Introduction

In Mexico, school dropout is present at all educational levels. For example, the performance averages for undergraduate studies are as follows: 50% reprobation, 20% dropout, 40% attrition, and 30% certification [1]. A large number of dropout students come from engineering majors, which is largely due to students’ lack of mathematical competencies [2]. At Universidad Popular Autonoma del Estado de Puebla (UPAEP), a private university in Mexico, more than 45% of automotive design engineering students drop out of their major studies in their freshman year. Several researchers have studied the causes of dropout, such as enrollment in other careers or institutions, entering the job market [3], teaching modality [4], lack of career planning [5], and students’, instructors’, and academic leaders’ roles [6] and their impact on school trajectories [7]. This study aims to identify new reasons for dropping out that have not been explored, such as early class hours, the number of advisory hours in the area of mathematics and the place of origin of first-year students pursuing the Engineering in Automotive Design field at a private Mexican university.

On the other hand, the Six Sigma (SS) methodology eliminates defective goods in production lines by decreasing process variation. This quality approach employs the Define-Measure-Analyze-Improve-Control (DMAIC) method. The literature reveals that this perspective has been implemented in the education sector in admission processes [8], enhancing educational quality in organizations [9], and identifying the success factors of applying Six Sigma in an academic library [10], among others. Meanwhile, at higher education institutions in Mexico, it has only been used in the implementation of ISO9001 quality management systems [11] and examining the causes of reprobation from academic and administrators’ perspectives [12].

Risk is the occurrence uncertainty that affects target goal achievement at the organization or personnel level. Thus, risk management in firms, and including universities, consists of the control of activities of the operations process, avoidance assumptions, and the elimination of unacceptable risks to adjust the risk level in the proper management of future events [13].

The International Standardization Organization (ISO) developed a risk management standard named ISO31000:2018. This norm applies to any public or private organization. It deals with institutional activities, including strategy, operations, processes, functions, products and services. A study of secondary schools in Thailand provides a useful risk framework that can be applied; it considers four main elements: input, process, outputs and outcomes, and feedback [14]. In addition, this norm increases the likelihood of achieving objectives, improving the identification of opportunities and threats, and effectively allocating and using resources for risk treatment.

In addition, there is a desire to decrease the current dropout rates, and to accomplish this goal, the Six Sigma methodology and the risk management approach will be used.

Materials and methods

Materials

The materials used to develop this research were data collected from an academic enterprise resource planning software called UNISOFT, property of UPAEP, and the statistical software Minitab, which was used to perform all the numerical analyses. An improvement team also supported the project; no economic funding was necessary to implement the improvement initiatives.

Methods

The methodology to identify causes that can be addressed and contribute to a reduction in dropout rates in the first year of studies of engineering students was the Define-Measure-Analyze-Improve-Control cycle. In the defining stage, the university engineering degree with the highest dropout rates is identified using statistical tools in the grade records of the enrolled students. Subsequently, the latent risks of the student must be identified from their applicant category as a freshman. Therefore, metrics are established that reflect the problems studied. A baseline is established, and a process capability study is performed for each metric at the measurement stage. Next, in the analysis stage, the possible causes are discovered and validated through inferential statistical methods. To improve the previously established metrics, interventions are established that cover aspects such as effectiveness, ease of implementation, cost, and security. Finally, the control measures that ensure the repeatability and reproducibility of the results achieved are documented and implemented.

Define Stage

The Industrial and Automotive Engineering Faculty at UPAEP has three majors: industrial, manufacturing and automotive design engineering. The main purpose of the university is to create critical thinking by using current theories and developing leaders that transform society. To accomplish this goal, a joint venture was created with an original equipment manufacturer (OEM) located in Puebla, México to create an engineering major dedicated to the automotive design field. In this case, the OEM contributed the personnel and know-how to support academics to address this matter. Therefore, a new major was developed. This new educational program consists of 60 courses in nine academic periods with three elective areas: electrical harnesses, engines, and vehicle interiors. The first two student classes lacked a sufficient science background and did not have the appropriate attitude to effectively engage in their professional studies.

At the beginning of the study, there was an average of two failed courses of four courses in which first year students enrolled in the automotive design major; see Figure 1. The objective of this research is to reduce the number of failed courses among freshman students of automotive design engineering.

To understand the process, an IPO chart was developed. Meanwhile, in Figure 2, the desired function in terms of the academic situation that freshmen present was established in a How-How diagram. It is necessary to point out that the CENEVAL test is a mandatory admission requirement and that the academic director chooses the courses for the students due to the lack of academic knowledge regarding the curricula and their management processes.

