Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

Abstract

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.

Data Set:https://0-doi-org.brum.beds.ac.uk/10.57687/FK2/PWJRSJ.

Data Set License: CC0

1. Introduction

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. Dropping out from higher education represents a cost for the government and society, an unnecessary expense for the family, and an experience of failure for the university student [1,2]. Therefore, the early identification of at-risk students and understanding of the main factors of dropping out have recently attracted a great deal of research interest [3,4,5]. Early detection of at-risk students allows higher education institutions to offer individualized assistance in varied forms, including remedial courses and tutoring sessions to mitigate academic failure.

The rich data accumulated in educational administrative systems together with the emergence of efficient Statistical and Machine Learning methods have opened novel approaches to address the problem of student attrition, generating a new line of research. In the last few years, a high number of predictive analytical educational research papers have been published and Artificial Intelligence-based decision support systems have been developed to assist stakeholders in higher education [6,7,8]. For example, the application of Learning Analytics in higher education institutions can provide quality and actionable information to implement educational interventions, such as timely support for at-risk students of dropping out [9,10].

Most dropout prediction studies rely on pre-enrollment achievement measures (high school grades, assessment tests) and personal details [11,12,13,14]; some also consider first-semester university performance indicators [12,15], such as course grades [16]. On the other hand, other factors were also found to have incremental predictive power on academic performance and retention such as on/off-campus housing [17], socioeconomic status [11,18,19], psychological factors such as coping and emotional intelligence [20], and schooling background of parents [21]. Notwithstanding, ethical principles on the collection and use of educational data should be proposed and applied with the aim of protecting the privacy of students, such as the ethical principle of considering student performance as a dynamic variable [22].

In Latin America, college access grew dramatically in the early 2000s, and particularly for those students from middle and low-income segments [23]. Most of these ‘new students’ enrolled in new private programs, relying on the recent growth of middle-class family incomes, student loans, and scholarships [24]. Although the coverage expansion of higher education systems was crucial for knowledge production and social mobility, it generated major challenges regarding quality and equity. According to Lemaitre [25], 50% of students drop out and only 50% of the remaining ones graduate on time. Considering that low-income students are the ones at higher risk of dropping out and being disfavoured by disparities in lifetime earnings [26], there is an urgent need to improve higher education quality in the region and reduce dropout rates [23,27]. In this context, data-based strategies are seen as an opportunity to tackle issues related to these problems, such as providing personalized feedback and support to an increasing number of learners [27].

Therefore, in order to support the prediction of student dropout and increase student retention rates, a student dataset has been gathered and curated based on the related work and the retention prediction model developed for Tecnologico de Monterrey within the early alerts program. This program is a project whose purpose is to provide timely and reliable information in the follow-up process to high school and undergraduate students according to their information and their retention indicator. Although retention rates at the institution have increased from 91.2% in High School and 89.9% in Undergraduate in 2014 to 94.5% in High School and 92.1% in Undergraduate in 2020, new or disruptive models are needed to identify all at-risk students in an effective and timely manner. A call for proposals was launched to research and develop solutions based on this dataset using Machine Learning algorithms [28]. According to the proposals received, the dataset was enriched with more variables related to student life and dropout time. Resulting in a dataset of 50 variables and 143,326 records.

The rest of the descriptor is organized as follows. Section 2 provides the context and detail description of the student dataset. Then, Section 3 provides the methodology carried out to collect, preprocess, preserve, and explore the proposed dataset, mentioning the materials and methods used as well as presenting a brief exploratory analysis of the dataset. Finally, Section 4 gives the conclusions.

2. Data Description

The Tecnologico de Monterrey is a university in Mexico made up of 29 campuses and 18 offices around the world. The institution has a total current population of 94,424 students, of which 26,794 are in High School, 60,169 in Undergraduate, and 7461 in Postgraduate programs [29]. In the dataset given through this descriptor, non-identifiable information is provided for 121,584 High School and/or Undergraduate students who have enrolled at Tecnologico de Monterrey. The information corresponds to seven admission cohorts to the institution from 2014 to 2020; that is, August–December 2014 (AD14), August–December 2015 (AD15), August–December 2016 (AD16), August–December 2017 (AD17), August–December 2018 (AD18), August–December 2019 (AD19), and August–December 2020 (AD20).

