Disciplinary Competencies Overview of the First Cohorts of Undergraduate Students in the Biotechnology Engineering Program under the Tec 21 Model

Abstract

In the current labor market, developing STEM skills in students is a priority for Higher Education Institutions (HEIs). The present research assesses the Competence Achievement Degree (CAD) of four disciplinary competencies in undergraduate Biotechnology Engineering students at a private university in Mexico. Descriptive statistics of CADs, considering sociodemographic and academic variables (age, gender, nationality, campus region, provenance, admission origin, and load), are presented. Data, filtered through Python, was statistically analyzed with Minitab software. The disciplinary competence of Innovation Management (BT4) was the strongest among undergraduate students in the 2019 and 2020 cohorts. Meanwhile, the other three competencies (BT1: Bioproduct Development, BT2: Bioreactor Design, and BT3: Bioprocess Design) had CADs above 90%. Although there was no statistical difference between the cohorts for BT1, the average CADs for both cohorts differed for BT4. The CADs differed from the Competence Average Grades (CAGs) for all competencies. However, the 2020 cohort showed the lowest correlation between CADs and CAGs and a few significant associations with the tested variables. These assessments will help to focus on the factors and key elements that influence CAD and subsequently establish and conduct appropriate actions to improve the quality of the academic program.

1. Introduction

The term “biotechnology” first appeared at the end of the 1970s and was initially used to refer to the emerging recombinant DNA technologies that were causing an impact on biological sciences [1]. Currently, it relates to science and engineering applications that use living organisms or part of their products in their natural or modified forms to create products or services that solve problems [2]. The biotechnological market size is valued at 331,213.73 million USD in 2023, with an annual growth rate of about 14.2% [3]. Hence, the demand for qualified employees grows too. As a reference, the biotechnological industry in the US had an annualized employment growth of 5.2% in the 2018–2023 period [4].

In this panorama, some studies in Higher Education (HE) have reinforced the need to develop combined skills/competencies and knowledge for occupations [5]. Loke [6] determined the standard general, technical, and academic skills of Biotechnology graduates by using questionnaires to collect data directly from students. A recent study by Ripoll et al. [7] examined how seminar activities could develop scientific and social skills among students. Other studies have focused on topics such as engagement, the implementation of novel educational techniques/technologies, or the development of transversal skills [8,9,10,11]. However, studies evaluating specific or disciplinary competencies in biotechnology programs have been scarce.

This work aims to evaluate which factors affect the development of some disciplinary competencies in students enrolled in the Biotechnology Engineering (BE) program at a Mexican Higher Education Institution (HEI) under a Competence-Based Education (CBE) framework.

1.1. Theoretical Framework and Background

The term “competence” has been used in the workplace since the 1970s, but it was in the 1980s, that it started being applied in higher education [12,13]. The approach of developing competencies has evolved from a focus solely on behavior to a more comprehensive method of enhancing interconnected clusters of knowledge, skills, and attitudes that apply to entering a particular field of study or career [13]. Since the 1990s, models have been developed to implement competencies in higher education.

Competence-Based Education (CBE) is a model designed to ensure that graduates can respond effectively in a rapidly changing world [14]. This approach can be applied at any post-secondary school educational level and in training programs [15]. Unlike the traditional model, CBE is not based on credit hours. It provides credit based on students’ learning rather than their time spent in class. In CBE, students progress at their own pace to achieve the learning objectives set by the institution. This has sparked interest as it can lower college expenses and cater to adult learners requiring more flexibility [16].

Implementing CBE involves significant curricular, instructional, and evaluative implications [17]. Regarding curricular implications, it emphasizes the importance of evaluating training objectives to create a curriculum centered around topics that integrate multiple disciplines. For example, understanding cellular organization requires competence that combines biology and chemistry rather than studying them separately. Regarding didactic implications, there is a need to shift towards student-centered and active learning approaches, involving students in actively constructing knowledge. Collaboration between students and teachers is crucial in assessing and achieving meaningful learning, as seen in examples like problem-based and challenge-based learning. Assessment poses a significant challenge in CBE, transitioning from outcome-based to process-based. This shift evaluates the results and the entire learning process, which may necessitate substantial changes in the educational system.

CBE can also positively impact economic growth and reduce inequality [18]. While disciplinary competencies have been shown to affect earnings, recent studies suggest that transversal competencies also offer economic advantages. Developing both competencies is crucial for success in the labor market and for promoting economic growth and equality [18].

