Student Profiles and Technological Challenges in Virtual Learning Environments: Evidence from a Technological Institute in Southern Mexico

Abstract

This study aimed to characterize students from the Instituto Tecnológico del Valle de Etla (ITVE), located in Oaxaca, Mexico, within the virtual learning environment (VLE) and to identify the main technological challenges affecting their learning experience. The research adopted a descriptive quantitative approach, using a self-administered questionnaire applied to a sample of 71 students enrolled in distance education programs. The instrument made it possible to analyze variables such as online instructional design, teaching experience, and information technologies. The results evidenced four distinct student profiles identified as follows: demanding, digitally competent, dependent on didactic material, and with technological barriers. These profiles reflect disparities in connectivity conditions, digital competencies, and expectations toward instructional design. The evidence generated by this research contributes to the formulation of more inclusive and resilient educational policies, in line with Sustainable Development Goal 4 (SDG 4), which promotes inclusive, equitable, and quality education for all.

1. Introduction

Since the beginning of the 21st century, and especially since the COVID-19 pandemic in 2020, virtual learning environments (VLEs) have profoundly transformed higher education globally. This transformation has been driven by the urgent need to ensure educational continuity through digital technologies, accelerating the adoption of online platforms, multimedia resources, and hybrid methodologies, among others. In this context, the VLE has gone from being a complementary resource to becoming a central component of contemporary education systems, which requires analyzing its evolution, applications, and challenges in different environments.

1.1. Evolution of the Virtual Learning Environment (VLE) at the Global Level and Its Application in the Mexican Context

The global landscape of higher education has experienced a profound transformation as virtual learning environments (VLEs) have increasingly expanded and reshaped teaching and learning practices. The educational trends of the 21st century anticipated that this evolution would accelerate considerably after the COVID-19 pandemic, forcing institutions at all levels to adopt digital teaching models (UNESCO, 2021). This major transition highlighted important structural inequalities, mainly in access to digital infrastructure, connectivity, and technological skills, among others, both in developed countries and in more disadvantaged regions.

However, the global health crisis also reflected a turning point. Accelerated digitalization allowed relevant advances in the design of more flexible, personalized, and student-oriented learning environments (UNESCO, 2021). The use of emerging technologies, hybrid methodologies, and digital resources generated opportunities to rethink traditional ways of teaching and learning, integrating practices such as alternative assessment, emotional support, and autonomous learning, as evidenced in cases such as the International Baccalaureate (International Baccalaureate Organization, 2024b).

In Mexico, the effects of this transformation have also been noticeable. Internet access coverage has increased, reaching 68.5% of households by 2022; however, marked inequalities persist between urban (84%) and rural (62%) areas (INEGI, 2022). Consequently, higher education institutions have implemented and adopted platforms such as Moodle and hybrid teaching strategies, but many face challenges related to teacher training, equitable access to technological devices, and reliable connectivity (UNAM, 2023). Universities such as Tecnológico de Monterrey have advanced in the consolidation of comprehensive digital teaching models, while other institutions lack resources or training that affect educational quality and equity (Conecta Tec de Monterrey, 2023; Guerrero Rodríguez, 2022).

1.2. Case Study: ITVE in Oaxaca

In regions such as Oaxaca, socioeconomic, geographic, and infrastructural conditions aggravate access gaps and reduce the permanence of students in virtual education. In this context, the Instituto Tecnológico del Valle de Etla (ITVE) is a paradigmatic case of a public institution that, after the pandemic, has strengthened its conditions to offer distance education. Through a distance learning model with regional centers, ITVE offers three online engineering programs: Business Management, Industrial, and Community Development. The purpose of this model is to expand educational coverage in rural and indigenous communities, promoting a more equitable and inclusive education, in line with Sustainable Development Goal 4 (SDG 4).

Figure 1 shows the geographical distribution of the ITVE’s distance education nodes in Oaxaca: Santa María Tlahuitoltepec (Sierra Norte), El Espinal and Unión Hidalgo (Isthmus), and Santiago Suchilquitongo (Valles Centrales). This last node houses the main headquarters, from where it coordinates the academic, technological, and administrative operations of the academic programs. This node serves as a nucleus of educational innovation that guarantees the quality and relevance of the offer in the virtual modality.

1.3. Conceptual Framework of Virtual Learning Environments

Virtual learning environments (VLEs) have evolved from being simple, complementary digital resources to complex, technology-mediated teaching-learning systems. These systems allow asynchronous and networked interaction between students and teachers, with access to content, activities, and collaborative communication, among others (Piccoli et al., 2001; Wilson, 1996). From the perspective of technology-mediated learning, VLEs offer opportunities to construct knowledge autonomously and collaboratively, favoring flexibility in time, space, and pace of study (Piccoli et al., 2001; Tsapali, 2019).

Constructivist approaches that focus on the student form the foundation of online learning, as they encourage active participation and personalization of the educational process. From this perspective, learning is not a simple reception of information, but a process of meaning construction through experience and interaction (García, 2019; García-Peñalvo et al., 2020). In this framework, the specialized literature identifies several key dimensions that configure VLEs: technology, i.e., tools used to deliver content and facilitate communication (email, forums, videoconferences, etc.); interaction defined as the degree of participation between students and teachers, crucial to foster engagement and avoid dropout (Hussain et al., 2018); student control which is the autonomy to decide the pace, order, and modality of learning; accessibility which is the possibility of overcoming geographical and economic barriers (Hackbarth, 1996; Massy & Zemsky, 1995); and space and resources defined as the variety of didactic materials that enrich the formative process (Alavi, 1994; Cradler, 1997), among others.

Among the widely recognized benefits of online learning are flexibility, personalization, the possibility of forming distributed learning communities, and access to updated and multi-format content, among others (UNESCO, 2021, 2023). These characteristics contribute to democratizing educational access more equitably, especially in rural regions or regions with geographic barriers that hinder face-to-face access. However, there are also important challenges, such as digital gaps resulting from unequal access to devices, connectivity and digital literacy (INEGI, 2022; UNAM, 2023), insufficient teacher training for the use of educational technologies (Guerrero Rodríguez, 2022), poor or deficient instructional design that generates demotivation or dropout (Hughes, 2007), and structural inequalities that limit the effective participation of certain social groups (International Baccalaureate Organization, 2024a). These barriers directly affect student retention, the quality of the educational experience, and equity in learning outcomes (Mollo-Flores & Medina-Zuta, 2020; Simpson, 2003).