The dropout rates in the institution have decreased from 8.8% in High School and 10.1% in Undergraduate in 2014 to 5.5% in High School and 7.9% in Undergraduate in 2020. However, in the 2015–2016 period, the dropout rates increased from 7.3% to 7.6% for High School, as well as in the 2018–2019 period from 7.5% to 9.4% for Undergraduate. Therefore, it is necessary to continue researching and developing models and strategies for student retention.

Among the categories of information available in this dataset are:

• Sociodemographic information, such as age, gender, and type of zone to which the student’s address belongs.
• Enrollment information, such as program, school, and educational model.
• Academic information related to the student, such as the average of the previous level, the average in the first term or midterm of the first semester, and the number of failed subjects.
• Information associated with scores on admission tests, such as the admission test, standardized English proficiency test, and Mathematics grade.
• Academic history, such as type of school from provenance, national/international student, and relationship with the Tecnologico de Monterrey system.
• Student life, such as participation in sports, cultural, and leadership activities.
• Scholarship and financial aid information, such as type of scholarship, percentage of scholarship, and percentage of scholarship loan.
• Academic information related to the student’s parents, such as educational level and whether the parents were students of the Tecnologico de Monterrey.
• Information on the student’s retention or dropout in the first year.

Table 1. Description of the attributes of the student dataset (Part I).

No.	Attribute	Data Type	Description	Values
1	student.id	Integer	Masked enrollment number of the student. There are duplicate student identifiers (IDs) as one identifier may be related to a different educational level: High School or Undergraduate. In addition, there are some student IDs that are repeated three times due to those students have additional information related to different generations.	1-121584
2	generation	String	Unique indicator that denotes the generation to which the student belongs.	AD14, AD15, AD16, AD17, AD18, AD19, AD20
3	educational.model	Binary	Educational model to which the student belongs.	1: TEC21 Model, 0: Previous educational model
4	level	String	Educational level to which the student belongs.	High School, Undergraduate
5	gender	String	Student gender.	Male, Female
6	age	Integer	Student’s age.	Range from 13 to 55 years
7	zone.type	String	Description of the type of zone to which the student’s address belongs.	Rural, Semiurban, Urban, No information
8	socioeconomic.level	String	Socioeconomic level of the student.	Level 1, Level 2, Level 3, Level 4, Level 5, Level 6, Level 7, No information
9	social.lag	String	It indicates the level of social backwardness at the level of urban areas of the student’s address according to the zip code.	Low, Medium, High, No information
10	id.school.origin	String	Masked identifier of the school where the student comes from.	Range from “School 0” to “School 10242”.
11	school.cost	String	Classification of the tuition cost of the student’s school of origin.	Public, Low cost, Medium cost, Medium-high cost, High cost, Not defined
12	tec.no.tec	String	Indicator that denotes if the student comes from a school that belongs to Tecnologico de Monterrey.	TEC, NO TEC
13	max.degree.parents	String	Highest educational level obtained by the student’s parents.	No information, No degree, Undergraduate degree, Master degree, PhD
14	father.education.complete	String	Description of the last educational level completed by the father.	Attended university, but did not graduate; Graduated from elementary or middle school; Graduated from high school; None educational degree; Received master degree; Received PhD; Received technical or commercial degree; Received undergraduate degree; No information
15	father.education.summary	String	Classification of the last educational level completed by the father.	No information, No degree, Undergraduate degree, Master degree, PhD
16	mother.education.complete	String	Description of the last educational level completed by the mother.	Attended university, but did not graduate; Graduated from elementary or middle school; Graduated from high school; None educational degree; Received master degree; Received PhD; Received technical or commercial degree; Received undergraduate degree; No information
17	mother.education.summary	String	Classification of the last educational level completed by the mother.	No information, No degree, Undergraduate degree, Master degree, PhD
18	parents.exatec	String	Indicator that denotes if either of the parents is an exatec (was a student at Tecnologico de Monterrey).	Yes, No, No information
19	father.exatec	String	Indicator that denotes if the student’s father is an exatec (was a student at Tecnologico de Monterrey).	Yes, No, No information
20	mother.exatec	String	Indicator that denotes if the student’s mother is an exatec (was a student at Tecnologico de Monterrey).	Yes, No, No information
21	first.generation	String	It indicates if the student is the first person in the family to study for a professional career.	Yes, No, No information, Does not apply

,

Table 2. Description of the attributes of the student dataset (Part II).