CBE has limitations as it only focuses on observable results and ignores the connection between thought, performance, and context. It assumes that competencies can be reduced to observable behaviors and that correct performance will always lead to desired outcomes [14]. However, some competencies are challenging to quantify using CBE’s functional indicators, and defining competencies based on minimally acceptable performance may promote settling for mediocrity. CBE prioritizes accountability and assessment over practical teaching and learning but there is a need for standardization to better serve diverse learners and settings [14].

The implementation of CBE can be tailored to each institution’s specific characteristics and requirements. It does not necessarily require a radical renovation but can be integrated into the existing course structure. The flexibility of CBE allows for a combination of traditional courses and integrative learning experiences focused on competencies [17].

1.2. Implementation of CBE at Tecnologico de Monterrey

At the Tecnológico de Monterrey, various initiatives regarding the exploration of the CBE model guided its incorporation into the institutional educational model Tec 21. This educational model was implemented for the first time in August 2019. It was consolidated after extensive benchmarking with the best universities in the world, and consists of four pillars: challenge-based learning, inspiring professors, memorable experiences, and flexibility. This new model is totally different from the traditional learning–teaching system, and its goal is to develop disciplinary and transversal competencies in students to face current world challenges [19].

In 2006, the competence profile for graduates of the School of Engineering and Sciences was developed. The main task was defining transversal (or generic) and disciplinary competencies specific to each program’s graduates. The goal was to establish a robust profile of graduates’ competencies that met accreditation requirements [17]. This profile was developed for the School of Medicine and Health Sciences and the School of Postgraduate Studies in Education.

In 2008, the Tecnológico de Monterrey initiated a project to define and assess graduate competencies in all professional programs offered by the institution. The project aimed to systematically evaluate the learning outcomes of academic programs, ensuring that students developed defined graduate competencies, promoting continuous improvement as part of academic quality monitoring, and meeting international accrediting agencies’ criteria.

The internally developed System for Managing Academic Program Evaluation (SMAPE) was created to support each project stage. SMAPE enabled the evaluation process for all programs, providing access to graduation profiles, evaluation results, and commitments to continuous improvement. The system also facilitated faculty members to evaluate students’ graduate competencies and document resulting evidence, which was crucial for successful accreditation processes from external, national, and international agencies. Relevant data include 100% participation of academic programs across all campuses and the evaluation of 1668 graduate competencies in the 2014 cycle, with 1541 closing the improvement cycle by establishing concrete improvement actions and plans. Evaluated graduate competencies were part of the graduation profiles of 208 program-campus combinations out of the 224 offered. The evaluation process involved program and department directors, and all faculty members teaching courses from the sixth semester onwards. The project facilitated accreditation processes for business programs by the Association to Advance Collegiate Schools of Business (AACBS), engineering programs by the Accreditation Board for Engineering and Technology (ABET), and institutional accreditation by the Southern Association of Colleges and Schools (SACS).

In 2011, a CBE model was implemented at one campus. This model allowed for the measurement of students’ competence development at the beginning, middle, and end of their academic journey. It also facilitated effective feedback to the academic and co-curricular areas involved in student education. To achieve this, dedicated teams were formed in each of the programs offered at the campus to create activities assessing students’ competency development through an Assessment Center exercise. This included evaluations at the midpoint and completion of their studies. Collaborative practices conducted by professors and industry peers resulted in valuable feedback to enhance teaching activities and co-curricular offerings for the benefit of the students [17].

Since 2019, the Tec 21 model has been applied to all careers and campuses of Tecnologico de Monterrey. Under this model, the disciplinary and transversal competencies and sub-competencies of students in every career and course are collegiately evaluated by teams of faculty members in collaboration with training partners.

Structure of Disciplinary Competencies of the Biotechnology Engineering (BE) Program at Tecnologico de Monterrey

The Biotechnology Engineering (BE) program at Tecnologico de Monterrey, plan 2019, is offered by the Engineering and Sciences School under the guidelines of the Tec 21 model. The BE program is composed of four disciplinary competencies (BT). In turn, each competence is subdivided into three possible sub-competencies, and each sub-competence owns three development levels (A, B, C), with A being the basic level and C being the most advanced level of the sub-competence.

Table 1. Disciplinary competencies and sub-competencies of the Biotechnology Engineering program at Tecnologico de Monterrey, Tec 21 model [20].