In this scenario, measuring student engagement is complex, especially in the absence of face-to-face interactions. Platforms must provide data on student behavior, e.g., content visits, forum participation, assignment completion, level of progress, among others, as a basis for identifying engagement levels and dropout risk (Chui et al., 2020; Gašević et al., 2020). Some studies even apply learning analytics techniques and predictive algorithms to detect at-risk students early and provide personalized support (Chui et al., 2020).

According to SDG 4, ensuring quality, inclusive, and equitable education requires not only expanding access, but also generating pedagogical, technological, and institutional conditions that favor permanence and educational success. When virtual online learning environments are adequately structured and supported by inclusion policies, they can contribute to reducing historical gaps in access to education, especially in marginalized regions (UNESCO, 2020). Conversely, if institutions do not address structural conditions of inequality and pedagogical conditions, digital education risks reproducing or even widening existing inequalities.

This tension between the democratizing potential of virtual education and its risk of exclusion in contexts without minimum conditions has been widely discussed in the literature (Simpson, 2003; UNESCO, 2020); however, most existing studies on virtual learning environments tend to focus on urban or institutionally consolidated contexts. Despite the increasing body of research on VLEs in diverse contexts, there is still limited empirical evidence on how students from rural and socioeconomically marginalized areas, particularly those enrolled in regional public institutions, experience, access, and engage with VLEs. This gap in the literature prevents a comprehensive understanding of the differentiated needs and challenges faced by students in vulnerable territories. It hinders the design of targeted, equity-oriented educational policies.

Addressing this research gap is essential for informing context-sensitive strategies that can effectively strengthen student retention and inclusion. In this regard, the Instituto Tecnológico del Valle de Etla (ITVE), located in the state of Oaxaca, Mexico, provides a pertinent case to explore how regional disparities in infrastructure, digital competencies, and access conditions shape virtual learning.

From this perspective, the objective of the present study is to characterize ITVE students in the virtual learning environment and identify the main technological challenges that affect their academic experience. The analysis focuses on differentiating student profiles based on their access to infrastructure, use of digital platforms, and degree of autonomy, to generate evidence to inform more equitable, resilient, and inclusive institutional policies in alignment with SDG 4, which seeks to ensure inclusive, equitable, and quality education for all.

This gap directly informs the central research question of this study, ensuring that the inquiry is firmly grounded in both the theoretical framework and the practical challenges faced by the target population. Accordingly, the study poses the following research question:

Q1—What student profiles emerge in the ITVE virtual learning environment, and what are the main technological challenges that affect their learning experience?

The answer to this question is expected to guide future educational interventions that foster greater equity and sustainability in virtual learning environments within technological higher education institutions.

2. Materials and Methods

This study presents a quantitative approach, with a non-experimental, cross-sectional, and exploratory design, aimed at identifying and analyzing the profiles of students with their experience in virtual learning environments, considering the pedagogical, technological, and teacher interaction dimensions in distance mode.

The data collection technique used was the survey, applied employing a questionnaire with semi-structured questions, designed in Google Forms (January 2024 version), which was provided virtually and answered online by the participants. The questionnaire made it possible to collect information on variables related to the design of online learning, teaching experience, and access to information technologies. This study draws upon the results of an earlier exploratory phase and a targeted review of the literature on virtual learning environments.

This study designed an instrument to assess one primary variable: the Virtual Learning Environment, which disaggregates the data into three key dimensions, such as online learning design, teacher experience, and information technologies, each composed of several indicators, as shown in

Table 1. Operationalization of the Virtual Learning Environment variable.

Dimension (α)	Indicator	Concept	Related Items	Theoretical Support
Online Learning Design (α = 0.907). Evaluates the organization, clarity, and variety of resources in virtual courses, as well as pedagogical design and feedback provided.	Structure of Didactic Material	Pedagogical and sequential organization of the online course, as well as the availability of resources and activities that promote structured learning.	EEmd4, EEmd3, EEmd2, EEmd1, EEmd5	Baragash and Al-Samarraie (2018); Conole (2012); Piccoli et al. (2001)
	Design and Feedback	Clarity of instructional design and relevance of the feedback provided by the instructor.	EEde4, EEde2, EEde3, EEmr4, EEmr2	Conole (2012); Fredricks (2011); Zhang et al. (2006)
Teacher Experience (α = 0.962). Analyzes the teacher’s technological competence and their role as a guide and facilitator in virtual environments.	Teaching Leadership/Teacher Support	The teacher’s ability to guide, motivate, and maintain effective communication in virtual environments.	EDm3, EDf2, EDf5, EDf3, EDm4, EDf4, EDf1, EDm1, EDo2, EDc2	Agudo-Peregrina et al. (2014); Hughes (2007); Hussain et al. (2018)
	Technological Proficiency of the Teacher	Teacher’s competence in the use of educational technological tools.	EDdt4, EDdt1, EDdt2, EDdt3	Blin and Munro (2008); Rienties et al. (2016)
Information Technologies (α = 0.919). Measures both the student’s technological skills and their access conditions, including equipment quality and connectivity.	Software Proficiency	Students’ technological skills to operate virtual learning tools.	ETds7, ETds6, ETds9, ETds4, ETds3, ETds1	Avci and Ergün (2022); López-Fernández and Rodríguez-Illera (2009)
	Access	Availability of connectivity, equipment, and technological means for online participation.	ETc1, ETc2, ETa3, ETct4, ETc3, ETa1	Hannum et al. (2009); INEGI (2022); Martín-Gutiérrez et al. (2017)
	Technological Quality	Stability and functionality of the technological infrastructure used by the student.	ETct2, ETct1, ETa4, ETa2	López-Fernández and Rodríguez-Illera (2009); Stichter et al. (2014)

.

The authors developed the questionnaire items based on the dimensions and indicators identified in the specialized literature, as shown in the operationalization table. Although they preserved the theoretical structure, they carefully adapted the wording of the questions to the student context of the ITVE in Oaxaca to ensure relevance and clarity for the target population. To strengthen content validity, a pilot test with 30 students was conducted prior to the main application. Based on their feedback, several items were restructured or adjusted.

