Meta-analysis of research in e-learning published in Spanish journals
© The Author(s) 2016
Received: 23 November 2015
Accepted: 11 March 2016
Published: 9 July 2016
E-learning is a way of teaching that presently makes greater sense in the process of democratization of education. This articles describes a meta-analysis performed on Spanish journals found in Social Sciences and Law areas (N = 118) indexed in JCR and SCOPUS and awarded with the FECYT seal, that have published articles on this topic in the last 5 years (2011–2015). The total number of articles analyzed was 108. The main results show that these have focused on studies on academic efficiency, learning, teaching and cognitive styles, and interaction and communication. The main conclusion reached was the need for a study on the reasoning behind why elements such assessments are not treated in these articles.
KeywordsE-learning Meta-analysis Research
E-learning is becoming one of the most-utilized teaching methods in different universities, institutions and businesses. Salinas, Darder, and De Benito (2015), when defining it, have pointed to the need to differentiate between the three different concepts: e-learning, b-learning and m-learning, which are conditioned not only by advances in telecommunications, but by transformations in the teaching fields as well.
Without wanting to do an exhaustive analysis of the three terms mentioned, we still agree with different authors (Castaño & Cabero, 2013; Sevillano & Vázquez, 2015; Thorne, 2012; Torres, Infante, & Torres, 2015), when they state that “e-learning” refers to virtual education supported by the presentation of content through the Internet and the use of different synchronous and asynchronous communication tools to establish the interaction between the teachers and the learners, “b-learning” is the combination of learning through the Internet as well as face-to-face, and “m-learning” is virtual learning supported by mobile devices, taking advantage of their mobility and ubiquity.
To these definitions, the term e-learning 2.0 has been added, which surpasses the simple addition of the distance learning platforms, also known as LMS with the Web 2.0 tools, resulting in a more critical and participative vision of the student in the process of learning, also implying a transformation of the virtual learning process and the roles played by the teachers and learners (Cabero, 2013).
Independently of modality, different authors give e-learning different advantages when used in learning processes (Gros, 2011; Roig, Mengual, & Rodríguez, 2013): it easily adapts to the student’s individual characteristics and needs, connects students that are located in different geographical areas, the learning pace is set by the students, updating of the content is fast, it offers spatial-temporal flexibility for learning, it facilitates the interaction between the people that participate in the teaching processes, the students themselves, and the students with the learning/teaching objects, resources in different formats (text, video,…) can be used, different types of synchronous and asynchronous communication tools are used for the interaction between participants, learning scenarios are broadened, and the delocalization of knowledge and where it is found takes place.
On his part, Bates (2011) points to a series of arguments with respect to the failure produced through its use: scarcely-motivating content; impossibility of downloading the content in printable version; lack of feedback by the instructor; lack of activities that are truly significant for the student’s needs; scarce personalization of the content; excessive time intervals lapsed between the application for taking the course and the course’s start date; lack of student’s motivation; low usability of the system, and technical failure and delay in resolving the incidents.
For Cabero (2006), in order to perform high-quality e-learning, it is not enough to focus exclusively on the technological aspects, but the focus should also be on the educational and organizational variables: this is how the content, methods employed, the e-activities performed by the students and the roles played by the teacher and tutors should be structured. These aspects are currently being performed in different research works (Andreu-Andrés & García-Casas, 2015; Gámiz, Montes, & Pérez, 2014; Islas, 2015).
Meta-analysis of research works are becoming traditions in the education domain, such as the ones performed by Cabero (2008) on e-learning research; Kinshuk, Huang, Sampson, and Chen (2013) on the tendencies of educational technologies; Bacca, Baldiris, Fabregat, Graf, and Kinshuk (2014), focused on augmented reality; Sangrá, González, and Anderson (2015) on the MOOCs, or Badia (2015), which analyzes research tendencies on technology-enriched learning.
The general objective of by this research work is: “to perform a meta-analysis of research performed in the last five years, referring to the analysis of the problems of performance and other learning variables of e-learning applied to teaching contexts that were published between 2011 and 2015”.
The criteria used was to only select research works published in Spanish journals that appeared in the JCR and/or SCOPUS databases, or had been awarded a FECYT Seal, and that also had an on-line version that could be used to more easily access them. Their inclusion within the area of Social Sciences was also taken into account.
