Apertura. Revista de innovación educativa‏

Vol. 8, Núm. 2 / octubre 2016 – marzo 2017 / ISSN 2007-1094

The inclusion of ICT university students:

A view from the connectivism

 

Claudia Islas Torres[1]

Orlando Delgadillo Franco[2]

 

Abstract

The article stems from a study based on the assumptions of connectivism, whose objective was to understand how ICTs are included in the learning of university students. A type of quantitative and transversal work with a descriptive scope was applied to a random stratified sample of 684 undergraduate students from a public university in the state of Jalisco, Mexico. We include the validation of the instrument that helped collect the data which, having been analyzed, indicates that the inclusion of ICT is made when students use them to move from the confusing to the definite state, and they are applied to the learning problems that involve identifying important information, as well as the reliability of the site that was consulted. 82% of participants have searched for information in scientific databases, and they relate it by applying it to the knowledge acquired through electronic means, sometimes using tools and giving meaning to data by developing graphic organizers or summaries, in which the students express what they have understood. Connectivism was an appropriate reference to interpret the inclusion of ICTs, as well as to recognize the influence of the context in which actions are developed

 

Keywords: Connectivism, ICT, inclusion, digital competence

 

 

INTRODUCTION

 

Presently, it is impossible to deny that we are in an era of influential, important and innovative changes. This type of evolution is largely due to the arrival and incursion of information and communication technologies (ICTs); therefore, humanity lives in a process of globalization in which knowledge and science can be observed clearly. This situation is a reality that cannot escape educational institutions, these being where knowledge is largely produced.

 

It is, therefore, necessary to reflect on how higher level education faces this evolution by involving the academic community and making the students include ICTs in their educational tasks and, through them, exponentially increase their creation and production capabilities with regard to content and information. The technologies, and specially internet and its Web 3.0 tools, make individuals sympathize with them, and thus, they get better opportunities for the access of knowledge and to manipulate and transform it even apart from the very institutions and their professors.

 

This context favors the digital natives, individuals who use the digital language, distinguished by computer games, videos, internet, among other applications; a situation that demands that the professors use innovative teaching methods which will allow them to connect to their students during their learning process (Prensky, 2010). However, according to Siemens (2010), educational institutions seem to be closed off to this evolution, as they are far from complying with the requirements of globalization with regard to economy, politics and society.

 

In light of the foregoing, we set out to carry out a non-experimental, transversal, quantitative study, through which we would identity and be able to describe, departing from the contributions of connectivism, how is it that the inclusion of ICTs in the learning actions of university students happens. Said theory helps figure out if they evolve so that the learning is acknowledged as a collective of individual opinions (Siemens, 2010) that converge into a series of networks which can be turned into knowledge.

 

We detail the validation procedure of a scale type instrument that helped collect the information necessary to achieve the objective of the investigation. The aforementioned study was done in a public university of the state of Jalisco, Mexico, with the participation of 684 students from fourteen different careers.

 

 

BACKGROUND

 

The ICTs have given rise to new skills, such as those that are formally promoted, the didactic sequences and the strategies for work in the classroom that admit non-linear process. Students frown upon traditional methods, which are obsolete and to a certain extent, are intransigent due to the lack of activity, resources and consistency on what is learned with what occurs in everyday life. The professor is responsible for the fulfillment of their student’s expectations and even those of the university in the 21st century (Salazar, 2014).

 

It is important to consider that the evolution of knowledge originates a reflection on the way in which the apprentices reach the construction of the same, like what they learn in educational institutions prompts them to get involved in the reality of the changes being lived, as the combination of different disciplinary areas is more and more present; this is because people are not learning solely with formal education, but also through different means, practice communities, networks of individuals, the performing of tasks, etc.; learning has turned into a continuous process throughout life, while technologies modify the ways to receive, organize, abstract and expose what has been learned. In this sense, connectivism arises as an alternative framework to the previous learning theories that did not foresaw the emergence of technologies and networks (Siemens, 2010).