Competence	Competence Description	Sub Competence	Sub Competence Description
BT1 (Bioproduct development)	Develop technologies and biosystems using biological or molecular information based on the needs of industry and society.	BT1.1 (Proposal of biotechnology products or services)	Proposes innovative products or services with cutting-edge biotechnological elements based on qualitative and quantitative analysis of the corresponding environment.
		BT1.2 (Generation of expression systems or genetically modified organisms)	Generates expression systems or genetically modified organisms using molecular biology and genetic engineering techniques.
		BT1.3 (Evaluation of the Feasibility of Biotechnology Projects)	Evaluates a biotechnological project’s feasibility under existing technical, sustainability, and bioethical criteria.
BT2 (Bioreactor Design)	Design bioreactors capable of supporting the specific needs of cells in generating products of interest.	BT2.1 (Definition of Bioreaction Parameters and Strategies)	Defines the required parameters and the fermentation strategy for the bioreactor design, considering the product specifications and the production system.
		BT2.2 (Bioreactor Performance Evaluation)	Evaluates the design and performance of a bioreactor seeking its optimization through mathematical models and information technologies.
		BT2.3 (Engineering Design Diagnosis)	Diagnoses the engineering design of the bioreactor considering technical, innovative, bioethical, sustainability, and regulatory aspects.
BT3 (Bioprocess Design)	Design purification processes for biotechnological products based on market specifications and sustainability principles	BT3.1 (Establishment of the Bioprocess Stages)	Systemically establishes the stages of a bioprocess based on product specifications and the production system.
		BT3.2 (Development of Bioprocess Sizing)	Develops the sizing and scaling of bioseparations unit operations according to the material required to be processed and obtained.
		BT3.3 (Bioprocess Performance Evaluation)	Evaluates the performance of a bioprocess seeking its optimization through mathematical models and information technologies that include innovative, bioethical, sustainability, and regulatory elements.
BT4 (Innovation Management)	Generates knowledge management and innovation strategies in biotechnology to create or improve technology-based products, services, or companies	BT4.1 (Rationale for Biotechnology Development)	Argues for biotechnological development using effective oral and written communication tools.
		BT4.2 (Planning the Innovation Process)	Plans the innovation process considering the type of biotechnological development.
		BT4.3 (Establishment of the Strategy for Obtaining Resources)	Establishes the strategy for obtaining and managing resources according to the type of biotechnological development.

shows the four BT competencies, sub-competencies, and their corresponding descriptions.

Each sub-competence and level are assigned to a specific course of the study plan (

Table 2. Courses and their relationship with disciplinary sub-competencies and competencies of the BE program, Tec 21 model [20].

Competence	Subcompetence	Levels of Subcompetence Development
		A	B	C
BT1 Bioproduct development	BT1.1	Analysis and Study of Biosystems	Preparation of Biotechnological Products	In Vitro experimentation
	BT1.2	Foundation and Application of Molecular Bases	Synthesis of Biofactories	Application and Analysis of Omic Technologies
	BT1.3	Preparation of Biotechnological Products	Integration of Transfer Operations	Design of Biotechnological Processes and Bioproducts
BT2 Bioreactor design	BT2.1	Analysis of transport Phenomena		Designs of Biorreactors
	BT2.2	Prospection of Bioprocesses	Design of Biorreactors	Planning of Biotechnological Processes
	BT2.3			Design of Biotechnological Processes and Bioproducts
BT3 Bioprocess design	BT3.1	Analysis of transport Phenomena	Integration of Transfer Operations	Design of Bioseparation Strategies
	BT3.2	Prospection of Bioprocesses	Design of Bioseparation Strategies	Design of Biotechnological Processes and Bioproducts
	BT3.3
BT4 Innovation Management	BT4.1	Synthesis of Biofactories	In Vitro experimentation	Planning of Biotechnological Processes
	BT4.2	Preparation of Biotechnological Products	Planning of Biotechnological Processes	Design of Biotechnological Processes and Bioproducts
	BT4.3

). A course can develop multiple competencies or sub-competencies.

During the corresponding sub-competence level evaluation, the course teacher assigns the status “Observed” or “Not Observed” according to the observed performance of the student. This process is carried out at the end of the course through a Learning Management System (LMS).

In Spring 2023, the first cohort under this new BE program will graduate. Hence, general results related to the BE students’ academic performance and their disciplinary competencies have yet to be obtained, since the implementation of the Tec 21 model is relatively new. As these students approach graduation, they are expected to be ready for their professional lives. Therefore, the influence of certain factors or variables on the development of these disciplinary competencies requires further study.