Presence of e-learning articles in the journal selected
Anales de Psicología
Digital Educational Review
El profesional de la Información
Electronic Journal of Research in Educational Psychology
Enseñanza de las Ciencias
Píxel-Bit. Revista de Medios y Educación
Retos. Nuevas tendencias en Educación Física, Deporte y Recreación
Revista Complutense de Educación
Revista de Investigación educativa (RIE)
Revista Española de Salud Pública
Revista Eureka sobre Enseñanza y Divulgación de las Ciencias
Revista Iberoamericana de Educación
Scire: Representación y Organización del Conocimiento
Shows the frequencies and percentages reached for the different subjects analyzed in the instrument
Design of materials and/or digital objects used in e-learning activities
E-learning environments, distance learning platforms
Tutorships and advising used in e-learning activities
Strategies and didactic methodologies used in e-learning activities
Use of synchronous and asynchronous communication tools in e-learning activities
Use of assessment techniques and strategies in e-learning activities
Organizational and institutional aspects related to e-learning activities
Collaborative and cooperative actions used in e-learning activities
Specific studies on accessibility, usability and their effects on e-learning activities
The instrument used for the analysis of the articles was the “Analysis sheet of the content found in documents related to e-learning and the results found”, created by Cabero (2008) (http://www.grupotecnologiaeducativa.es/images/LIBROS/elearning08.pdf), which was constructed through a Delphi study. A quantitative analysis was performed with the data extracted. The codification of the articles was performed by the authors of the present study, after previously agreeing on the meaning of the different categories in the instrument.
The data on the year of publications show that similar values were found in the years analyzed (2011–2015), although there was a greater number of articles in 2012 (22.2 %) and a lesser number in 2011 (17.6 %), while the quantity was the same in the years 2013 and 2014 (19.4 %), with the articles published in 2015 contributing 21.3 % of the total.
The journals’ impact factor data shows that only one of the journals was indexed in JCR (f = 1, 0.9 %), the same as in JCR and SCOPUS combined (f = 1, 0.9 %), while most of the journals had the FECYT Seal (f = 42, 38.9 %), and only 24.1 % (f = 26) of the articles were found in journals that were both indexed by SCOPUS and had the FECYT Seal.
When classifying the articles by the numbers of authors, the articles with three authors totaled 33.3 %, followed by two (29.6 %) and more than 3 (19.4 %), with the least being the articles written by only one author (17.6 %).
The innovation and investigation experiments found in the articles were directed towards university surroundings (87.0 %); with a lesser number found for the institution-administration pair (0.9 %); those that had variable subjects of study (3.7 %), while those that were impossible to identify added up to 2.8 %.
Most of the studies were grouped under the label “various” (44.4 %), the descriptive studies added up to 27.8 % and the experimental studies to 23.1 %, with the case studies being the less addressed (4.6 %).
With respect to the research methodology employed, those that used quantitative methods were 51.9 %, only 6.6 % employed a mixed method of analysis; likewise, 21.7 % described projects, and 19.8 % used a qualitative methodology.
As for topics studied within the different subjects, we found that from greater to lesser frequency, the results found were: academic performance (f = 218; 22.45 %); learning, teaching and cognitive styles (f = 212; 21.83 %); interaction, communication (f = 165; 16.99 %); others (f = 1140; 14.42 %); degree of satisfaction (f = 57; 5.87 %); development of competencies (f = 55; 5.66 %); assessment (f = 48; 4.94 %); curricular integration into diverse areas (f = 41; 4.22 %); and attitudes/perception (f = 35; 3.60 %).
The comparison between the journals’ indexation with the type of study the article investigated were found to be related, with a Chi-square = 44.056 (p < 0.001). As the contingency table for the statistical analysis was 6x4, other association statistics were needed, as pointed by Rodríguez and Mora (2001), in order to re-enforce results. Values such as Phi (0.639, p < 0.001), Cramer’s V (0.369 and p < 0.001), with the Coefficient of contingency (0.538 and p < 0.001) were calculated, and they all showed that there was a relationship between both variables.
To understand the proportional reduction in the error, when using the values of the independent variable (journal indexing) to forecast the values of the dependent variable (type of study), the nominal statistic Goodman-Kruskal Tau was applied, obtaining a value of 0.821, p < 0.001 (dependent), and a value for the Coefficient of uncertainty (Cu) of 0.812 (p = 0.021). This means that knowing one variable reduces the error 80 % when forecasting the values of the other.