 

It is possible to find several references on this theory, which define and explain it; the documents that show results on their application are far lesser, for example, Techakosit and Wannapiroon (2015) used connectivism as a theoretical fundament for the configuration of a learning environment with a heightened reality, all in order to evaluate the suitability of the theory to improve scientific literacy. Its results indicated that the theory was suitable for the objective and that the majority of the participants in the investigation improved their literacy.

 

One more referent on the application of this fundament is the one presented by Sitti, Sopeeral and Sompong (2013), who applied connectivism as a base for the implementation of a learning model that allowed the improvement of the skills of university students for the resolution of problems with ICTs, as well as to measure the impact of these within the classrooms with regard to teaching and learning. The results show that the model was sufficiently accepted by the students and that they managed to implement it in the resolution of activities based on problems. In the same manner, they identified that the web technology, especially social networks, are an important feature for the collaboration and integration of activities that generate problem-based learning.

 

For their part, Kultawanich, Koraneekij and Na-Songkhla (2015) defined the concept of connectivism as a new learning theory; they carried out a study that aimed to describe the development and the validation of the information of three tools to measure the informational literacy of the students and verify if the requirements of the current environments were complied, where the abilities for the constructions and creation of knowledge from the interaction, collaboration and communication with professors and classmates through web tools are required.

 

In the above paragraphs, we give account of some investigations that reference the use of connectivism for their foundation; in the search for literature on this topic, we found that, for the most part, the documents coincide in how they present the background, definitions and appreciations on this theory, and there are fewer reports that show empirical data that evaluates the relevance of it as applicative, interpretative or explicative framework. This lack of information represented a problem that developed into the project that we present here; with this, we are adding to the elaboration of a report that will comprise hard facts on the use of said theory as a theoretical framework.

 

CONCEPTUAL REFERENTS

 

The emergence of connectivism is attributed to Siemens and Downes in 2004 (Siemens, 2004); since then, their proposal in relation to the social learning has prevailed, which is of relevance for modern students. This theory defines learning as a process that takes place in environments with diffuse changes which are not under the control of the individuals. Learning is defined as the knowledge that can be processed and that may reside outside ourselves (within an organization or a database); it is oriented towards sets of specialized information and to the connections that allow us to learn more. It is based on the individual ideas and opinions, the assessment of the diversity of perspectives of others, permanent learning, the building of relationships, interdisciplinary connections, current information and risk-taking; these are the same principles that can be found in several current technologies that the students use on a daily basis: Facebook, Whatsapp, Wikis, YouTube, among others.

 

In this sense, connectivism can be understood as a network that connects packages of specialized information and determines the existing relations that allow us to expand our knowledge. According to this theory, a network has at least two components: nodes and connectors. A node could be any external entity: people, libraries, organizations or any type of information, so that there can be an endless number of connections. This internal network that is formed in our minds is dynamic and intelligent. Throughout time, each node gains or loses importance; in this manner, when losing value, the node can be eliminated from the network. Thus, it is more important to decide what to learn according to the relevance of that piece of information and to know where to find it. Therefore, in such a changing world, the professor has to prepare the student to create and evaluate networks that, in a continuous process of interaction, generate knowledge (Vintimilla, 2015; Gutiérrez, 2012).

 

Presently, technological advances allow the platforms, students and professors to be able to interact in a similar manner to classroom learning through programs and systems that exemplify connectivism, such as Skype and Facetime; face to face interactions and asynchronous conversations may happen as well. Furthermore, the tools to share work online, such as Google Drive, Wikispaces and Dropbox, make it possible to access information repeatedly in time and space, facilitating collaborative work among students (Brescó and Verdú, 2014).