The objective of the present work is to diagnose the current state and advance the disciplinary competencies in undergraduate students of the BE program at Tecnologico de Monterrey in the first cohorts (2019 and 2020) under the teaching–learning model Tec 21. The questions that the present research aims to answer are the following:

• What is the Competence Achievement Degree (CAD) for each disciplinary competence of students in the Biotechnology program (BE) as a function of the cohort?
• What is the CAD of disciplinary competencies of students in the BE program as a function of each sociodemographic and academic variable (age, gender, nationality, campus region, provenance, admission origin, and load)?
• Which of these variables affects CAD for each competence?
• What are the strong and weak competencies of biotechnology undergraduates?
• Does the CAD correlate with the Competence Average Grades (CAGs) for each disciplinary competence and cohort?

2. Materials and Methods

In this work, the characteristics of the studied population and a descriptive panorama of the disciplinary competencies of the BE program, considering sociodemographic and academic variables (age, gender, nationality, provenance, campus region, admission origin, cohort, load), are described. Subsequently, the analysis for each competence in the function of which of these variables affects the CAD is presented. Finally, the correlation of the CAD with the students’ obtained CAGs was researched. These results represent valuable information and feedback for the Tec 21 model in the BE program.

2.1. Data Gathering and Cleaning

The Data Hub and Living Lab of the Institute for the Future of Education at Tecnologico de Monterrey provided access to original data (see Data Availability Statement). The original raw data file contained the entire data of the BE program inscribed up to the Fall of 2022, containing information such as sociodemographic variables, complete student courses, competencies, grades, and academic parameters, many of which were not of interest to the current research. Then, the raw .csv file with 46 columns was purged through an algorithm developed in Python 3.8.10 (Python Software Foundation, Wilmington, DE, USA). The data were cleaned using Panda’s routines, an open-source data analysis and manipulation library based on Python programming [21]. The .csv file was saved within a DataFrame data structure containing 516,426 rows and 46 columns.

After reducing the DataFrame to 16 columns, the processing algorithm matched the four competencies’ respective sub-competences and interrelated courses (Table 2). During each match, the algorithm considered the “sucompetence.level_assigned” variable indicated as “Observed” or “Not Observed” and the “student_grades.final_numeric_afterAdjustment” variable, containing the corresponding grade between 0 and 100. Every time the “Observed” indication appeared among the competence/sub-competence level assigned, the code gave 1 point to the student for counting the qualitative teacher observation of the competence in a discrete variable called “Competence score”. An Excel-type file was generated per analyzed competence. The columns contained the student ID and cohort, the sociodemographic and academic variables defined in

Table 3. Definition of sociodemographic and academic variables.

Variable	Concept	Values
Age	Chronological number of the student since their birth date	From 18 to 30
Gender	Student categorization according to their physical and physiological features as established in their official paperwork	Female, Male
Nationality	Official state of belonging of the student to a particular country	Non-Mexico, Mexico
Campus region	Location of the campus where the student is enrolled, considering the country’s different regions.	Mexico City (RCM), Centre-South (RCS), Monterrey (RM), Occident (RO)
Provenance	Denotes if the student’s residence is in the same city as the Campus where they are enrolled or if they come from another city	Foreign, Local, Missing
Admission origin	Indicator that denotes whether the student comes from a school that belongs to the Tecnologico de Monterrey system	No TEC, TEC
Cohort	Year of admission of the student to Tecnologico de Monterrey	2019, 2020
Load	Indicates whether the student is a full-time student (value equal to 1) or the equivalent status according to the number of subjects enrolled	0.5, 0.9, 1, 1.25

, and additional columns with the obtained grades in the courses matching the analyzed competence.

Because the BE students of the considered cohorts have yet to take all the courses of the program, some courses and sub-competence levels were omitted from our analysis. Specifically, under this consideration, the sub-competences not considered were BT1.3, BT2.3, BT3.2, BT3.3, BT4.2, and BT4.3.

2.2. Definition of Academic Performance Variables

The Excel-type file for each competence was processed, including additional columns for estimating the Desired Competence Score (DCS), the Competence Achievement Degree (CAD), and the Competence Average Grade (CAG). Equations (1)–(3) describe the estimation of these parameters, respectively:

DCS = ∑ T C _i

(1)

CAD = (CS/DCS) × 100

(2)

CAG = (∑ G C _I)/n

(3)

where Tci is the number of related competence courses taken, CS is the competence score, and Gci is the obtained grade in the related competence course.

A second data treatment in Excel was performed, keeping only regular students (those with less than 11 failed courses until the previous academic period), and only undergraduates belonging to the cohorts 2019 and 2020 were filtered.