No.	Attribute	Data Type	Description	Values
22	school	String	Acronyms of the school to which the student’s academic program belongs.	High school, EN = Business School, EMCS = School of Medicine and Health Sciences, EIC = School of Engineering and Sciences, ECSG = School of Social Sciences and Government, EHE = School of Humanities and Education, EAAD = School of Architecture, Art and Design
23	program	String	Acronyms of the academic program to which the student belongs.	The meaning of the acronyms is found in Appendix A
24	region	String	Code of the region to which the campus where the student is enrolled belongs.	RM = Monterrey Region, RO = West Region, RCM = Mexico City Region, RCS = South/Central Region, DR = Regional Development Region
25	foreign	String	Indicator to identify if the student is a foreigner (Yes: Foreigner), if the Mexican student’s birthplace is different from the location of the school campus (Yes: National), or if the student belongs to the same location (Local).	Local, Yes: National, Yes: Foreigner
26	PNA	Float	Previous level score (average)	Range from 0 to 100
27	english.evaluation	Integer	Level of English obtained from a standardized test of English language proficiency.	Level 0: No information, Level 1: Beginner, Level 2: Basic, Level 3: Basic, Level 4: Intermediate, Level 5: Intermediate, Level 6: Upper Intermediate, Level 7: Advanced
28	admission.test	Integer and String	Admission test score. There are two scoring scales depending on how the test is applied: (1) Academic Aptitude Test ( Prueba de Aptitud Académica-PAA): admission test applied face-to-face for all generations of students before the closure due to the COVID-19 pandemic. The range of scores is from 400 to 1600. (2) Online Aptitude Test ( Prueba de Aptitud en Línea-PAL): admission test that, as a consequence of the closure due to COVID-19, is applied online. The range of scores is from 0 to 100.	Ranges from 1 to 100 and from 400 to 1600, Does not apply
29	online.test	Binary	It indicates if the student took the online admission test.	1: Yes, 0: No
30	general.math.eval	Float and String	Mathematics score from the admission test or from the school of origin.	Range from 0 to 100, Does not apply, No information
31	admission.rubric	Integer	Score generated from the student’s profile where 50 is outstanding and 0 is average.	Range from 0 to 50
32	scholarship.type	String	Type of scholarship.	Academic talent, Army/Navy scholarship, Child of Professor/Employee/Director, Contingency scholarship, Cultural talent, Entrepreneurial talent, Leaders of Tomorrow Scholarship, Leadership talent, No scholarship, Sports Talent, Traditional
33	scholarship.perc	Integer	Scholarship percentage.	Range from 0 to 100
34	loan.perc	Integer	Percentage of the educational loan.	Range from 0 to 50
35	total.scholarship.loan	Integer	Total percentage of financial support provided to the student for education (scholarship + educational loan).	Range from 0 to 100
36	FTE	Float	It indicates if the student is a full-time student at Tecnologico de Monterrey according to the number of subjects enrolled.	Range from 0.04 to 1.44
37	average.first.period	Float	Average obtained in the first term (five weeks–Undergraduate) or the first midterm (six weeks–High School) of the student’s first semester. This data corresponds only to the AD19 and AD20 generations (TEC21 Model).	Range from 0 to 100

and

Table 3. Description of the attributes of the student dataset (Part III).