This study evaluated each of the indicators using Likert-type items with a scale of 1 to 5 points, which probed the students’ perception of their virtual learning environment experience. This study confirmed the empirical validity of the instrument through an exploratory factor analysis (EFA), which yielded a KMO (Kaiser–Meyer–Olkin) index of 0.839 and a statistically significant Bartlett’s test of sphericity (p < 0.05), a key indicator of the instrument’s validity. The analysis also revealed a total explained variance of 77.064%. Items with factor loadings below 0.50 were eliminated. This study constructed the final dimensions of the instrument by integrating the categories from the empirical analysis with the conceptual references found in the specialized literature.

Cronbach’s alpha coefficient (α) was calculated for two different purposes: (1) in the factor analysis, it is used to verify the internal consistency of each dimension of the instrument and (2) subsequently, when identifying profiles through cluster analysis, Cronbach’s alpha was calculated again to corroborate the internal consistency of the variables within each cluster, that is, to verify that the selected dimensions homogeneously characterize the students grouped in each profile.

Analysis Unit and Sample

The unit of analysis consisted of students enrolled in the distance learning engineering programs.

Table 2. Degree programs in non-school distance learning modality.

No	Degree Program	Men	Women	Total
1	Business Management Engineering (BME)	71	177	248
2	Industrial Engineering (INE)	67	45	112
3	Community Development Engineering (COD)	28	42	70
	Total non-school distance			430

shows that based on the ITVE’s official records, a total population of 430 students was identified, distributed among the Business Management Engineering, Industrial Engineering, and Community Development Engineering programs.

The sample was determined by stratified random sampling, considering the different semesters and programs. The formula for finite populations was applied, with a confidence level of 90% and a margin of error of 10%, resulting in a minimum sample of 59 students. However, the study obtained 71 completed questionnaires, which were considered valid and used for the analysis.

The researchers chose the sample size considering the exploratory and descriptive nature of the study, as well as the technological and geographical limitations of the rural context. They recognized that confirmatory research typically requires larger samples. However, according to Conroy (2007, 2021), an appropriate sample size in social research should prioritize feasibility, contextual realities, and the objectives of the study. Similarly, Sathyanarayana et al. (2024) argue that in exploratory research, especially in settings with limited resources or social inequalities and constraints, representativeness, internal diversity, and the ability to detect emerging patterns outweigh the need for statistical generalization. Using a 90% confidence level with a 10% margin of error provides a representative sample to identify preliminary trends and relationships, while avoiding unnecessary oversizing and optimizing resources. These perspectives justify the selected sample as methodologically sound and contextually appropriate for generating insights and informing targeted institutional interventions.

3. Results

This section presents the results derived from the hierarchical cluster analysis conducted on the student profiles in the virtual learning environment. Using a quantitative-descriptive approach, the analysis identified four distinct student groups based on their experiences with online learning. The findings reveal the technological, pedagogical, and contextual factors that influence students’ engagement and performance in distance education, categorizing them into the following profiles: demanding, digitally competent, dependent on didactic material, and with technological barriers. These results provide insight into the varying conditions that shape students’ educational experiences in virtual learning environments.

3.1. Hierarchical Cluster Analysis for the Virtual Learning Environment Variable

Figure 2 presents the dendrogram obtained through hierarchical cluster analysis, applied to group students according to their experiences within the virtual learning environment. The researchers performed the analysis using Ward’s method and standardized Z-scores to normalize the scale of all variables, ensuring that no single indicator dominated the clustering process. The researchers determined the optimal number of clusters by identifying the most pronounced discontinuity in the dendrogram, which corresponded to an 8-point increase in Euclidean distance highlighted by the red line. This jump indicated a clear separation in the data structure, supporting the decision to extract four distinct clusters.

These four clusters represent differentiated student profiles, each characterized by unique patterns in their interaction with virtual courses, access to technological resources, and engagement with online didactic materials. For example, some clusters grouped students with high levels of digital competence and autonomy. In contrast, others concentrated cases with recurrent technological limitations or a strong dependency on instructor guidance and structured materials.

This segmentation is relevant because it highlights heterogeneity in student experiences, even within the same academic programs, pointing to the need for diversified pedagogical and technological support strategies.

Table 3. Cluster of student membership in the virtual learning environment.