To unify and homogenize the criteria found in the articles selected, a comparison between the subject “Design of materials and/or digital objects used in e-learning activities”, with the type of method used in the article was performed, taking into account the existence of a relationship between both aspects. The Chi-square value was 58.714 (p < 0.001), with the Phi (0.744, p < 0.001), Cramer’s V (0.430, p < 0.001) and a Coefficient of contingency (0.597, p < 0.001), with these values indicating that there is a relationship among all the variables.
The Tau value was 0.831 (p < 0.001) for the dependent variable, with a Cu value of 0.702 (p < 0.001). This signifies that the knowledge of a variable reduces 70 % of the error when forecasting the values of the other.
Likewise, when addressing the subject “E-learning environments, distance learning platforms” with the type of method used in the article, we observed the existence of a relationship between both aspects, with a Chi-square of 42.590, p = 0.011, with values of Phi (0.634, p = 0.011), Cramer’s V (0.456, p = 0.011) and a Coefficient of contingency (0.535, p = 0.011), signifying that there was a relationship between both variables.
The Tau value was 0.731 (p = 0.005) for the dependent variable; however, the symmetric “Cu” obtained was not significant (p = 0.186). Therefore, knowledge of one variable did not reduced the error when forecasting the value of the other.
With respect to the subject “Tutorships and advice used in e-learning activities” with the comparison of the type of method used in the article, we observed that there was a relationship between both aspects, Chi-square = 47.774 (p = 0.003), with values of Phi (0.675, p = 0.003), Cramer’s V (0.423 and p = 0.003), with the “Coefficient of contingency (0.559, p = 0.003), meaning that there was a relationship between both variables.
The Tau values were 0.631 (p < 0.001) for the dependent variable; however, those (Fig. 13) obtained for the symmetric “Cu” values were not significant (p = 0.230), therefore the knowledge of one variable did not reduce the error when forecasting the values of the other.
The data showed that in the articles selected, for the subject “Tutorships and advice used in e-learning activities”, quantitative methodologies were mostly used in the sub-categories “academic performance” (20.0 %), “attitudes and perceptions” (1.8 %), “assessment” (5.5 %) and the “curricular integration into diverse areas” (3.6 %). On the other hand, those that had a more qualitative approach were observed in “learning styles” (47.6 %), as was already found with the rest of the subjects. The results related to “interaction and communication” (63.6 %) used description of projects, just as the development of competencies (13.6 %) and others (18.2 %) did. And lastly, the “degree of satisfaction” used mixed typologies (28.6 %).
However, when analyzing the subject “Didactic strategies and methodologies used in e-learning activities” with a comparison of the type of methodology used in the article, we observed that there was no relation between them, as the Chi-square values were 31.087, p = 0.151, which were not significant.
Likewise, when looking at the subject “Use of synchronous and asynchronous communication tools in e-learning activities” compared to the type of methodology utilized in the article, we observed that there was a relationship between both aspects, with a Chi-square equal to 62.806 (p < 0.001) with values of Phi (0.770, p < 0.001), Cramer’s V (0.444, p < 0.001) and a Coefficient of contingency (0.610, p < 0.001), which implied that both variables were related.
The Tau value obtained was 0.831 (p = 0.025) for the dependent variable; however, the values obtained for the symmetrical “Cu”, were not significant (p = 0.201). Therefore the knowledge of a variable did not reduce the error when forecasting the values of the other.
In relation to the subject “Use of assessment techniques and strategies in e-learning activities” compared to the type of methodology used in the article, we observed that a relationship between both aspects existed, with values of Chi-square = 41.716, p = 0.014, Phi =0.627, p = 0.014), Cramer’s V (0.401, p = 0.014) and Coefficient of contingency (0.572, p = 0.014), indicating that there was a relationship between both variables.
The Tau value was 0.712 (p = 0.008) for the dependent variable, and the symmetrical “Cu” was significant at p < 0.001 with a value of 0.902. Therefore the knowledge of one variable reduced the error 90 % when forecasting the value of the other variable.