No.	Attribute	Data Type	Description	Values
38	failed.subject.first.period	Integer	Number of subjects failed in the first term (five weeks–Undergraduate) or the first midterm (six weeks–High School) of the student’s first semester. This data corresponds only to the AD19 and AD20 generations (TEC21 Model).	Range from 0 to 8
39	dropped.subject.first.period	Integer	Number of subjects dropped out in the first term (five weeks–Undergraduate) or the first midterm (six weeks–High School) of the student’s first semester. This data corresponds only to the AD19 and AD20 generations (TEC21 Model).	Range from 0 to 9
40	retention	Binary	Value that indicates if the student continues studying at Tecnologico de Monterrey.	1: Retention, 0: Dropout
41	dropout.semester	Integer	Value indicating the semester when the student dropped out. Where 0 = the student continues studying, 1 = the student dropped out during the first semester, 2 = the student did not enroll in the second semester, 3 = the student dropped out during the second semester, and 4 = the student did not enroll in the third semester.	0, 1, 2, 3, 4
42	physical.education	Binary and String	Value that indicates if the student was enrolled in any physical education activities during the first semester. This data corresponds only to the AD14, AD15, AD16, and AD17 generations.	0: No, 1: Yes, Does not apply, No information
43	cultural.diffusion	Binary and String	Value that indicates if the student was enrolled in any cultural diffusion activities during the first semester. This data corresponds only to the AD14, AD15, AD16, and AD17 generations.	0: No, 1: Yes, Does not apply, No information
44	student.society	Binary and String	Value that indicates if the student was enrolled in any student society activities during the first semester. This data corresponds only to the AD14, AD15, AD16, and AD17 generations.	0: No, 1: Yes, Does not apply, No information
45	total.life.activities	Integer and String	Number of LiFE (Leadership and Student Education) activities in which the student was enrolled during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0, 1, 2, 3, 4, 5, Does not apply, No information
46	athletic.sports	Binary and String	Value that indicates if the student was enrolled in any athletic or sports activities during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0: No, 1: Yes, Does not apply, No information
47	art.culture	Binary and String	Value that indicates if the student was enrolled in any artistic or cultural activities during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0: No, 1: Yes, Does not apply, No information
48	student.society.leadership	Binary and String	Value that indicates if the student was enrolled in any student society activities and a leadership program during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0: No, 1: Yes, Does not apply, No information
49	life.work.mentoring	Binary and String	Value that indicates if the student received advice on life and work plans during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0: No, 1: Yes, Does not apply, No information
50	wellness.activities	Binary and String	Value that indicates if the student was enrolled in any integral wellness activities during the first semester. This data corresponds only to the AD18, AD19, and AD20 generations.	0: No, 1: Yes, Does not apply, No information

provide a detailed description of the variables constituting the student dataset.

It is relevant to mention that this student dataset provides information on two educational models implemented at Tecnologico de Monterrey. The previous model, corresponding to the AD14–AD18 generations, is based on the teaching-learning process while the current model called “TEC21 Model”, corresponding to the AD19–AD20 generations, is based on challenges and competencies [29]. In this dataset, information on the average obtained in the first term or midterm, the number of subjects failed, and the number of subjects dropped out by the student is only provided for the AD19–AD20 generations. Hence, this data is interesting to analyze from this perspective as well.

In the same way, co-curricular activities related to the integrated learning of students have also evolved in accordance with the new educational model (“TEC21 Model”). The AD14–AD17 generations of students contemplated enrolling in one type of activity or the three categories of activities offered: (1) physical education, (2) cultural diffusion, and (3) student society. For the AD18–AD20 generations, the offer of activities increased since they are now part of the well-rounded education of the student to contribute to the development of transversal skills for all students [30,31]. This evolution is called the LiFE (Leadership and Student Education) program, which goes hand in hand with the TEC21 educational model [31] and is made up of the following categories: athletic or sports activities, art or culture activities, student society activities, life or work mentoring, and wellness activities.

3. Materials and Methods

The methodology used in this research is based on the Data Life Cycle used in the field of Research Data Management shown in Figure 1. The Data Life Cycle illustrates the research process and its different phases, as well as the stages associated with the data generation, use, and dissemination [32].

3.1. Data Planning

The first 40 variables shown in Table 1, Table 2 and Table 3 were defined according to the related work cited in this descriptor, as well as the Analytics and Business Intelligence Department of Tecnologico de Monterrey due to its experience in the early alerts program (student retention). The following nine variables (listed from 41 to 50 in Table 3) related to the student’s dropout semester and the student’s co-curricular activities were gathered after receiving the proposals of the researchers participating in the call for proposals. The dataset along with its data dictionary were built in Excel files to allow downloading them through the Tecnologico de Monterrey’s Data Hub (https://datahub.tec.mx/dataverse/tec (accessed on 24 August 2022)). Taking into account the sensitivity of the data, the dataset will be made available to researchers who request it through the Data Hub.