No	Cluster 1: Demanding	Cluster 2: Digitally Competent	Cluster 3: Dependent on Didactic Material	Cluster 4: With Technological Barriers
1	EDo3—In general, to what extent does the instructor comply with the resources, assignments, and activities established in the academic program?	ETds8—Of the virtual tools available in the online environment, how well are audio editors for text-to-speech conversion known, used, and operated by the student?	EEmd2—To what extent are the didactic materials of the online course perceived to be organized in a sequential and logical way within the modules?	ETa4—How often does the student have internet connection from a place other than their home (e.g., work, school, park, municipal site)?
2	EDo4 –In general, how often is the instructor perceived to comply with the evaluations established in the academic program?	ETds9—Among the virtual tools available in the online environment, how well are video editors known, managed, and operated by the student to create video clips, photos, graphics, audio, digital effects, and more?	EEmd4—To what extent is the didactic material in the online course perceived as relevant according to the syllabus?	ETct2—How often does the student have solutions to technical connection issues during online course sessions?
3	EDo1—In general, how often is the instructor perceived to comply with the academic program?	ETds1—Among the virtual tools available in the online environment, how well are different web browsers known, managed, and operated by the student to access the internet?	EEmd1—To what extent is the didactic material in the online course perceived to include books, scientific articles, websites, images, videos, and podcasts that support course development?	ETct1—How often does the student have connectivity through broadband, portable modems, or other technologies different from home or work internet?
4	EDo2—In general, how often is the instructor perceived to comply with the resources, assignments, and activities established in the academic program?	ETds2—Among the virtual tools available in the online environment, how well are language translators known, managed, and operated by the student to communicate information from one language to another?	EEmd3—To what extent is the didactic material in the online course perceived to cover all the content needed to learn a topic?	EEmr1—How often does the online course include educational digital games that reinforce previously covered topics?
5	EDdt3—In general, how often is the instructor perceived to master various teaching technologies, such as feedback games, virtual whiteboards, or other digital tools that enhance student learning?	ETds4—Among the virtual tools available in the online environment, how well is referencing software known, managed, and operated by the student to correctly link and cite the works referenced in their assignments?	EEmd5—To what extent is the didactic material in the online course perceived to include forums for interaction and discussion that support course development?
6	EDdt4—In general, how often is the instructor perceived to master the course syllabus as demonstrated through various virtual tools?	ETds5—Among the virtual tools available in the online environment, how well is software for creating and editing digital text files known, managed, and operated by the student?
7	EDdt4—In general, how often is the instructor perceived to demonstrate mastery of the course syllabus through the use of various virtual tools?	ETds5—Among the virtual tools available in the online environment, how well is software for creating and editing digital text files known, managed, and operated by the student?
8	EDc3—In general, how often is the instructor perceived to provide feedback on students’ work (through any medium, written or oral) during the online course?	ETds7—Among the virtual tools available in the online environment, how well are spreadsheets known, managed, and operated by the student to handle numerical and alphanumerical data?
9	EDc4—In general, how often is the instructor perceived to offer online sessions as part of effective communication within online courses?	ETds3—Among the virtual tools available in the online environment, how well are network-based communication tools (videoconferencing, calls, messages) known, managed, and operated by the student?
10	EDc1—In general, how often is the instructor perceived to provide clear and concise instructions through the Moodle platform?	ETc1—How often does the student have connectivity through broadband, portable modems, or other technologies different from home internet?
11	EDc2—In general, how often is the instructor perceived to provide feedback on students’ work (through any medium, written or oral) during the online course?	ETc2—How often does the student have a stable internet connection in their community when performing online activities?
12	EDm2—In general, how often is the instructor perceived to create an environment that fosters students’ interest in completing the online course?	ETc3—How often does the student have a stable cell phone signal (voice and data) in their community when performing online activities?
13	EDm4—In general, how often is the instructor perceived to promote values such as inclusion and equity to encourage the equal participation of men and women?	ETct3—How often does the student have a stable internet connection during synchronous online course sessions?
14	EDm3—In general, how often is the instructor perceived to actively collaborate with students to achieve the objectives of the online course (e.g., specific practices, individual questions, or personalized support)?	ETct4—How often does the student have the adequate equipment to carry out the activities of their online courses?
15	EDm1—In general, how often is the instructor perceived to encourage students to participate actively in the online course?	ETa1—How often does the student have a computer to take their online course?
16	EDf4—In general, how often is the instructor perceived to alternate methods to evaluate and grade the competencies acquired?	ETa3—How often does the student have an internet connection at home?
17	EDf5—In general, how often is the instructor perceived to be empathetic toward the situations shared by students?	ETa2—How often does the student have a backup device to complete their online course (another PC, laptop, tablet, or smartphone)?
18	EDf2—In general, how often is the instructor perceived to apply teaching strategies when a student does not respond or is absent in the online course?
19	EDf3—In general, how often is the instructor perceived to adjust their teaching methodologies according to the students’ context?
20	EDf1—In general, how often is the instructor perceived to adapt to problems that arise within the online learning group?
21	EEmr3—How often are assignments included in the online course that lead to the creation of relevant products for topic development?
22	EEmr4—How often are activities included in the online course that help strengthen students’ skills and abilities?
23	EEmr2—How often are resources included in the online course that help reinforce the knowledge acquired by students?
24	EEde2—How often does the structure of the online course provide easy access to resources, assignments, and activities?
25	EEde3—How often does the structure of the online course include resources, assignments, and activities that facilitate teaching and learning?
26	EEde4—How often does the structure of the online course include dates, times, and activity descriptions to support task submission management?
27	EEde1—How often does the structure of the online course include an introduction, syllabus, topic development, feedback, assessment, and conclusions?
28	α = 0.976	α = 0.946	α = 0.948	α = 0.747

Source: Prepared by the authors based on the descriptive results obtained in IBM SPSS Statistics (version 26).

presents the variables and items most associated with each of the four clusters, which guided their naming and characterization: Cluster 1: Demanding—Students with high expectations for the instructor’s organization, compliance, and punctuality; Cluster 2: Digitally Competent—Students proficient in virtual tools such as editors, translators, and referencing software, showing autonomy and digital literacy; Cluster 3: Dependent on Didactic Material—Students whose learning relies on the quality and organization of books, articles, videos, and podcasts provided in the course and Cluster 4: With Technological Barriers—Students facing connectivity, equipment, and technical problem-solving limitations, reducing participation.

Naming the clusters based on these traits highlights the diversity of experiences in virtual learning and supports the design of targeted pedagogical and technological strategies.

Subsequently, a K-means cluster analysis was applied, which allowed us to confirm the participants’ belonging to each group and to refine the classification (see

Table 4. Virtual Learning Environment membership clusters.

Segment 1: Demanding—24%			Segment 2: Digitally Competent—3%			Segment 3: Dependent on Didactic Material—45%			Segment 4: With Technological Barriers—28%
Case	Distance	Degree Program	Case	Distance	Degree Program	Case	Distance	Degree Program	Case	Distance	Degree Program
2	2.326	BME	38	6.075	BME	1	6.583	BME	7	4.781	INE
4	2.498	BME	64	6.075	BME	3	5.605	INE	15	5.585	BME
6	6.112	BME				5	6.495	BME	22	5.645	BME
8	4.916	COD				9	5.805	BME	24	4.934	INE
10	7.112	COD				12	5.376	BME	25	8.148	BME
11	3.159	INE				13	5.831	BME	28	4.355	BME
14	4.258	BME				18	5.532	BME	30	2.995	COD
16	4.509	BME				19	4.944	BME	33	3.818	BME
17	8.608	BME				20	5.115	INE	37	4.996	BME
21	4.641	INE				23	3.79	BME	39	6.044	BME
29	6.575	COD				26	4.706	BME	41	5.763	BME
31	6.223	COD				27	5.421	BME	43	4.509	BME
34	4.698	BME				32	4.446	BME	44	7.382	INE
46	4.681	BME				35	4.904	BME	45	5.28	BME
59	4.933	BME				36	4.109	BME	54	5.086	BME
62	2.844	INE				40	6.452	BME	55	5.802	BME
66	4.181	BME				42	4.422	INE	57	4.881	BME
						47	4.395	BME	58	5.951	INE
						48	7.333	BME	67	5.664	BME
						49	4.698	BME	71	5.034	BME
						50	5.567	INE
						51	6.885	INE
						52	4.778	BME
						53	4.671	BME
						56	5.242	BME
						60	4.209	BME
						61	4.517	COD
						63	5.185	BME
						65	9.258	COD
						68	4.457	BME
						69	6.47	BME
						70	5.781	BME

Note. BME = Business Management Engineering; INE = Industrial Engineering; COD = Community Development Engineering. Source: Prepared by the authors based on the descriptive results obtained in IBM SPSS Statistics (version 26).