 

In this sense, connectivism suggests that there can be learning ecosystems in constant evolution that provide the people who learn, the control to explore and direct themselves in the direction they want, with the help of synchronous and asynchronous tools (Gutiérrez, 2012). Thus, ICTs help individuals be less passive in the reception of information and prompts them to participate in the co-creation of content. This active knowledge construction, from Siemens’ perspective and consistent with the authors of this work, generates the interaction between subjects, which is considered an important factor for the foundation of networks represented with nodes that connect to generate activity and communities that share, converse and think in a cooperative manner departing from a common interest.

 

For Giesbrecht (2007, cited in Gutiérrez, 2012), Sobrino (2014), Casco and Aguirre (2015), De la Hoz, Acevedo and Torres (2015) connectivism is a pedagogical proposal that provides those who learn with the capacity to connect with one another through social networks or collaborative tools from the new realities derived from the Web 2.0.

 

The particularity that the ICTs have given to the learning methods caused Siemens to brag that his proposal on connectivism would define learning for the digital era, and would classify it as an emerging theory that surpasses the previous learning theories (behaviorism, cognitive learning and constructivism); however, Zapata (2011), Gutiérrez (2012) and Sobrino (2014) differ from this posture and argue that connectivism should not be considered a theory, as it lacks the elements that would make it so in its specifications (objectives, values, methods and contributions), though they acknowledge that it surpasses the limitations of the other ones when interpreting the effects, advantages and conception of learning in environments where ICTs exist; furthermore, information is processed and there is communication.

 

In this sense, Sobrino (2014) expresses some limitations identified in the connectivism proposal; among them, the management of information, search, exploration or browsing stands out, as it does not guarantee learning; the possibility of building networks and making contact with the nodes could generate a series of relations that are not, necessarily, the representation of knowledge, as the individual is left to understand the structure of the network and interpret the meanings of the same; a commitment is made with the informal, open and divergent contexts, and fewer importance is given to the role of the professor and of institutions in general. What is expected of the students could surpass reality; very little is said about their analysis skills, visualization and overview that lead them to complex thoughts; the contributions of the rest do not necessarily encourage knowledge in itself.

 

These considerations reveal the different postures that could or could not be in favor of the conception of connectivism as a theory; however, for the purposes of this investigation, we think that the principles proposed by Siemens could be retaken and adapted to present a series of steps applicable to formative contexts and that in this case allowed for more clarity to guide and explain the investigation:

 

1. Going from confusing to defined. On the understanding that knowledge is not acquired in a linear manner, cognitive operations that involve technologies for the storage and recovery of information must be carried out. To define and organize the ideas to go from confusing to what has been defined and know what is going to be searched and learned.

 

2. Decide where to look. Know how and what is complemented with the finding of knowledge, which implies recognizing between what is useful or important information and what is not.

 

3. Dive into the information and decide what is useful and what is not. The need to remain updated and well informed implies delving into the world of information, but taking the precautions to know what is useful; what information is valid and what is not. It requires the professors and students to know the websites, databases, etc., which contain reliable, truthful and updated information.

 

4. Relate information and connect in order to create knowledge. Develop the necessary cognitive skills to identify how to connect the established knowledge (connections) between the areas, ideas and fundamental concepts.

 

5. Share with other people. We do not learn from a single experience, but rather also from other people’s experiences, so the collaboration of other people is necessary.

 

6. Give meaning through identifiable patterns. In order to learn it is necessary to acknowledge the patterns that could be hidden in the chaos of information; this implies going beyond linear abstraction in order to discover what is hidden, and thus, create important connections that represent comprehension and, at the same time, knowledge, and take the information that is useful to generate reflexive critical thinking.

 

7. Presentation and feedback. Learning and the construction of knowledge depend on the diversity of opinions, which implies presenting the knowledge produced in order to receive feedback from classmates and the professor.

 

8. We learn from the environment and in the environment. Both the students and the institutions are learners; therefore, connectivism attempts to explain individual and institutional learning.

 

9. Generate learning networks. Connect between areas, ideas, concepts and link the nodes that are generated through the selection of information, so that a modification to any node of the network is reflected as a wave in everything; this implies the creation of a personal learning network.