2.3. Competences Analysis and Statistics

The statistical analysis was performed using the software Minitab^® 21.4.1 and Microsoft Excel. The student populations studied in the present work were described using descriptive statistics [22]. Initially, for competencies BT1 and BT4, a two-sample t-test was done to compare if the cohorts 2019 and 2020 were similar regarding CAD. There were no students in the analysis of BT2 and BT3 since the courses developing those competencies had yet to be completed by the 2020 cohort. The two-sample t-test aimed to determine if the median of two population groups is different; in this case, we were comparing if the CAD for the 2019 cohort was statistically equal to the CAD for the 2020 cohort.

To research if determinate variables had a relationship with the CAD of Biotechnology students in the four BT competencies, the data in variables (age, gender, nationality, provenance, campus region, admission origin, and load) and CAD response were grouped into intervals and analyzed using individual Chi-Squared Tests.

Once associated variables with CAD were identified in each BT competence, absolute frequencies of CAD groups per variable were obtained and graphed, including relative frequencies.

Finally, a correlation analysis established a relationship between the CAD and the Competence Average Grades (CAGs) obtained in the formation units (courses), generating the corresponding matrix plots.

3. Results

3.1. General Population Indicators

The total population of BT students in the current study was 1072, belonging to the 2019 and 2020 cohorts. Of the studied population, 64.8% were women, 35.2% were men, 96.2% had Mexican nationality, and 56.1% were local students. The average age of the whole population was 19.3 ± 0.9 years. The distribution of this population according to regions was as follows: 26.8% Mexico City (RCM), 30.0% Center-South (RCS), 26.5% Monterrey (RM), and 16.7% Occident (RO).

Subpopulations for the analysis per competence were required. For competence BT1 (Bioproduct development), the population was 1072 students; 569 were in the 2019 cohort, and 503 were in the 2020 cohort. For competencies BT2 (Bioreactor design) and BT3 (Bioprocess design), 476 students were considered, all being part of the 2019 cohort. A total of 1058 undergraduates took part in the competence BT4 (Innovation management) analysis, with 563 and 495 belonging to the 2019 and 2020 cohorts, respectively.

3.2. BE Competencies Profiles by Cohorts

To answer the first research question (RQ1), the two-sample t-test for the BT1 competence indicated that the cohort 2019 with 569 students and a CAD average (CADav) of 94.95 ± 9.83 was different (p-value = 0.035) from the cohort 2020 with 503 students and a CADav of 93.5 ± 12.9. Instead, CADav was not significantly different (p-value = 0.205) for cohort 2019 in the BT4 competence with 563 students, a CADav of 97.15 ± 9.95, and for cohort 2020 with 495 students and a CADav of 97.88 ± 8.71.

3.3. Effect of Socio-Demographic and Academic Variables on CAD

To answer the second to the fourth research questions (RQ2-QR4), the association test results for the four disciplinary BT competencies are summarized in

Table 4. Association test results for the four competencies of the Biotechnology undergraduate program.

Variable	p-Value	Significance Level	Interpretation
Competence BT1-2019
Age	-	-	Indeterminate
Gender	0.091	10%	Association
Nationality	0.048	5%	Association
Campus region	0.001	5%	Association
Provenance	0.145	10%	No association
Admission origin	0.342	10%	No association
Load	0.001	5%	Association
Competence BT1-2020
Age	-	-	Indeterminate
Gender	0.297	10%	No association
Nationality	0.547	10%	No association
Campus region	0.000	5%	Association
Provenance	-	-	Indeterminate
Admission origin	0.477	10%	No association
Load	-	-	Indeterminate
Competence BT2-2019
Age	-	-	Indeterminate
Gender	0.556	10%	No association
Nationality	0.547	10%	No association
Campus region	0.000	5%	Association
Provenance	0.000	5%	Association
Admission origin	0.048	5%	Association
Load	0.698	10%	No association
Competence BT3-2019
Age	-	-	Indeterminate
Gender	0.873	10%	No association
Nationality	0.295	10%	No association
Campus region	0.012	5%	Association
Provenance	0.412	5%	No association
Admission origin	0.63	10%	No association
Load	0.669	10%	No association
Competence BT4-2019
Age	-	-	Indeterminate
Gender	0.529	10%	No association
Nationality	-	-	Indeterminate
Campus region	0.003	5%	Association
Provenance	0.021	5%	Association
Admission origin	0.904	10%	No association
Load	-	-	Indeterminate
Competence BT4-2020
Age	-	-	Indeterminate
Gender	-	-	Indeterminate
Nationality	-	-	Indeterminate
Campus region	0.127	10%	No association
Provenance	-	-	Indeterminate
Admission origin	0.250	10%	No association
Load	-	-	Indeterminate

.