). In order to verify the statistical significance of the differences between clusters, an analysis of variance (ANOVA) was carried out on the mean scores of each dimension.

This procedure made it possible to identify and characterize four differentiated profiles of students in virtual environments: (1) demanding, (2) digitally competent, (3) dependent on didactic material, and (4) with technological barriers. The results of the ANOVA further validated the differentiation between these clusters, confirming that the groups show statistically significant differences in their engagement with the virtual learning environment. Demanding students obtained higher scores in monitoring instructor compliance; digitally competent students stood out for their advanced use of technological tools and self-directed learning; those dependent on didactic material showed the highest appreciation for structured resources; and students with technological barriers recorded the lowest averages across all dimensions, reflecting persistent difficulties in participation and academic performance.

These profiles provide a detailed overview of the diversity of distance education experiences at the ITVE and constitute a sound empirical basis for guiding pedagogical, technological, and institutional policy decisions.

Table 5. Analysis of Variance between segments of the Virtual Learning Environment.

	Cluster Error				F	Sig.	Distances Between Final Cluster Centers
Cluster	Mean Square	df	Mean Square	df			1	2	3	4
Cluster 1: Demanding	16.019	3	0.328	67	48.911	p < 0.001		7.933	10.727	5.274
Cluster 2: Digitally Competent	15.647	3	0.344	67	45.461	p < 0.001	7.933		15.423	10.266
Cluster 3: Dependent on Didactic Material	15.104	3	0.368	67	40.990	p < 0.001	10.727	15.423		6.466
Cluster 4: With Technological Barriers	16.301	3	0.315	67	51.770	p < 0.001	5.274	10.266	6.466

shows the results of the analysis of variance (ANOVA) applied to the four student segments identified in the ITVE virtual environment. The significance values (p < 0.001) and the high F values in all cases confirm the existence of statistically significant differences between the clusters. These indicators validate the segmentation performed, indicating that the student profiles present differentiated behaviors concerning their online learning experience.

The distances between the cluster centers allow us to observe the degree of differentiation between the profiles. The most significant distance is registered between digitally competent students and those dependent on didactic material (15,423), indicating marked differences in their relationship with technology and learning. In contrast, the smallest distance is between the demanding and technology-barrier segments (5.274), suggesting some similarity in their demands regarding course design, although they differ in access to infrastructure. These results provide strong evidence for customizing pedagogical design based on the specific characteristics of each group, ensuring a more tailored and practical approach to online learning.

3.2. Sociodemographic Profile of Distance Mode Students

Table 6. Sociodemographic profile of distance learning students.

Age			Marital Status			Gender			Program
	Freq.	%		Freq.	%		Freq.	%		Freq.	%
18–23	28	39.4%	Married	9	12.7%	Male	24	33.8%	BME	52	73.2%
24–27	29	40.8%	Single	51	71.8%	Female	46	64.8%	INE	12	16.9%
28–33	7	9.9%	Domestic partnership	11	15.5%	Prefer not to say	1	1.4%	COD	7	9.9%
34–37	2	2.8%
38–46	5	7.0%
Total	71	100%		71	100%		71	100%		71	100%
Mean	1.97			2.03			1.68			1.37
Std. Dev.	1.121			0.534			0.501			0.660
Variance	1.256			0.285			0.251			0.435
Semester			In addition to your studies, what other activity do you engage in?			Describe one of the reasons why you chose the distance learning modality.
	Freq.	%		Freq.	%					Freq.	%
2–3	25	35.2%	Work	61	85.9%	Work				34	47.9%
4–5	14	19.7%	Entrepreneur	5	7%	Distance (far from the institution)				7	9.9%
6–7	4	5.6%	Household duties	5	7%	Economic reasons (lack of resources)				7	9.9%
8–9	15	21.1%				Comfort/convenience				13	18.3%
10 or more	13	18.3%				Parental or caregiving responsibilities				3	4.2%
						Family problems				5	7%
						Professional development				2	2.8%
Total	71	100%		71	100%					71	100%
Mean	2.68			1.21						2.54
Std. Dev.	1.575			0.558						1.811
Variance	2.479			0.312						3.281

Based on this heterogeneous profile, the following section presents the results of the cluster analysis to identify student typologies within the virtual learning environment.

presents the sociodemographic profile of the analyzed sample, which included 71 students enrolled in online education programs at the Instituto Tecnológico del Valle de Etla (ITVE). The most represented age group was 24 to 27 years old (40.8%), followed by 18 to 23 years old (39.4%), and over 28 years old (17.6%).

Regarding marital status, 71.8% reported being single, 15.5% living in a domestic partnership, and 12.7% married. In terms of gender, 64.8% identified as female and 33.8% as male.

Students enrolled as follows: 73.2% in the Business Management Engineering program, 16.9% in Industrial Engineering, and 9.9% in Community Development Engineering.

Academically, 35.2% were in their second or third semester, 19.7% in the fourth or fifth, and 39.4% in the eighth semester or beyond. A total of 85.9% reported combining their studies with a job, 7% work from home, and another 7% are involved in entrepreneurial activities.

Regarding the reasons for choosing distance education, 47.9% cited work-related reasons, 18.3% convenience, 9.9% geographic distance, another 9.9% economic reasons, 7% family problems, and 4.2% caring responsibilities.

In summary, the profile of distance education students at the ITVE reveals a heterogeneous population in terms of age, gender, employment status, and academic progression.

3.3. Demanding Students

Of the 71 students surveyed, 17 (24%) make up the cluster of demanding students. This group has high expectations regarding compliance with the academic program, the quality of teaching, and the structure of the virtual environment. These students expect teachers to deliver course resources, assignments, and assessments on time and as described in the program. They emphasize the need for an organized course that covers everything from introduction to feedback, with defined deadlines and accessible content.