 

With the above, we can confirm that the learning model of connectivism is adjusted to the society of knowledge, as they take advantage of the use of collaborative tools by the learners (González, 2015), in addition to considering the virtual environments as channels that allow the student to create work and knowledge construction zones with other people; this way, it has a foundation for its cognitive structure (Rodríguez and López, 2013).

 

 

METHODOLOGY

 

In this study, we formulate the following investigation question: what are the learning actions of the university students that could be explained through connectivism and those that include the ICTs? This was done in order to confirm if said actions have evolved so that their learning is acknowledged as a collection of individual opinions that come together in a series of networks in which they can achieve the construction of knowledge.

 

In order to respond to this investigation question, we proceeded to elaborate a scale type instrument comprised by 33 items, in which we represented the principles of connectivism. The method applied was of the non-experimental, quantitative type.

 

Investigation procedure

 

The study was carried out at the Centro Universitario de los Altos of the University of Guadalajara, Mexico, which offers fourteen degrees: Law, part-time Law, Agroindustrial engineering, Computer Engineering, Livestock Systems Engineering, Administration, Dental Surgery, Accounting, Nursing, International Business, Nutrition, Psychology, Zoo technical Veterinary, and Medical Surgery. The student population recorded in the 2015-A calendar was of 3,663 students.

 

For the management of the population that would participate in the investigation, we did a calculation of randomized sampling, determined under a 99% reliability factor, a 95% response rate and a 5%maximum error. Said calculation indicated that the sample that ought to be considered was of 684 students, which represented 18.67% of the total population. In addition to the foregoing, we did a stratification of the sample so that there would be equal representativeness per degree, semester and gender of the participants.

 

The investigation process also foresaw the design and implementation of a scale type instrument in which the response options were Likert with five types of responses: always, almost always, sometimes, almost never and never; furthermore, question that would refer to data from the nine steps of connectivism previously proposed were included.

 

For this investigation, it was necessary to have a reliable instrument; therefore, we proceeded with the validation of the items through different alternatives. First, we applied Cronbach's alpha, which is a consistency model that refers to the degree in which the instrument measures that which it pretends to know, that is, it assumes that the items evaluate the same constructs and that they are highly correlated; thus, the closer the alpha value is to 1, the greater the consistency of the analyzed items (Oviedo and Campo-Arias, 2005; González and Pazmiño, 2015).

 

According George and Mallery (2003), the representative values of the alpha could be in the following ranges:

 

·      Alpha coefficient > 0.9 is excellent

·      Alpha coefficient > 0.8 is good

·      Alpha coefficient > 0.7 is acceptable

·      Alpha coefficient > 0.6 is questionable

·      Alpha coefficient > 0.5 is poor

·      Alpha coefficient < 0.5 is unacceptable

 

The fact is that the criteria established and identified by different authors oscillate in a range between 0.7 and 0.9 to indicate the good internal consistency of a scale (Oviedo and Campo-Arias, 2005). Gadermann, Guhn and Zumbo (2012) mention that Cronbach’s alpha has been cited in 76% of the cases of social sciences articles to evidence the validity of the tests (García, González and Jornet, 2010).

 

The instrument was also validated by experts in the subject, through a pilot test; similarly, a Bartlett’s sphericity test was carried out, which has the objective of evaluating if the factorial model (or the extraction of the factors) the study applied is significant.

 

The KMO test (Kaiser, Meyer and Olkin) relates the correlation coefficients. The closer the value obtained from the KMO test is to 1, it implies that the relation between the variables is high. If KMO ≥ 0.9, the test is very good; notable for KMO ≥ 0.8; medium for KMO ≥ 0.7; low for KMO ≥ 0.6; and very low for KMO < 0.5.