3.2. Data Collection

The data was extracted in two phases. Firstly, data was collected from the Tecnologico de Monterrey’s Data Warehouse by the Analytics and Business Intelligence Department through the SAP BusinessObjects Web Intelligence (WebI) tool. This first dataset includes personal and academic information on Undergraduate and High School students, such as gender, age, tests, schooling background of parents, among others. The variables related to retention and the socioeconomic level of the students were calculated by the same department with the purpose of designing a model to identify students at risk, used in the early alerts program. Secondly, the co-curricular activities of the students from 2014 to 2020 were obtained from the Tecnologico de Monterrey’s LiFE Department.

3.3. Data Assurance

For the dataset that was extracted from the WebI tool, the following preprocessing steps were performed:

1.

Considering the privacy of students and faculty, it is important to emphasize that the data must be de-identified before it is made available for institutional use and research purposes [22]. Therefore, the student’s enrollment identifier ( student.id) and the name of the previous level school ( id.school.origin) became non-identifiable values as they represent sensitive information.

2.

All records were translated into the English language.

3.

An exhaustive exploration was carried out to find inconsistencies in the values of variables 1 to 40 (described in Table 1, Table 2 and Table 3) and in the relationships among them.

4.

Spelling and typographical errors were checked for the categorical values of each variable.

5.

Missing values for the variables socioeconomic.level and social.lag were filled in with “No information”.

6.

The empty values corresponding to admission.test for the Undergraduate level were replaced by “Does not apply” when the variable tec.no.tec has the value “TEC”. That is, the student is a graduate of the Tecnologico de Monterrey’s High School.

7.

The variable dropout.semester was categorized according to the period in which the student dropped out: before or during the semester.

8.

The values of the variables scholarship.perc, loan.perc, and total.scholarship.loan were multiplied by 100 to represent a percentage.

3.4. Data Description

The dataset was described in detail in Section 2.

3.5. Data Preservation

This dataset will be available upon request through the Tecnologico de Monterrey’s Data Hub repository for its long-term preservation. The metadata was properly described and a specific Digital Object Identifier (DOI) was assigned in order that the data can be easily traceable and correctly cited. This dataset is protected by the Creative Commons Zero (CC0) waiver and is governed by Tecnologico de Monterrey’s Terms of Use and a Data Policy.

3.6. Data Discovery

Based on the proposals received by the researchers, information on co-curricular activities and dropout semester were identified as potential data that could be valuable for the student dropout prediction model and were added to the original dataset.

3.7. Data Integration

The first dataset consisting of 40 variables was merged with the co-curricular activities database and semester dropout information based on the variables student.id and generation to create a single data file. As a result, the final dataset is made up of 50 attributes to test and predict student dropout at the High School and Undergraduate levels.

3.8. Data Analysis

Firstly, a descriptive analysis of dataset variables was performed using the Pandas library version 1.4.3 and the Scikit-learn library version 1.1.2 in Python 3 shown in

Table 4. Description of the numerical attributes of the student dataset.

No.	Attribute	Unique	Mean	Min	Max	Information Gain
6	age	32	17	13	55	0.0086
26	PNA	2881	88.15	0	100	0.0068
28	admission.test	907	1259	1	1600	0.0026
30	general.math.eval	423	68.50	0	100	0.0062
31	admission.rubric	51	33	0	50	0.0025
33	scholarship.perc	26	17	0	100	0.0066
34	loan.perc	14	4	0	50	0.0010
35	total.scholarship.loan	3066	21	0	100	0.0064
36	FTE	64	1.02	0.04	1.44	0.0154
37	average.first.period	545	87.26	0	100	0.0321
38	failed.subject.first.period	9	0	0	8	0.0039
39	dropped.subject.first.period	10	0	0	9	0.0006
45	total.life.activities	8	1.74	0	8	0.0061

and

Table 5. Description of the categorical attributes of the student dataset.