Students value teachers who actively use digital tools such as virtual whiteboards, interactive games, videoconferencing, email, and instant messaging. They believe that teachers should use these tools to improve communication, support the learning process, and clarify academic content. The demanding segment of students also highlighted the need for clear instructions and teaching materials that they can use. Students focus on the importance of synchronous sessions, quick responses from teachers, and consistency between content and assessment guidelines.

Instructors who show empathy toward students’ challenges and adapt their methods to group needs earn strong appreciation from this group. They advocate for an inclusive, equitable learning environment where all students can access the same opportunities for academic success.

In general terms, students in this cluster have a highly critical and demanding profile, seeking a structured online educational experience with a committed, communicative, and flexible teacher.

3.4. Digitally Competent Students

Among the 71 students surveyed, 2 (3%) belonged to the digitally competent cluster. These students reported strong proficiency in digital tools and consistent access to technological resources. The students reported advanced skills in using specialized software for audio and video editing, information processing, collaborative platforms, and communication tools. They regularly use text-to-speech converters, video editors with integrated graphics and effects, digital translators, efficient web browsers, and referencing software for academic writing.

This group also described advanced skills in creating and modifying digital text files and designing presentations with graphics, images, animations, and other multimedia elements. Their proficiency in manipulating spreadsheets allows them to process numerical and alphanumeric data with ease. At the same time, their knowledge of network-based communication tools, such as videoconferencing, calls, and instant messaging, gives them greater fluency in interacting with teachers and peers within the virtual environment.

In addition to their competence in the use of digital tools, these students have an adequate technological infrastructure for their online training. They have stable connectivity through broadband, portable modems, and technologies that allow them to connect beyond their home. They also have access to a stable internet connection in their community and a reliable cellular signal to carry out their academic activities.

Regarding equipment, these students have the necessary devices to carry out their studies online. They have a central computer, as well as a backup device, which can be another computer, tablet, or cell phone. Their access to the internet from home allows them to carry out their activities without depending on external spaces, which gives them greater flexibility to organize their study time.

Overall, the students in this cluster described a profile of technological autonomy, efficient management of digital resources, and fluency in virtual communication. Their responses indicate that they are well-equipped to engage with the Virtual Learning Environment.

3.5. Students Dependent on Didactic Material

Of the 71 students surveyed, 32 (45%) belong to the cluster characterized by a strong dependence on the teaching materials provided in the online course. These students structure their study habits around the digital resources available, so the quality, organization, and relevance of the content are essential to their learning process.

Students also highlight the value of sequential and logically organized materials within the online course modules, which help them understand the content and progress gradually. A coherent arrangement of resources within the virtual platform reduces confusion and promotes a smooth learning process.

They also highlighted the importance of consistency between course materials and the program. They expect educational resources to directly address course topics and provide diverse sources, such as books, scientific articles, websites, images, videos, and podcasts.

These students emphasize the need for materials that fully cover the course content, without any bias in the information that could hinder their understanding. They rely heavily on the teacher’s resources, and when they perceive the content to be incomplete or superficial, they say that their academic progress is limited. This learning pattern fits with an instructional design based on selected and comprehensive content.

In addition, they consider discussion forums to be valuable educational tools, as they allow for interaction with peers and teachers, the exchange of ideas, and collaborative problem solving. They consider these forums to be essential for clarifying concepts and consolidating knowledge through dialog and group analysis.

In summary, students in this cluster described a strong dependency on well-structured, comprehensive, and accessible didactic resources within the virtual platform. Their responses reflect the need for clear guidance and content organization to support their learning in digital environments.

3.6. Students with Technological Barriers

Of the 71 students surveyed, 20 (28%) belonged to the cluster defined by technological barriers. These students reported limitations in device availability, connectivity, and technical conditions necessary for effective online learning, which directly affected their academic performance.

These students indicated that they depend on external spaces to connect, which makes it difficult for them to participate and follow the academic content. Other challenges for these students are the lack of continuous access to an internet connection from home; they rely on networks in public spaces, such as work, school, parks, or municipal facilities, which limits their availability to access synchronous classes or perform online activities efficiently.

These students face recurring technical difficulties during online sessions. The absence of practical solutions to connection problems can result in the impossibility of keeping up with the pace of the course, generating delays in the delivery of activities, and limiting their interaction with teachers and classmates. The instability of connectivity also impacts their ability to access educational resources hosted on digital platforms, which aggravates the gaps in their training process.

In order to cope with these limitations, some students opt to use alternative technologies, such as mobile modems, cellular networks, or broadband connections outside the home. These alternatives do not always ensure a continuous and stable connection, which constitutes an additional obstacle in the learning process. The lack of adequate digital infrastructure can result in an interrupted online educational experience, with episodes of disconnection, loss of information, and difficulties in complying with academic activities within the established deadlines.

Another aspect that influences the experience of these students is the availability of interactive resources in the course, such as didactic digital games that can provide dynamic learning feedback. In contexts with limited internet access, the possibility of interacting with this type of tool can affect the understanding and development of key skills within the course.

In summary, students in this cluster described a learning experience conditioned by unstable internet access and limited digital infrastructure. Their participation in online education depends mainly on the availability of external networks and their ability to maintain connectivity throughout the course activities.

4. Discussion

The use of cluster analysis made it possible to identify latent patterns within a heterogeneous student population, revealing four profiles: demanding students, students with digital skills, students dependent on teaching materials, and those with technological barriers and specific pedagogical needs. This approach segmented ITVE students through observable variables and meaningful combinations of digital skills, connectivity conditions, and learning styles, highlighting structural inequalities and providing information for moving from standardized models to more equitable and adaptable systems that prioritize students and respond effectively to diverse educational contexts.

4.1. General Characterization of the Student Population

An analysis of the sociodemographic profile of students enrolling in distance learning at the Instituto Tecnológico del Valle de Etla provides a better understanding of the actual learning conditions in rural and semi-urban areas of Southern Mexico. The large proportion of students aged 18 to 27, combined with a high proportion of people aged 28 and over, suggests a generational shift that encompasses both those who are pursuing a continuous academic path and those who are trying to re-enter education. UNESCO (2021) and Salinas et al. (2022) would consider those differences a strength of distance education, rather than its ultimate downfall, enabling mobile and flexible access for people who might not otherwise be able to attend because of time or family commitments.