As observed, in some variables, it was not possible to determine if the variable has a certain association with CADs since the obtained frequencies in some variable intervals were insufficient for validating the test [23], resulting in an indeterminate interpretation. The observed association between variables occurred at a high significance value (10%) in several cases.

In the BT1 cohort 2019, four variables are directly associated with CAD (gender, nationality, region, and load), and in cohort 2020, the Campus region variable was the only one showing an association. For both cohorts, the association of only one or a maximum of two variables was indeterminate. In BT2, cohort 2019, three variables affect CAD (region, provenance, and admission origin).

The BT3 competence cohort 2019 CAD is associated with the Campus region, and for BT4, region, and provenance had significant associations with CAD, which were not observed for the 2020 cohort. This last cohort and competence showed major associations with indeterminate variables.

3.4. Descriptive Panoramic of BE Competencies by Associated Variable

The frequencies of CAD for each associated variable for the BT1 cohort 2019 are presented in Figure 1.

In terms of gender, more women had outstanding CADs (>90%) (Figure 1A). Still, despite the women population being greater than men, 72–74% of individuals of each gender had CADs >90%, and an equal number of females and males had a CAD less or equal to 75% (3.5–3.7%). Non-Mexican students got outstanding CADs only (Figure 1B).

The Mexico City (RCM) and Center-South (RCS) regions had the highest percentages (72%) of students achieving CADs >90% compared to Monterrey (63.1%) and Occident (56.1%) regions. At the same time, the RCM region had the lowest number of students with deficient CADs (1.9%) compared with the other three regions (7–10%) (Figure 1C).

There were no overloaded students with CADs ≤ 75%, and most students (71–78%) had good performance in their competencies, independent of their academic load, with 17% of students with 0.9 < Load ≤ 1.1 reaching 75 < CAD ≤ 90% in comparison with the 0.7–2.3% of the population in the other CAD categories (Figure 1D).

The BT1 competence cohort 2020 CAD only showed an association for the Campus region variable. The percentage of students with CADs > 90% was 81.8% for RM, 73.8% for RCM, 76.4% for RCS, and 52% for RO. In RO, 40.8% of the BE students of this campus had moderated CADs (75 < CAD ≤ 90%) (Figure 2).

As observed (Figure 1C and Figure 2), the behavior of the CAD in the two cohorts (2019 and 2020) for BT1 was different. While RCM had high CADs in 2019, the CADs of RM were higher in 2020. The RO region had more deficient CADs in 2020. The relative frequency in this CAD category decreased in RCS and RM from 7–10% to approximately 4.5–5%.

The BT2 competence in cohort 2019 presented up to 18.8% of the deficient CAD interval in RCS, with RCM having the lowest percentage of CAD > 90% (62.8%), while the other regions had major relative frequencies of outstanding CADs (71.1% for RCS, 80.5% for RM, and 87.5% for RO) (Figure 3A).

Also, in this BT2 competence, local students had high frequencies (13.7%) of deficient CADs (≤ 75%), with 69.7% of CADs > 90%. These numbers were 3.4% and 83.1% for the students from other cities (Figure 3B). Similarly, the group of students who attended their High School at the TEC system presented fewer students (69.4%) with CAD >90% than those who did not (78.7%). Students with a TEC origin got slightly more deficient CADs (10.0%) compared to students with no TEC origin (8.6%) (Figure 3C).

Figure 4 displays the CAD groups for each campus region in the competence BT3 (Bioprocess design) for the cohort of 2019.

RCS had more students (14.8%) with CADs ≤ 75% and fewer students (77.3%) with CADs > 90%. The remaining areas (RCM, RM, and RO) had 81–83% of their students with outstanding CADs, keeping the deficient CADs between 4–9%.

CADs’ distribution for competence BT4, cohort 2019, is indicated in Figure 5.

The region with the lowest and the highest percentage of BE students with deficient and outstanding CADs was RCM (0.7% and 94.8%, respectively) (Figure 5A). The other regions had between 84% and 90% of their populations with CADs > 90%. RCS had more students with deficient CADs (12.2%). The variable provenance in this dataset showed that foreign students develop BT4 competence with more frequency (93.7%) compared to local students (85.7%) (Figure 5B).

3.5. Correlation between CAD and CAG

To answer research question 5 (RQ5), a correlation analysis between CAD and CAG was done in Minitab for each competence and cohort. All the results are concentrated in

Table 5. Results of correlations between CAD and CAG in BT competencies.

