will be closer to the real life of the students than the concepts and models
discussed in a project that can have a direct impact on the TtG and GR of the student? In Fall
semesters, the author of this proposal teaches SOM306: Operations Management to 340 students.
In Spring semesters, he teaches a smaller course of SOM307: Data Analysis and Modeling. We
will assimilate the findings of this project into our courses. We will replace many of the existing
examples with the material of this project. This will include 6 weeks of systems thinking,
forecasting, operations strategy, process flow analysis, and modeling. This project is an excellent
opportunity for my student assistants and my students in general to improve their excel
(Computations), Tableau (Data Visualization), and Frontline Solver (Predictive and Prescriptive
Analytics) proficiencies.
Plan for Dissemination: The author of this proposal was the chair of the Innovative Education track
of the Decision Sciences Institute (DSI). In 2016, this track topped all other tracks at DSI in the
greatest number of presentations. A quick Google Scholar search reveals close to 600 citations in
scholarly publications referencing the author of this proposal. He is also a 4-time winner of the
DNCBE Faculty Publication Award. The result of this study will be presented in two national
meetings: INFORMS and DSI. Two papers will be submitted to a leading (high impact) journal.
Availability of the Resources: The required infrastructure is available at DNCBE. We have licenses
for Tableau and Frontline Solver. However, the most important resource of this project will be our
students, who will be seriously involved in the project in both teaching and research.
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