No.	Attribute	Unique	Mode	Frequency	Information Gain
2	generation	7	AD20	21,962	0.0047
3	educational model	2	0	99,534	0.0029
4	level	2	Undergraduate	77,517	0.0089
5	gender	2	Male	75,285	0.0081
7	zone.type	4	No information	101,920	0.0058
8	socioeconomic.level	8	No information	124,041	0.0174
9	social.lag	4	No information	119,327	0.0208
10	id.school.origin	10,243	School 5,328	3106	0.0080
11	school.cost	6	High cost	67,135	0.0057
12	tec.no.tec	2	NO TEC	102,481	0.0026
13	max.degree.parents	5	Undergraduate degree	52,494	0.0128
14	father.education.complete	9	Received undergraduate degree	49,888	0.0110
15	father.education.summary	5	Undergraduate degree	49,888	0.0124
16	mother.education.complete	9	Received undergraduate degree	53,453	0.0119
17	mother.education.summary	5	Undergraduate degree	53,453	0.0130
18	parents.exatec	3	No	94,020	0.0056
19	father.exatec	3	No	97,845	0.0047
20	mother.exatec	3	No	104,787	0.0039
21	first.generation	4	Does not apply	65,809	0.0064
22	school	7	High School	65,809	0.0100
23	program	76	PBB	38,506	0.0074
24	region	5	RCM	36,678	0.0078
25	foreign	3	Local	116,933	0.0020
27	english.evaluation	8	6	49,296	0.0070
29	online.test	2	0	142,204	0.0004
32	scholarship.type	11	No scholarship	71,866	0.0165
40	retention	2	1	131,687	Target
41	dropout.semester	5	0	131,687	0.2819
42	physical.education	4	1	58,701	0.0243
43	cultural.diffusion	4	1	40,768	0.0233
44	student.society	4	0	52,710	0.0235
46	athletic.sports	4	1	36,908	0.0176
47	art.culture	4	0	43,566	0.0174
48	student.society.leadership	4	0	42,987	0.0175
49	life.work.mentoring	4	0	51,553	0.0176
50	wellness.activities	4	0	44,364	0.0175

. Secondly, a data visualization was carried out using Tableau Desktop Professional Edition 2021.4.4.

On the one hand, Table 4 describes the numerical variables of the dataset through their unique, mean, minimum, and maximum values. The identifier of each variable corresponds to the identifier assigned in Table 1, Table 2 and Table 3. Similarly, the gain information is integrated to demonstrate the dependency between each feature in the dataset and the target variable: retention. The information gain was calculated using a mutual information classifier, the values “Does not apply” and “No information” were excluded from the calculation of the statistical variables admission.test, general.math.eval, and total.life.activities since they do not represent numerical values, and the records containing null values were also not considered in the information gain calculation. It is important to remember that for the variables average.first.period, failed.subject.first.period, and dropped.subject.first.period the data is only available for AD19 and AD20.

In addition, a correlation matrix is provided in Figure 2 to show the correlation coefficients between each numerical attribute in the dataset. Due to the considerations mentioned above, the dataset used for these analyzes resulted in 25,061 records. From this matrix, it can be seen that the degree of linear relationship between the variable total.scholarship.loan and the variable scholarship.perc is 0.94, which means that these variables are strongly correlated. While between the variables average.first.period and failed.subject.first.period the coefficient is −0.43, which indicates that they are associated in the opposite direction.

On the other hand, Table 5 describes the categorical variables of the dataset through their unique and mode values, and the frequency of the mode. The identifier of each variable corresponds to the identifier assigned in Table 1, Table 2 and Table 3. Regarding the co-curricular activities, the mode and frequency were calculated according to the generation to which they correspond. For example, for the variables physical.education, cultural.diffusion, and student.society, only the values corresponding to the generations AD14 to AD17 were considered. Similarly, for the LiFE activities, only the values of the generations AD18 to AD20 were contemplated. Furthermore, the "Does not apply" value was ignored for all generations. In the same way, the gain information is integrated to demonstrate the dependency between each feature in the dataset and the target variable: retention. The information gain was calculated using a mutual information classifier, it was necessary to encode the features using an OrdinalEncoder while the target variable, in this case, “retention” was encoded with a LabelEncoder. From this calculation, it can be deduced that the retention variable is more dependent on the students’ co-curricular activities, such as cultural.diffusion, student.society, and physical.education, while the variables online.test and dropped.subject.first.period have less dependency on retention.

