Predicting bootcamp success: using regression to leverage preparatory course data for tech career transitions - Santos, Ricardo; Pesovski, Ivica; Henriques, Roberto; Trajkovik, Vladimir (IATED, 2024)
In our ever-evolving digital landscape, the demand for tech-savvy professionals is soaring. However, traditional education often falls short in equipping individuals with the practical skills needed by employers. Aspiring tech enthusiasts face a dilemma: they want to gain swift entry into the industry without committing to lengthy degree programs. Meanwhile, career changers seek streamlined paths to acquire relevant skills. Programming bootcamps provide a pragmatic solution. These intensive, short-term programs prioritize hands-on learning over theoretical depth. Participants emerge with coding abilities, web application development skills, and collaborative prowess — all within months. Bootcamps attract both young learners exploring alternatives to Bachelor's degrees and professionals switching careers to tech jobs. By bridging the education-employment gap, bootcamps empower individuals for junior roles in the tech sector. However, bootcamps also pose challenges. Many participants lack formal programming training, which can impact their bootcamp success. Institutions offering these programs are incentivized to create preparatory courses, ensuring fundamental skills and providing support mechanisms. In this study, we analyze the efforts of future boot campers in preparatory courses at a European university, using leave-one-out cross-validation on a dataset of 207 bootcampers to create a predictive regression model that uses information provided upon registration and their respective attendance at the preparatory courses. Then, we used this model to predict the final score of a new cohort of 58 students and measure the model's performance by measuring the mean, squared, and root mean squared errors on the test set. In the second step, we analyzed the importance of the variables used by the predictive model by measuring the R2 score and the relative tree-based feature importance for each variable. Our results show that data collected before the start of a bootcamp can be used to predict the success of a bootcamper as our Random Forest model predicted each participant's final grade with a mean absolute error of 17.67 points (grades vary between and 100). Moreover, our model explains 46% of the final grades' variability, with prior knowledge of the topic, level of instruction and the number of completed preparatory steps among the most relevant features. Implications for both research and practice are analyzed and discussed.
Keywords: Bootcamps, Tech Education, Predictive Modeling, Preparatory Courses, Career Transition.