In addition, an institutional risk matrix, Figure 3, shows the five main risks that new students confront. Two phases of risks were considered: candidate status risks and freshman status risks. Before the student’s enrollment at UPAEP, the risks consist of an unmotivated and/or poor attitude and insufficient funds to complete the entire major. After their enrollment, the principal dangers are an excessive number of academic courses and skipping classes. This matrix also confirmed that the lack of control in some steps of the enrollment process generates the most crucial school dropout risk effects.

As a result, the author decided to take into consideration three metrics to reduce the number of failed courses by the students. Those metrics, their current values and the academic requirements are shown in Table 1.

Measure Stage

In this stage, UPAEP uses UNISOFT as enterprise resource planning software, and academic performance data are collected. This information allowed the baseline establishment and the construction of control charts for each metric considered.

In terms of the percentage of attendance, Figure 4 illustrates that in four cases (points outside the upper control limit and the lower control limit), the students received a score of less than 50%. Therefore, the percentage of attendance is out of statistical process control. In addition, the calculated capability index, C_pk, had a value of 0.30, which means that students perform poorly in this metric (see Figure 5).

Statistical studies were performed for the semester average and CENEVAL test score metrics, and the results are summarized in Table 2.

Analysis Stage

In this phase, an academic team was assembled to identify the possible root causes that are generating the three six sigma metrics. The members of the team were full-time professors and the Industrial and Automotive Engineering Faculty Head, Mathematics Head, Physics Head, and Director of Admissions.

After a brainstorming session, the team delivered three Ishikawa charts to identify causes that can be addressed. Figure 6 establishes the detected cause for the class absence problem. This demonstrates that early morning course scheduling affects the decision to drop a class. In fact, several students live by themselves, and the lack of parental authority enabled them to skip morning classes without any consequences or family members encouraging them.

In the other two six-sigma metrics, the same analysis tool was used, and the results are shown in Table 3. Professors of mathematics and physics affirm that there is a relationship between the final score in a science course and the number of advisory hours, so this fact must be considered. On the other hand, the number of courses enrolled in by the students could be an explanation for bad academic performance. Finally, the team identified that most of the freshman engineering students came from the southern part of México, and it is well known that this area had disruptions in education due to continuous industrial action in universities.

With this in mind, several statistical studies confirmed the potential causes. The following summary in Table 4 highlights the findings.

Improvement Stage

For each verified cause, an intervention is proposed. The team, following the risk management principles, established the following actions considering four criteria: effectiveness, easy implementation, cost, and safety, which are shown in Table 5.

To validate each intervention, measurements of the six sigma metrics were taken to demonstrate improvement. In the attendance percentage (Y_p1), the students increased their participation; however, the performance of one student fell outside of the new control limits, as presented in Figure 7. Figure 8 shows an improvement of 1.26 in the capability index (Cpk). This means that only five absences occurred in one million opportunities.

The semester average (Y_p2) increased from a value of 6.11 to a new average of 8.24. In addition, the standard deviation decreased by 50% from the original data. The CENEVAL score increased by 50.55 points in the new class of students and ended with a capability index of 0.54.

Control Stage

Finally, the improvement team created a control plan, see Figure 9, that documented all the steps to sustain all the improvement gained with the project.

Results

Four interventions were implemented after the spring semester, and their impact is shown in Figure 10. The upper part of the figure shows the name and major of the student, as well as the student’s schedule during the semester period, and in the lower part, the final score of the periods demonstrates the reached achievement.

The DMAIC methodology was implemented in a period of four semesters; it involved management and academic personnel. The three six sigma metrics showed at least 5% improvement in each one (see Table 6).

Conclusions

In terms of the interventions established, scheduling classes starting at 8:00 am and creating a 15-minute grace period in the first class of the day represented parental interest from the Faculty Academy to promote confidence and motivation among freshman students. Therefore, attendance and counseling programs should be implemented by innovation centers [15]. This represents a different outlook from the one that posits that adult learners are initially highly motivated and self-directed [16].

Another finding of the research was the positive relationship between the attendance percentage and the final course scores. The results support the conclusion that class attendance is the best predictor of college grades compared to any other known predictor of academic performance [17].

In terms of CENEVAL admission test scores, the research concludes that an increase in the admission requirement only results in a 5% improvement in reducing school dropout risk. There is evidence that high school grades and scores on standardized admission tests are not correlated with study skills [18], so there is no guarantee of academic success during college studies.

The author realizes that the research performed has certain limitations such as the study having a scope of only one engineering major and the results being based on a private Mexican university. Additionally, he recommends investigating cognitive deficit profiles among individuals with mathematics difficulties and assessing them in engineering program candidates [19] to reach better results in the DMAIC process.

References

Notes

* Research article

Author notes

^a Correspondence author: luis.cuautle@upaep.mx

Additional information

How to cite this article: Cuautle Gutierrez, L., “Reducing School Dropout Risk in a Mexican Private University.” Ing. Univ. vol. 27, 2023. https://doi.org/10.11144/Javeriana.iued27.rsdr

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