 

Bartlett’s sphericity test evaluates the applicability of the factorial analysis of the variables that were studied based on the following assumptions: if the value obtained is < 0.05, the null hypothesis is accepted (H0), which indicates that a factorial analysis can be applied. If the value obtained is > 0.05, H0 is rejected and the alternative hypothesis is accepted (H1), the factorial analysis cannot be applied (Universidad de Alicante, 2011).

 

The instrument applied was subjected to the validation process; therefore, the exploratory factorial analysis was carried out. The findings of that investigation are presented in the results section. All of this was done in order to identify the frequencies of the population that was studied, to investigate the significant actions that the university students carry out when using ICTs. The data was calculated through the SPSS statistical package version 19.

 

 

RESULTS

 

The demographic characteristics of the population participating were the following: 396 female and 288 male students. The distribution by degrees was: Law (32), Part-time Law (19), Agroindustrial Engineering (24), Computer Engineering (27), Livestock Systems Engineering (24), Administration (60), Dental Surgery (61), Accounting (63), Nursing (63), International Business (59), Nutrition (65), Psychology (64), Zoo technical Veterinarian (47), and Medical Surgeon (78). The average age of the participants was of 21 years.

 

The first validation process implemented was the calculation of Cronbach’s alpha through the SPSS package. The result obtained was of 0.795 (good) (see Table 1), which indicated the reliability of the instrument.

 

Table 1. Reliability statistics.

Cronbach’s alpha

Number of elements

0.795

20

 

We then proceeded with the application of the method of main components, the KMO indexes (0.853) and Bartlett’s test with significance (p=0.000), which indicates that the model is suitable and does not present sphericity (see Table 2), i.e., that the H0 (null hypothesis) is accepted; therefore, the factorial analysis can be applied.

 

Table 2. KMO and Bartlett’s test.

Kaiser-Meyer-Olkin measure of sample size

0.853

Bartlett’s sphericity test

Approximate square Chi

2503.354

gl

190

Sig.

0.000

 

Based on the criteria of eigenvalues greater than 1, we obtained six factors that explain 53.86% of the variance. When applying the extraction method of main components in the factorial analysis, the results were what we present in Table 3.

 

 

Table 3. Total variance explained by the extraction method:

analysis of the main components.

Total variance explained

Component

Initial Eigenvalues

Sums of the square saturations of the extractions

Total

Percentage of the variance

Accumulated percentage

Total

Percentage of the variance

Accumulated Percentage

1

4.621

23,103

23,103

4,621

23.103

23,103

2

1.513

7,563

30,666

1,513

7.563

30.666

3

1.381

6.904

37.570

1.381

6.904

37.570

4

1.174

5.870

43.441

1.174

5.870

43.441

5

1.084

5.418

48.858

1.084

5.418

48.858

6

1.000

5.001

53.860

1.000

5.001

53.860

7

.976

4.882

58.742

 

 

 

8

.838

4.189

62.931

 

 

 

9

.789

3.947

66.878

 

 

 

10

.771

3.853

70.731

 

 

 

11

.708

3.538

74.269

 

 

 

12

.692

3.461

77.730

 

 

 

13

.668

3.338

81.068

 

 

 

14

.646

3.231

84.299

 

 

 

15

.602

3.008

87.307

 

 

 

16

.566

2.830

90.137

 

 

 

17

.555

2.776

92.913

 

 

 

18

.501

2.506

95.419

 

 

 

19

.467

2.337

97.756

 

 

 

20

.449

2.244

100.000

 

 

 

 

As we can observe in Table 4, the representation of the factors is reflected in the components: utility, ability, inclusion, significance, collaboration and information network, which allude to the steps that are proposed by connectivism in order to understand the inclusion of ICTs in the learning actions of university students.

 

Table 4. Extracted components.

1

Utility

2

Ability

3

Inclusion

4

Significance

5

Collaboration

6

Information network

 

The graph represents the group of factors through the sedimentation of components that justifies the selection of six factors (with values higher than 1); something that had already originated when establishing the eigenvalue criterion in the unit.


 

Component sedimentation graph.

