Abstract:

Selecting an academic degree program can be overwhelming for high school students seeking to pursue a postsecondary degree. Some students pursue certain degree majors primarily based on their income potential and job demand. Other students choose academic majors they are enthusiastic about or better prepared for. Nonetheless, the chosen field of study can have meaningful consequences for their future life.

Higher education reports have indicated that students are taking longer to graduate. Longer time to degree completion can lead to a greater amount of opportunity cost in forgone wages while students stay outside the labor force. Many factors contribute to extended time to a degree. One potential reason attributed to the extended time to degree completion is poor academic performance resulting in failing courses. It is generally accepted throughout the academic community that differences exist among college academic programs due to the level of difficulty of the course.

Difficult academic majors require the commitment necessary to study with intense focus despite challenges. Hence, a student's choice of major field of study in college can have a significant impact on the probability of graduation, time of degree completion, cumulative grade point average (CGPA), and, ultimately, the cost of a college education.

Analyzing the risk associated with pursuing a particular academic degree program in terms of reaching prompt graduation with a high CGPA can help make an informed decision. In addition, it can also help faculty and administrators better serve student populations to meet their educational goals.

In this WIP paper, a probabilistic model that forecasts a large set of outcomes will be presented. The model can go beyond what has occurred in the past, considering new scenarios and a wide range of uncertainties to estimate the graduation-related risk associated with a particular degree major. The model is based on the Monte Carlo method, which is a computerized mathematical technique used in fields such as finance, investment portfolio management, project management, energy, manufacturing, engineering, research and development, insurance, oil and gas, transportation, and the environment.

The approach to building the probabilistic model to assess risk associated with a degree program involved; selecting the right probability distributions for grades in each course required to graduate from the program, correct use of the input data from these distributions, and proper consideration for the dependencies between performance in different courses. For the base model, the Engineering Technology (ET) degree program is being used as a pilot. To build the model, the project team gathered historical data on grades attained in courses offered in the ET program to derive the probability distributions for grades in each course.

Microsoft Excel was used to build the model. Once the model was set up, it was run thousands of times. In each iteration, different randomly generated values from the probability distribution for grades were used in the model. When the simulation was complete, the model outputted a range of values. These results were then used to understand the risk and uncertainty associated with the academic degree in terms of attainable CGPA and time to completion. Using graphical visualization tools, one can then create graphs of different outcomes and their chances of occurrence.

Keywords:

Degree completion, Engineering Technology, Monte-Carlo Method, Simulation, Risk Modeling.