The subject “Organizational and institutional aspects related to e-learning activities”, along with the type of methods used in the articles, were found to be related, with a Chi-square = 48.850 (p = 0.002), with values of Phi (0.679 and p = 0.002), Cramer’s V (0.424, p = 0.002) and Coefficient of contingency (0.569, p = 0.002), all of which point to a relationship between both variables.
The Tau value was 0.724 (p < 0.001) for the dependent variable, with a symmetric “Cu” which was significant, as shown by p = 0.038, and a value of 0.623. Therefore the knowledge of a variable reduces the error 62 % when forecasting the values of the other.
As for the comparison results of the subject “Collaborative and cooperative actions used in the e-learning activities” with type of methods used in the articles, we observed that there was a relationship between both aspects with Chi-square =46.327 (p = 0.004), values of Phi (0.661, p = 0.004), Cramer’s V (0.452, p = 0.004), and a Coefficient of contingency (0.543, p = 0.004), indicating that there was a relationship between both variables.
The Tau value reached was 0.653 (p < 0.001) for the dependent variable, with a symmetric “Cu” value of 0.836 (p = 0.048), a value that implied that the knowledge of one variable reduced 83 % of the error when forecasting the values of the other.
The data showed that on the articles selected under the subject “Collaborative and cooperative actions used in e-learning activities”, the the sub-categories of “attitudes and perceptions” (1.8 %) and “assessment” (3.6 %) were found using quantitative methods, while for the qualitative methods, we found it to be used in the sub-category “academic performance” (38.1 %) and “learning styles” (28.6 %). The degree of satisfaction (28.6 %) used mixed methodologies.
Lastly, the subject “Specific studies on accessibility, usability and their effects on e-learning activities” with the comparison of the type of methodology used in the articles, we observed the existence of a relationship between both aspects as shown by a Chi-square of 43.734 (p = 0.008), with values of Phi (0.645, p = 0.008), Cramer’s V (0.413, p = 0.008) and a Coefficient of contingency (0.542, p = 0.008), with a relationship therefore present (Fig. 17).
The Tau value was 0.639 (p = 0.001) for the dependent variable; however, the results obtained in the symmetric “Cu” were not significant (p = 0.127), therefore the knowledge of one variable did not reduce the error when forecasting the values of the other.
The first result that should be pointed out is that from 2011 to 2015, the number of publications linked to e-learning decreased when compared to the volume of work found in the meta-analysis from 2008 by Cabero. However, the presence of this subject has been steady in the different years analyzed, which suggests that it is still a field of interest in education research (Raghuram, 2013), where there are still a great number of problems to study (Conole & Oliver, 2007).
The study performed revealed, on the one hand, the tendency of the research to focus on the university context (Islam, Martin Beer, & Slack, 2015; Jirasak, 2014), possibly due to the university being the place where it is mostly used. On the other hand, there was not a single type of method that was predominantly used, although studies that used a quantitative methodology were numerous.
With respect to the subjects analyzed, we can highlight two groups, with the most analyzed being the articles that studied problems of: “Design of materials and/or digital objects used in e-learning activities”, “E-learning environments, distance-learning platforms”, “Didactic strategies and methodologies used in e-learning”, and “use of synchronous and asynchronous communication tools in e-learning activities”. The least subjects studied were related to “Tutorships and advice used in e-learning activities”, “Use of assessment techniques and strategies in e-learning activities”, “Organizational and institutional aspects related to e-learning activities”, “Collaborative and cooperative actions used in e-learning activities, and “Specific studies on accessibility, usability and their effects on e-learning activities”, in line with research by Borokhovski et al. (2011), Jirasak (2014), Anil and Batdi (2015), Bozkurt et al. (2015) or Islam et al. (2015). The results found coincide with those found by Cabero (2008), and point to the need for more research on deficient areas, even more so when some of them could explain some of the errors committed during their putting into practice, as suggested by Bates (2011).
At the same time that the areas investigated allowed us to point out that the proposal suggested by Conole and Oliver (2007), which stated that research in the area of e-learning could be performed with respect to its organizational, pedagogic and technical dimensions, at the individual level as well as in their interaction, without forgetting that these could be explained by sociocultural factors, could be a useful and interesting proposal for generating research proposals.