Competence	Cohort	CADav	CAGav	Correlation Coefficient	95% Confidence Interval
BT1	2019	94.95 ± 9.83	93.41 ± 5.27	0.577	0.519–0.629
BT1	2020	93.46 ± 12.86	92.95 ± 4.29	0.259	0.715–0.339
BT2	2019	93.95 ± 14.62	91.99 ± 8.50	0.659	0.605–0.707
BT3	2019	94.46 ± 15.67	92.16 ± 8.63	0.570	0.506–0.627
BT4	2019	97.15 ± 9.95	94.33 ± 5.25	0.604	0.548–0.654
BT4	2020	97.88 ± 8.71	93.55 ± 4.20	0.238	0.153–0.320

, which indicates a CAD average (CADav) and CAG average (CAGav), the Pearson correlation coefficient, and the confidence interval. All the correlations were positive, indicating a linear relationship.

The best correlation was observed for BT2 (bioreactor design) (0.659), followed by BT4 cohort 2019 (0.604). In general, cohort 2019 correlated well with CAG in the four competencies; cohort 2020 of BT1 and BT4 were the least correlated (coefficients around 0.230–0.260). Figure 6 shows the matrix plots for each disciplinary competence and cohort.

Matrix plots with wide data distribution along the x and y axes suggest a low correlation between CAD and CAG, as happened with the BT1 cohort 2020 (Figure 6B) and the BT4 cohort 2020 (Figure 6F). On the other hand, data concentration is observable in highly correlated variables, such as the BT2 2019 (Figure 6C) and BT4 cohort 2019 (Figure 6E).

4. Discussion

Regarding the interpretation of the performed t-tests for the 2019 and 2020 cohorts in BT1 and BT4 competencies, it could be observed that for BT1, the reviewed cohorts have different behavior in CADs, while for BT4, CADav were similar. This may be correlated with the competence strength since BT4 (innovation management) is a competence highly promoted at Tecnologico de Monterrey along the entire academic trajectory [24,25]. It is well reflected in the high average CADs for both cohorts. Meanwhile, CAD between cohorts for BT1 (bioproduct development) differed; student population characteristics must have a particular role, being an observed marked behavior in some other works.

The explanation for these changes in the dynamic of CAD in BT1 could have been affected not only by the natural diversity in student cohorts but also by the addressed changes in the campuses for attending teaching–learning of the BE students during the COVID-19 pandemic because many of the undergraduates belonging to cohort 2019 had to acquire biotechnology bases in a theoretical way rather than practical [26].

4.1. Effect of Sociodemographic and Academic Variables on CADs

The relevant differences and effects observed in CADs for BT2 competence may be affected by the different ways in which these classes are attended in the four regions since this competence deals with several related sub-skills and specific knowledge in the engineering area, it is multidisciplinary for biotechnologists involving particular infrastructure and topics ranging from biological structure to engineering–technology applications [27]. Similarly, improved CADs were better reached with international students for provenance and international students for admission origin. However, it is well known that international students have specific academic and social needs [28,29]. In some research works, this group has had a worse academic performance than local or domestic students [30]. The better CADs for international students may find a partial explanation in the fact that the acquisition of this competence needs attention. International students focus more on their studies, while local students are distracted frequently [29]. It may be argued that due to the multidisciplinary of BT2 competence, it is not expected that the development of the competence is affected by light differences in how this was approached by campuses and the background and experiences of the students related to their provenance and admission origin variables; this is because not all have been exposed to the same knowledge bases and involved sub-skills.

The high frequencies of CADs ≤ 75% in RCS for 2019 can be explained because, despite the effort of Tecnologico de Monterrey in foresting the entrepreneurial culture, it is not frequently set in practice in the center/south of Mexico; evidence of this is that the context for developing innovation and entrepreneurship skills is a key element, as described by Portuguez-Castro and collaborators [31]. These authors point out the favorable conditions that the state of Nuevo Leon has for foresting these competencies because they found that these were learned from companies and other entrepreneurs. As remembered, Monterrey region (RM) is located at Nuevo Leon, and the analysis of competence BT4 presented outstanding CADs.