The majority presence of women in this modality (64.8%) is a particularly relevant finding, aligned with the study by Nkwanyana and Fagbadebo (2024), which explains this trend as driven by the flexibility of virtual education to accommodate caregiving and domestic responsibilities. Consequently, this underscores the importance of virtual learning, incorporating not only digital inclusion but also gender equity in instructional design, assessment, and participation. In this sense, online education becomes a practical way to expand educational opportunities for women facing structural inequalities.

From an educational perspective, there is a growing interest among students in the early stages of their education in virtual learning environments. However, institutions need to implement effective mechanisms for retaining and supporting students to encourage them to stay in education and reduce long-term dropout rates. At the same time, the high percentage of students who combine their studies with work (85.9%) confirms that online learning offers a viable solution for those who balance their academic activities with professional or business activities. As Morgan et al. (2024) point out, distance learning becomes a strategic option for students who must study and work at the same time, especially in economically vulnerable contexts.

The reasons why students choose this modality, such as the need to work, convenience, geographical distance, economic difficulties, or family responsibilities, reveal structural inequalities that limit access to face-to-face education. Taken together, the sociodemographic and academic profile of the ITVE students reveals a highly autonomous and diverse student body with multiple responsibilities.

4.2. Cluster 1: Demanding Students

Representing 24% of the participants, the demanding student cluster demonstrates high academic expectations and maintains a critical perspective on the structure, coherence, and pedagogical quality of online courses.

The findings obtained support the conclusions of Chiu (2021), who argues that effective virtual environments must explicitly and structurally articulate objectives, content, and assessment systems in order to promote autonomous learning, furthermore, Rodríguez et al. (2022) emphasize that educational institutions in virtual environments, where academic success depends mainly on self-regulation and effective time management by students, play a fundamental role in strengthening commitment and motivation. From this perspective, the demands expressed by this cluster of students are a call for greater pedagogical soundness and educational coherence, key aspects in asynchronous and flexible environments.

Demanding students require individualized, constructive, and timely feedback. As Salinas-Ibáñez and Hernández-Pina (2023) point out, formative feedback is essential in virtual environments because it encourages reflection and continuous improvement and makes students feel supported academically. Students’ assessment of synchronous sessions and clear instructions on platforms is consistent with the principles of effective online pedagogy, in which feedback is a formative, dialogic, and constant process. These expectations challenge teachers to go beyond simple grading and build pedagogical links that stimulate both cognitive and affective engagement.

This cluster of students also stands out for their sensitivity to how teachers use digital tools and integrate them into the educational process. Beyond the simple use of virtual platforms, these students demand a meaningful incorporation of technologies that enrich learning through active communication channels and interactive resources that promote collaborative and immersive experiences. In this regard, García-Peñalvo et al. (2020) argue that the perception of quality in a virtual course is closely related to the teacher’s ability to generate innovative and contextualized interactions through the strategic use of technology. Therefore, students’ expectations respond to a conception of digital teaching that combines technical competence with pedagogical soundness.

Similarly, Pardo and Cobo (2021) emphasize that affective, relational, and sociocultural components fundamentally determine the quality of virtual education. For this cluster of students, the role of the teacher goes beyond disciplinary mastery: they expect the teacher to act as a facilitator of learning environments in which participation, diversity, and collective knowledge construction are encouraged. These demands reflect a profound vision of equity, in which timely support, empathy, and adaptability are considered fundamental pillars for achieving meaningful learning.

These students require different types of assessments for their learning. Flores and Gutiérrez (2022) argue that virtual education should incorporate different types of assessment that can capture more skills rather than merely measuring memorized knowledge. For this student profile, assessment is a pedagogical opportunity to demonstrate learning, receive formative guidance, and advance in their academic career, beyond its administrative function. The demand for assessments adapted to their circumstances highlights the importance of promoting flexible teaching practices aimed at continuous improvement in virtual learning environments.

4.3. Cluster 2: Digitally Competent Students

The cluster of students with digital skills represents only 3% of the sample and constitutes a key profile within the virtual learning ecosystem. Their high level of digital literacy and remarkable autonomy allow them to adapt quickly to the online environment and make the most of the technological tools available. Their mastery of various specialized software reflects not only their ability to access information but also to transform it and generate their content, an essential competence in 21st-century education (Ng, 2021; van Laar et al., 2020).

These students’ technological mastery goes beyond the basic use of educational platforms, as they incorporate various digital tools and collaborative spaces into their academic work, taking on an active role as generators of knowledge. Ng (2021) points out that students who use technology in a transformative way become co-creators of the learning process, and this reinforces their critical thinking, creativity, and capacity for innovation, which is why it is essential to establish pedagogical strategies that stimulate these skills through activities focused on problem solving, the development of digital projects, and applied research.

In addition, this cluster benefits from favorable technological conditions. Their access to stable internet, backup devices, and mobile technologies enables them to participate in synchronous sessions without interruption and manage their time flexibly for asynchronous activities. According to Ramírez-Montoya and Valenzuela (2021), these conditions foster autonomy, resilience, and self-regulation when dealing with the challenges of virtual learning.

Their profile poses challenges for instructional design, as these students require more complex activities, and it is necessary to address pedagogical innovation through the development of personalized and intellectually stimulating resources, since repetitive or undemanding content can lead to demotivation. Gutiérrez-Castañeda and Ceballos-López (2024) argue that learning environments should go beyond the standard use of platforms and integrate technology in a creative, meaningful, and flexible way.

When used correctly, this cluster of students becomes a strategic ally within the virtual classroom, as it can create digital content, support their peers, and skillfully manage communication tools to improve collaborative participation. According to UNESCO (2023), this profile generates good practices in the field of digital education and the potential to exemplify an inclusive and visionary use of technology in educational contexts.

4.4. Cluster 3: Students Dependent on Didactic Material

The students who are heavily dependent on teaching materials make up the largest cluster in the sample, accounting for 45%. They demonstrate a strong need for well-structured, sequential, and sufficient educational resources within the virtual learning environment. This behavior is consistent with the findings of Ramírez-Montoya and Valenzuela (2021), who state that clear pedagogical organization significantly promotes student self-regulation.