The present study has revealed the tendency to study the repercussions of e-learning on academic performance (Bagarukayo & Kalema, 2015). That said, it also points to the few works that study its effects on assessment (Noesgaard & Ørngreen, 2015; Sozcu, 2014), and its curricular integration into different areas. These studies are necessary, in agreement with the new directions of integration of the ICTs, and as pointed out by Conole and Oliver (2007), Kituyi and Tusubira (2013), Islam et al. (2015) or Mothibi (2015) in their research works.
Lastly, it is important to come to an agreement on a list of study subjects, to direct future research, in order to ease their significant integration to education and training (Koehler, Mishra, & Cain, 2013; Middle States Commission on Higher Education, 2011, Mothibi, 2015). But these studies should be routed to the areas of study found to be deficient in our study.
Dr. Julio Cabero-Almenara
PhD in Educative Technology of the Faculty of Education in University of Seville, Director of Secretariat of Audiovisual Resources and New Tecnologies in University of Seville. Director in chief the digital journal Pixel Bit.
Dr. Verónica Marín-Díaz
PhD in Media Literacy in the Faculty of Education in University of Cordoba, Director in chief of digital journal EDMETIC
Dr. Begoña E. Sampedro-Requena
Substitute professor in Media Literacy in Faculty of Education in University of Cordoba.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Andreu-Andrés, M.A., & García-Casas, M. (2015). Learning through the forum as college students. Pixel-Bit. Journal of Media and Education, 119–133. http://dx.doi.org/10.12795/pixelbit.2015.i47.08.
- Anil, Ö., & Batdi, V. (2015). A comparative meta-analysis of 5E and traditional approaches in Turkey. Journal of Education and Training Studies, 3(6), 212–219.View ArticleGoogle Scholar
- Bacca, J., Baldiris, S., Fabregat, R., Graf, S., & Kinshuk. (2014). Augmented reality trends in education: a systematic review of research and applications. Educational Technology & Society, 17(4), 133–149.Google Scholar
- Badia, A. (2015). Tendencias de la investigación en el aprendizaje favorecido por la tecnología. Infancia y Aprendizaje., 38(2), 253–278. http://dx.doi.org/10.1080/02103702.2015.1016744.MathSciNetView ArticleGoogle Scholar
- Bagarukayo, E., & Kalema, B. (2015). Evaluation of eLearning usage in South African Universities: a critical review. International Journal of Education and Development using Information and Communication Technology, 11(2), 168–183.Google Scholar
- Bates, T. (2011). E-learning in 2011: a retrospective. Online learning and distance education recourses, Retrieved from http://www.tonybates.ca/2011/12/13/e-learning-in-2011-a-retrospective/.Google Scholar
- Borokhovski, E., Bernard, R.M., Mills, E., Abrami, P. C., Wade, C. A., Tamim, R., Bethel, E., Lowerison, G., Pickup, D., & Surkes, M. A. (2011). An Extended Systematic Review of Canadian Policy Documents on e-Learning: What We're Doing and Not Doing. Canadian Journal of Learning and Technology, 37(3). Retrieved from http://cjlt.csj.ualberta.ca/index.php/cjlt/article/view/589/324.
- Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., Sezgin, S., Karadeniz, A., Sen-Ersoy, N., Goksel-Canbek, N., Dincer, G. D., Ari, S., & Aydin, C. H. (2015). Trends in distance education research: a content analysis of journals 2009–2013. International Review of Research in Open and Distributed Learning, 16(1), 330–363.Google Scholar
- Cabero, J. (2006). Bases pedagógicas del e-learning. Revista de Universidad y Sociedad del Conocimiento (RUSC), 3(1) UOC. Retrieved from http://www.uoc.edu/rusc/3/1/dt/esp//cabero.pdf.