4.2. Relation between CADs and CAGs

It is evident that although a moderate correlation degree was identified for determined BT competencies between CADs and CAGs, it is expected to have a major correlation degree, for example, 0.8 or greater. The results suggest that the student’s performance during the CAG’s courses must appropriately reflect the competency acquisition measured in CAD. In this sense, part of the observed divergence between the CAD and CAG could be due to the dichotomous evaluation of the competencies since it only allows for deciding whether the student has the competencies (“observable” and “non-observable” criteria) or not; in certain cases, a student could have demonstrated the partial presence of the competence, and the criterium among teachers could have been distinct. The way to solve this problem is to design and use a more suitable instrument and scale in the competence’s evaluation, such as rubrics, which aim to offer an accurate and fair assessment of the student work quality based on qualitative and quantitative relevant dimensions, avoiding subjectivity and arbitrariness [32]. In this line, since the summer of 2022, it has been proposed to set up rubrics with four performance degrees to evaluate sub-competencies at Tecnologico de Monterrey [33]. In CBE, rubrics can be holistic or analytical, and they help teachers assess student performance and provide feedback according to specific dimensions of knowledge and skills [34]. They can be used to obtain detailed diagnostic information as a basis for instructional decisions and developed by a single or a group of expert teachers, or even with the involvement of students [35]. Rubrics are helpful for learning and can improve student performance or motivation by increasing transparency, reducing anxiety, aiding the feedback process, improving student self-efficacy, or supporting student self-regulation [36].

A second cause of the scarce correlation between competency evaluation and grades for the first cohorts of the BE students of the Tec 21 model at Tecnologico de Monterrey could be that the assessment of competencies occurs only in some evaluation instruments but not integrally. These are included in other activities considered in the final grade, so the proposal to address this problem may be based on re-designing the evaluation plan for a more constant evaluation through the courses.

An interesting finding of these data analyses is that the competencies and cohorts having the lower Pearson coefficients (BT1 cohort 2020 and BT4 cohort 2020) also were the cases in which scarce association with socio-demographic or academic variables were visualized, as well as the analysis presenting more indeterminate results (three for BT1 2020 and five for BT4 2020). This must be widely researched through future cohorts and trying for another type of non-linear correlation or testing additional variables.

4.3. Limitations

The present study does not consider other variables or indicators in the competence evaluation, for example, evaluation instrument, teacher-associated variables, and teaching–learning implementation process. These have been considered under an equality criterion in all Tecnologico de Monterrey systems, and considerations have been based on the scope of the provided data. Moreover, it was assumed that the teaching classes and evaluation were homogeneous among groups and professors since one of the aims of the Tec 21 model is to standardize the teaching and academic quality, particularly in the School of Engineering and Sciences [37].

Another limitation derived from the nature of the data is that competence analysis is restricted to the courses taken for the BE students until the cut-off date of data (Spring Term 2022), leaving outside our estimation of the grades and sub-competence evaluation of both studied cohorts. For example, cohort 2019 has completed all their competencies and sub-competence evaluations for the end of Spring 2023. However, data still need to be processed and released.

4.4. Implications for Practice and Future Research

The information obtained from the current research can be useful for improving the BE program at Tecnologico de Monterrey by introducing new pedagogies to develop deficient competencies in the students and considering the studied variables. In the same way, this will allow us to make adequate decisions in the plan implementation for prospective cohorts in the BE program, guaranteeing academic quality and the integral formation of undergraduates according to the institutional mission. As part of future research, the nature of the associated variables to CAD in each competence must be investigated, including any other variables not considered in the current work; for example, how teachers do the collegial assessment of competencies.

As observed, a better correlation between the competency assessment and grades must be guaranteed, looking for the most adequate evaluation instruments [38]. In the same way, this will allow us to make proper decisions in the plan implementation for prospective cohorts in the BE program, guaranteeing academic quality and integral formation of undergraduates according to the institutional mission and the Strategic Plan 2025. The consolidation and adequations to the Tec 21 model are one of the seven initiatives [39]. Also, the present work offers a strategy and methodology for the treatment and analysis of the recent information generated in the Tec 21 model, not only for the BE program but also for other programs inside this framework.

5. Conclusions

In summary, the strongest disciplinary competence of the four analyzed for the 2019 cohort was BT4 (innovation management). This same competence was strong for the 2020 cohort. There were no weak disciplinary competencies for BE undergraduates since the other three analyzed competencies, BT1 (bioproduct development), BT2 (bioreactor design), and BT3 (bioprocess design), showed no significant differences between them. For the BT2 competence in the 2020 cohort, the variables of campus region, provenance, and admission origin had an association with CAD, while for the BT3 competence of bioprocess design, only region affected CAD. The Campus region variable is most associated with CAD through the four BT competencies. The correlation analysis between CAD and CAG indicated a moderate correspondence between both variables in the evaluation process of BE students at Tecnologico of Monterrey for the cohort 2019; however, BT1 and BT4 competencies in the cohort 2020 had a low correlation with CAG.