Aligning course objectives with available content is essential for reducing cognitive overload and avoiding disorientation in digital contexts. In this regard, Mayer (2020) argues that content which is clearly aligned with course objectives and presented sequentially acts as cognitive reference points, optimizing information processing and strengthening meaningful learning.

According to Salas-Pilco and Yang (2023), the diversity of resources responds to a multimodal approach that caters to different learning styles, stimulates motivation, and promotes more profound understanding.

When materials are inadequate, out of date, or disorganized, students experience insecurity, confusion, and a lack of motivation. Hodges et al. (2020) argue that autonomy in online environments hinges not only on students’ self-regulatory skills, but also on solid, coherent, and reliable educational resources. Therefore, instructional design aimed at this type of student must anticipate possible information gaps and provide carefully selected resources.

Garrison and Vaughan (2021), from their socioconstructivist approach, affirm that virtual environments strengthen understanding when they promote collaborative dynamics aimed at the joint construction of knowledge.

Ultimately, the student profile discussed here requires pedagogical planning that focuses on their experience, particularly in contexts where the clarity, accessibility, and consistency of resources are key to educational success.

4.5. Cluster 4: Students with Technological Barriers

Students who face technological challenges comprise 28% of the sample and draw attention to the structural disparities that continue to affect distance education. These barriers are not merely technical issues; they reflect broader socioeconomic and territorial conditions, as noted by UNESCO (2021) about the persistent digital divide in marginalized contexts.

These students often rely on public networks, community spaces, or unreliable mobile connections, which limits their access to synchronous classes, interactive activities, and educational materials hosted on digital platforms. Laufer et al. (2021) highlight that COVID-19 accelerated the digital transformation of higher education but also deepened existing inequalities, as limited infrastructure and insufficient teacher training can turn digital education into a divider rather than a bridge for equity. This digital fragmentation reduces academic achievement and undermines the right to an equitable, quality education.

Cluster analysis made it possible to identify this group as a specific segment with needs requiring differentiated attention (Gutiérrez-Castañeda & Ceballos-López, 2024). This methodological technique helps capture the diversity of the student body and suggest more inclusive teaching methods. Salinas et al. (2022) explain that technological limitations put students in a difficult position: they have to navigate online learning in inadequate conditions with minimal technical support from their institutions. Because of this, it is vital to completely change how we plan teaching and develop digital resources, ensuring that all students receive an equal education.

In general, ITVE students reflect a highly autonomous and heterogeneous community that balances multiple responsibilities. This diversity highlights the coexistence of students with advanced digital skills, others who depend on structured resources and constant guidance, and those with limitations in connectivity, technological access, and infrastructure. These profiles underscore the need for virtual learning environments that are flexible, accessible, and responsive to different realities, aligned with SDG 4. The analysis of student profiles provides a clearer perspective on how institutions should integrate equity and inclusion into online education, ensuring that technological infrastructure, pedagogical design, and academic support create relevant and socially responsive learning spaces.

5. Limitations and Implications

This study contributes significantly to the field of virtual education by identifying four differentiated student profiles based on pedagogical, technological, and access variables in the rural and territorially diverse context of Southern Mexico. This study recognizes that certain methodological aspects may limit its scope; however, it treats them as strategic decisions aligned with the contextualized nature of the exploratory phase.

In the first place, the sample size (n = 71) does not seek to achieve broad statistical representativeness, but rather to capture with depth and internal validity the diversity of student experiences within the Instituto Tecnológico del Valle de Etla (ITVE). This delimitation allowed for a detailed, empirically robust, and situated approach to the phenomenon studied.

This methodological strategy lays the groundwork for future sample extensions, inter-institutional comparative studies, and longitudinal analyses.

At the analytical level, the model made it possible to identify four differentiated profiles in the virtual learning environment. Although the segmentation obtained was static, its value lies in providing a structured and replicable basis that allows researchers to adapt it for follow-up studies and impact evaluation. Future integration of variables such as digital self-efficacy, family conditions, or emotional resilience will enrich the model and increase its explanatory power.

In terms of implications, the findings offer a clear path to guide institutional policies and pedagogical strategies, such as the identification of differentiated profiles, which allows progress towards an effective personalization of e-learning, considering both students with high digital competencies and those who require accessible materials and structured accompaniment. The results underline the importance of strengthening the pedagogical, socioemotional, and technological competencies of teachers, not only as transmitters of content but also as key agents for equity and student retention.

6. Conclusions

This study was able to characterize the students of the Instituto Tecnológico del Valle de Etla (ITVE) in the distance education modality, identifying four differentiated profiles that allow a better understanding of their trajectories, needs, and challenges in the virtual environment. The findings show that the student population is highly heterogeneous, both in sociodemographic terms and in terms of their digital competencies and technological access conditions, which reinforces the need to design equitable and inclusive institutional policies.

In response to the research question about emerging profiles in virtual environment learning and the associated technological challenges, the analysis identified four clearly defined clusters of students: demanding, digitally competent, dependent on didactic material, and with technological barriers. Each of these profiles places specific demands on instructional design, teacher interaction, course structure, and institutional infrastructure.

The predominant socio-demographic profile reveals that a majority of students are young, economically active women who choose distance education as a strategy to combine studies with work, family care, or geographical distance. This reality underscores the urgency of strengthening the gender approach, curricular flexibility, and technological adaptability as axes of institutional educational policy.

Among the most critical technological challenges are connectivity limitations, unequal access to devices, and dependence on digital resources hosted on institutional platforms. While some students show a high degree of autonomy and digital mastery, others require more guided structures, complete materials, and constant support, which forces a rethinking of the teaching model from a differentiated perspective.

The results also reveal that effective use of technology is not just a matter of equipment, but of meaningful pedagogical integration. Those students who achieve a satisfactory educational experience are those who access well-structured, interactive courses with timely feedback and diverse materials. On the other hand, those with technical difficulties or less digital literacy lag in their learning processes.

Finally, the study provides evidence to guide institutional strategies that promote digital equity, the personalization of learning, and the strengthening of teaching skills. These strategies should be sensitive to the real context of the student body and aligned with the principles of SDG 4: ensuring inclusive, equitable, and quality education for all. Based on this characterization, the ITVE can move towards a more resilient, accessible, and learner-centered distance education model.