- Cabero, J. (2008). Contributions to e-learning: from educational research. Seville: GID, University of Seville.Google Scholar
- Cabero, J. (2013). The virtual training in the new framework 2.0: e-learning 2.0. In J. I. Aguaded & J. Cabero (eds.), Technologies and means for education in the e-society (pp. 23-51). Madrid: Alianza.Google Scholar
- Castaño, C., & Cabero, J. (Eds.). (2013). Teaching and learning environments m-learning. Madrid: Síntesis.Google Scholar
- Conole, G., & Oliver, M. (2007). Contemporary perspective in e-learning research. Themes, methods and impact on practice. New York: Routledge.Google Scholar
- Gámiz, V., Montes, R., & Pérez, M. C. (2014). Self-assessment via a blended-learning strategy to improve performance in an accounting subject. RUSC. Universities and Knowledge Society Journal (RUSC), 11(2), 41–54. http://dx.doi.org/10.7238/rusc.v11i2.2055.Google Scholar
- Gros, B. (Ed.). (2011). Evolution and challenges of virtual education. Barcelona: UOC.Google Scholar
- Islam, N., Martin Beer, M., & Slack, F. (2015). E-learning challenges faced by academics in higher education: a literature review. Journal of Education and Training Studies, 3(5), 102–112.View ArticleGoogle Scholar
- Islas, C. (2015). The interaction in blearning as an enlaber of constructive learning environments: from the perspective of students. Pixel BIt, Journal of Media and Education, 47, 7–22. http://dx.doi.org/10.12795/pixelbit.2015.i47.01.Google Scholar
- Jirasak, S.-K. (2014). Developing of indicators of an e-learning benchmarking model for higher education institutions. Turkish Online Journal of Educational Technology - TOJET, 13(2), 35–43.Google Scholar
- Kinshuk, H. W. H., Huang, H. W., Sampson, D. G., & Chen, N. S. (2013). Trends in educational technology through the lens of the highly cited articles published in the journal of educational technology and society. Educational Technology & Society, 16(2), 3–20.Google Scholar
- Kituyi, G., & Tusubira, I. (2013). A framework for the integration of e-learning in higher education institutions in developing countries. International Journal of Education and Development using Information and Communication Technology, 9(2), 19–36.Google Scholar
- Koehler, M., Mishra, P., & Cain, W. (2013). What is technological pedagogical content knowledge (TPACK)? Journal of Education, 195(3), 13–19.Google Scholar
- Middle States Commission on Higher Education. (2011). The interregional guidelines for the evaluation of distance education programs (Online learning). Philadelphia: Middle States Commission on Higher Education.Google Scholar
- Mothibi, G. (2015). A meta-analysis of the relationship between e-learning and students' academic achievement in higher education. Journal of Education and Practice, 6(9), 6–9.Google Scholar
- Noesgaard, S. S., & Ørngreen, R. (2015). The effectiveness of e-learning: an explorative and integrative review of the definitions, methodologies and factors that promote e-learning effectiveness. Electronic Journal of e-Learning, 13(4), 278–290. 2015.Google Scholar
- Raghuram, P. (2013). Theorising the spaces of student migration. Population, Space and Place, 19(2), 138–154. http://dx.doi.org/10.1002/psp.1747.View ArticleGoogle Scholar
- Rodríguez, M. J., & Mora, R. (2001). Statistical information: cases and examples with SPSS. Alicante: Service Publications. University of Alicante.Google Scholar
- Roig, R., Mengual, S., & Rodríguez, C. (2013). Internet as an information, communication and learning. In Barroso J. & J. Cabero (Eds.), New digital scenarios (pp. 209-233). Madrid: Pirámide.Google Scholar
- Salinas, J., Darder, A., & De Benito, B. (2015). ICT in higher education: e-learning, b-learning and m- learning. In Cabero J. & J. Barros (Eds), New Challenges in educational technology (pp. 153-173). Madrid: Sintesis.
- Sangrá, A., González, M., & Anderson, L. (2015). Meta-analysis of research on MOOC in 2013-2014. Education XX1, 18(2), 21–49. doi:10.5944/eduXX1.13463.Google Scholar
- Sevillano, M. L., & Vázquez, E. (2015). Modelos de investigación en contextos ubicuos y móviles en Educación Superior. Madrid: McGraw-Hill.Google Scholar
- Sozcu, O. F. (2014). The relationships between cognitive style of field dependence and learner variables in e-learning instruction. Turkish Online Journal of Distance Education, 15(2), 117–144.View ArticleGoogle Scholar
- Thorne, K. (2012). Blended learning. London: Kogan Page.Google Scholar
- Torres, J. C., Infante, A., & Torres, P. V. (2015). Mobile learning: perspectives. RUSC. Universities and Knowledge Society Journal, 12(1), 38–49. http://dx.doi.org/10.7238/rusc.v12i1.1944.View ArticleGoogle Scholar