 

 

 

 

 

The results indicate that the validation of the instrument showed satisfactory properties in relation to the analysis performed, and it is a valid and reliable measure for the findings of this investigation. When obtaining the extraction of the components, six factors from the nine that were originally planned were achieved; the reason being that the amount of total variance obtained was greater than 1, so it represents the more significant elements of the inclusion of ICTs in the learning actions.

 

According to the steps that from the point of view of connectivism explain the inclusion of the ICTS, and with a descriptive clarification, we found the following: in the subject of how the students go from what is confusing to what is defined, in a such a manner that they apply cognitive operations for the recovery of information, they indicated that sometimes (mean: 3.73, standard deviation: 0.817) when facing a problem, they think of several alternatives on how they can solve it through the use of technologies; furthermore, they considered that sometimes (mean: 3.61, standard deviation: 0.853) the formation that they have received based on the use of the ICTs by the institution has been enough to generate knowledge from them, and they are able to define and organize the ideas and know what it is that they should look for and learn.

 

In order to decide where to look and in this way complement the knowledge that they have, they stated that they are able to, almost always (mean: 4.07, standard deviation: 0.747), identify the important information and the reliability of the site, but they consider that sometimes (mean: 2.93, standard deviation: 1.193) the electronical media are distractors in the realization of tasks.

 

Regarding the use of specialized databases, 82% of the interviewees said that they do use them and 17.9% said that they do not; the former said that they mostly use: Redalyc, Dialnet and Psicothema. According to the type of sites selected, we observed that the students that do make use of these databases are from the social and medical areas, and that they have used them at least once. From the participants, 78% know that their university provides a virtual platform with access to electronic books, magazines and articles, the rest commented that they were not aware of this feature. For the students, the Moodle platform is almost always (mean: 3.57, standard deviation: 1.090) a good tool to support learning, as well as a good resource for the realization of activities.

 

Regarding the actions of delving into the information and deciding which is useful or not, the students sometimes represent it (mean: 3.49, standard deviation: 0.891) and delve into it to give meaning to what they understood through graphic organizers, comparative tables, cards, etc.

 

The results indicate that the learners relate information and connect it in order to create knowledge when they, almost always (mean: 3.97, standard deviation: 0.773), apply cognitive abilities such as interpreting, reflecting on and evaluating necessary information in order to figure out how to connect knowledge between the areas, ideas and fundamental concepts; this almost always leads them (mean: 3.83, standard deviation: 0.732) to the application of the acquired knowledge from the Web to real situations or learning problems.

 

Regarding the sharing of information with others, the students state that sometimes (mean: 3.42, standard deviation: 1.118) they use collaborative tools to do school work and to share information of interest through websites such as Dropbox, Google Drive, blogs, Evernote and One Drive; this demonstrates the principle that we do not learn from just one experience, but rather also from the experience of other’s, which requires the collaboration with other people. Likewise, students almost always (mean: 3.88, standard deviation 0.876) rely on each other for the use of ICTs and, sometimes (mean: 3.98, standard deviation: 0.728), they work in teams to strengthen their knowledge and to select information according to their criteria. In a similar manner, they make use of social networks to communicate and almost always the most representative of these are Facebook and Whatsapp.

 

The students give meaning based on identifiable patterns when, in order to learn, they discover those that may have been hidden in the chaos of information, and they sometimes do this through abstraction (mean: 3.49, standard deviation: 0.891), by symbolizing this with other techniques, such as that of graphic organizers, the identification of key words and ideas in texts or documents from the Net. Their activities comprise comprehension and, at the same time, knowledge, and they obtain the information that proves useful in the best of cases to generate reflexive critical thinking.

 

The learners say that sometimes (mean: 3.17, standard deviation: 1.118) they reinforce their learning thanks to presentations and feedback; therefore, this weakness is something that should be reviewed and improved, in the understanding that learning and the construction of knowledge depend on the diversity of opinions, which makes it necessary to promote feedback among classmates and professors.