It is worth mentioning that it is recommended to carry out a greater analysis of the factors since the gain values may vary depending on the data preprocessing and the approach that each researcher considers in their experiments.

Subsequently, graphical representations were performed with the variables related to the dropout rates and the specific variables of the institution (student life). Figure 3 illustrates the number of High School and Undergraduate students who dropped out during their first year of study from AD14 to AD20. In general, the number of students enrolled increased over time for both levels. Figure 3 shows that in AD14 the number of High School students who dropped out is higher compared to other generations. It is also found that in AD15 there is a slight decrease in student dropout of 7.28% but during the following three generations, from AD16 to AD18, the dropout rates increased and ranged between 7.61% and 7.98%. In AD19, when the Tec21 model started, this rate started to decrease from 6.48% to 5.51% in AD20, which is the lowest dropout rate of the seven generations.

Although at the Undergraduate level the number of students enrolled seems to increase year after year, the number of dropouts does not behave the same. It is observed in the orange line of Figure 3 that the year with the highest student dropout is also found in the AD14 generation with a dropout rate of 10.09%. According to the graph, there was a downward trend starting from the AD15 generation with a dropout rate of 9.20%, then between the AD16 and AD17 generations, the dropout rates decreased and had a minimum variation with percentages of 8.82% and 8.71%, respectively. In AD18, the dropout rate continued to decrease with a percentage of 7.53%. Although there was a decreasing trend in dropout rates during the past generations, in AD19, despite the number of students enrolled increased, the dropout rate rose to 9.43% but in AD20 this rate decreased to 7.95%.

Moreover, Figure 4 presents information on the number of High School and Undergraduate students who participated in different co-curricular activities during the fall semesters between 2014 and 2017. The total number of students enrolled in those years was 78,715. The graph shows that the majority (58,701) of the students were involved in Physical Education activities with a dropout rate of 7.10%, followed by cultural diffusion with 40,768 students enrolled and a dropout rate of 7.10%; while a smaller number of students (25,115), participated in some student society activity with a dropout rate of 6.31%.

Figure 5 shows the information on the co-curricular activities that belong specifically to the Tecnologico de Monterrey’s LiFE program implemented since AD18. The number of students enrolled in these three generations was 64,611. According to the graph, more than half of the students (36,908) participated in Athletic Sports with a dropout rate of 6.09%. The Student Society Leadership was the second activity with a participation of 21,429 students and a dropout rate of 6.10%, followed by Art Culture with 20,849 students and a dropout rate of 6.02%. Compared to this last activity, slightly fewer students participated in the Wellness activities (20,052) with a dropout rate of 5.91%. Participation in activities related to Life-Work Mentoring was the least preferred by students with a participation of 12,863 but with the highest percentage of dropouts of 7.40%.

It is worth mentioning that a student could have participated in one or more activities at the same time.

4. Conclusions

Through this data descriptor, a non-identifiable dataset of 121,584 High School and Undergraduate students from Tecnologico de Monterrey was provided in order to contribute to the scientific community with data that will allow it to generate more accurate models to predict student dropout in higher education institutions. The generation of an appropriate model based on this dataset would benefit the students, by having timely and personalized strategies from their institution that support their permanence in their career, as well as the institution, by improving their statistics of student degree completion and their student investment costs.

The dataset is made up of variables reported in the literature as good predictors of school dropout as well as variables of the institution that are part of the student life. The contribution of more data related to the variables found in the literature from an institution other than their own could allow testing models already developed in their own institution to find new findings or improve those models.

On the other hand, the new variables (student life) could provide new relationships between the factors already studied that could enhance the development of new or improved models to predict student performance and identify at-risk students. Most papers use traditional Machine Learning algorithms (e.g., logistic regression, k-nearest neighbors, and decision tree-based ensemble models) [13,34]. However, only 5% of the studies have applied unsupervised learning algorithms [16]. Furthermore, the emergence of Explainable Artificial Intelligence (XAI) tools has made it possible to use advanced Machine Learning algorithms for interpretable dropout prediction [35,36,37].