 

Based on the assumption that people learn from the environment and in the environment, the students considered that, sometimes (mean: 3.34, standard deviation: 0.949), the institution supports the innovation of technological resources to add to the improvement of knowledge through the application of ICTs; in this sense, connectivism explains individual and institutional learning in the understanding that institutions should attend to the needs of the learners and provide resources that satisfy their demands; thus, this is turned into a learning circle, as it helps the students evolve while involving the institution.

 

From the actions of the students, their ways of generating learning happen when they work in teams, reinforce their knowledge and select information based on their criteria. They almost always (mean: 3.67, standard deviation: 0.905) expect the professor’s motivation to be able to connect between areas, ideas, concepts and to link the nodes that are created through the selection of information; in this regard, we infer that the teaching practice could be alien to what students require.

 

 

CONCLUSIONS

 

To speak about the theory of connectivism as a basis of this study facilitated a different vision for the interpretation of the data, as it did not only refer to the descriptions, but also from the perspective of the theory which is to interpret the actions of the students to include the ICTs in their academic activities. The results indicate that their learning is influenced by the characteristics of the context in which they develop; they are surrounded by technology, information, communication networks, etc., therefore, the construction of knowledge is done under the terms of what the students are able to share, collaborate, discuss or reflect on with their classmates and professors on topics of common interest, even though feedback is not done as often as one would expect.

 

Similarly, said conclusion happens when the students receive the sufficient formation to use the technologies from the institution, and thus, they are able to apply them to learning problems that involve the identification of important information, in addition to the reliability of the site that was consulted; they relate the information and apply the knowledge that was acquired through electronical media and use, to a lesser extent, the collaborative tools for the execution of tasks or to share information with their classmates, which gives meaning to the data when producing knowledge from what they have understood.

 

The validation of the instrument applied with an alpha of 0.795 represented a level of reliability for the authors of this work with regard to the information that it reflects, as the theoretical construct that we attempted to measure was represented in the factors that explain the inclusion of ICTs in the learning processes of university students. The investigation group considers, as a future project, to apply this scale in other populations and with other types of institutions.

 

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Received: 25/11/2015

Published: 07/01/2016

 



[1] PhD in Educational Systems and Environment. Research Professor of the Department of Organizational Studies of the Centro Universitario de los Altos, Universidad de Guadalajara, Mexico.

[2] Computer Engineering student of the Centro Universitario de los Altos, Universidad de Guadalajara, Mexico.

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Apertura vol. 11, núm. 1, abril - septiembre 2019, es una revista científica especializada en innovación educativa en ambientes virtuales que se publica de manera semestral por la Universidad de Guadalajara, a través de la Coordinación de Recursos Informatiivos del Sistema de Universidad Virtual. Oficinas en Lerdo de Tejada 2640, colonia Arcos Vallarta, C.P. 44130, Guadalajara, Jalisco, México. Tel.: 3268-8888 ext. 18775, www.udgvirtual.udg.mx/apertura, apertura@udgvirtual.udg.mx. Editor responsable: Alicia Zúñiga Llamas. Número de Reserva de Derechos al Uso Exclusivo del Título de la versión electrónica: 04-2009-080712102200-203, e-ISSN: 2007–1094; número de Reserva de Derechos al Uso Exclusivo del Título de la versión impresa: 04-2009-121512273300-102, ISSN: 1665–6180, otorgados por el Instituto Nacional del Derecho de Autor. Número de Licitud de Título: 13449 y número de Licitud de contenido: 11022 de la versión impresa, ambos otorgados por la Comisión Calificadora de Publicaciones y Revistas Ilustradas de la Secretaría de Gobernación. Responsable de la última actualización de este número: José Mariano Isaac Castañeda Aldana, Lerdo de Tejada 2640, colonia Arcos Vallarta, C.P. 44130, Guadalajara, Jalisco. Fecha de última actualización: 29 de marzo de 2019.