, computer self-efficacy plays an important role in an individual’s decision to use a computer and its resources and, therefore, in the disposition to learn other knowledge related to the use of this device.

Computer self-efficacy and students’ performance has also been object of study. For example, Compeau & Higgins (1995) assert that computer self-efficacy exercises significant effects on result expectations and performance in computational scenarios. Peinado & Ramírez (2014) found that students with a high level of computer self-efficacy exhibit better attitudes and academic achievements than their peers with a low or medium level of computer self-efficacy. According to the foregoing and to the findings of Moos & Azevedo (2009) , behavioral and psychological factors have a positive relationship with computer self-efficacy, which, in turn, positively influences students’ learning results in computational environments.

The third field of study, referred to as the field of interest, is self-efficacy in internet-based learning environments. Is defined as the judgements that students make about their ability to organize and execute activities related to the Internet in order to produce the desired results ( Eastin & LaRose, 2000 ). More recently, Cheng & Tsai (2011) redefine it as students’ expectations and confidence to participate and learn in Web-based environments. Diverse studies assert that the increase in levels of self-effi- cacy in internet-based learning environments can lead to the development of better cognitive and metacognitive abilites for information searches in a Web-based environment and, in turn, promote students’ learning ( Tzeng, 2009 ).

Research into self-efficacy in internet-based learning environments has been more prominent than in the foregoing postulates. In this regard, Tsai et al. (2011) asserts that IS has been linked to: learning processes ( Lu et al., 2007 ), learning results ( Chu & Tsai, 2009 ), anxiety ( Ekizoglu & Ozcinar, 2010 ), results expectations ( Bates & Khasawneh, 2007 ), and information searches ( Chiou & Wan, 2007 ), among others.

Based on the foregoing, it is possible to assert that in the research into the role that individuals’ self-efficacy plays when they interact with computational learning environments, this has been researched significantly in recent decades ( Tsai et al., 2011 ). A practical evolution of the concept can be observed, which has allowed researchers to effectively link it as a variable object of study, which, in turn, derived into the establishment of didactic strategies for the design of computational scenarios that take into account students’ motivational dimension ( López, Hederich, & Camargo, 2012 ). Consequently, the present study has gathered and studied 81 research articles published between 2006 and 2015, derived from the fields of study already described, and it performs a bibliometric analysis of the literature in question.

3. Methodology

With the purpose of obtaining a scientific production approach to the subject matter of self-efficacy and its association with learning in computational environments, a bibliometric study is conducted with descriptive type techniques.

4. Bibliometric Study Results

The initial results showed a total of 142 publications, which were reviewed to establish their relevance and identify duplicate entries. A total of 81 papers that systematically study the subject matter of self-efficacy and its association with learning in computational environments were confirmed. Of this set of articles, 53 were obtained from Science Direct, 19 from Scopus, and 9 from SciELO.

4.1. General Aspects

Regarding the origin of the scientific papers, it was found that they proceed from 19 countries. In terms of production by country (Figure 1), the countries that stand out for exhibiting greater research and productivity in the subject matter are Taiwan, with 23 papers; United States, with 17, and Turkey, with 11. These papers, as a whole, represent 63% of total papers. Asian countries contribute 49% of the research in the area of interest, followed by North America, with 21%; Europe, with 20%, and, finally, South America, with 10%.

In relation to the contexts within which the studies are conducted and participants’ levels of education, higher education stands out as the context within which the subject matter is studied the most. In Figure 2, a representation of the situation is shown. Indeed, of the 81 articles found, 48 (62%) refer to applications in universities. With much smaller shares, there are applications in secondary and middle education 12 (15%), basic primary education 8 (10%), distance education through Internet 7 (9%), and adult education 3 (4%) in informal contexts. It is noteworthy to mention that these values do not take into account 3 studies of a documentary nature.

Figure 1. Country of origin of research.

Figure 2. Levels of education.

Figure 3 shows the distribution of participants only in the context of higher education by discipline. As it can be observed, participants that come from higher education programs that educate educators represent 38% of the population in this context and, in turn, these represent 22% of the total of studies on the subject matter. There are also participants from nursing and medicine programs (11%), engineering (8%), and psychology (8%). It is noteworthy to mention that 18% of the participants come from diverse disciplines, such as social sciences, humanities, and architecture among others.

On the other hand, Figure 4 shows the types of computational environments that researchers use to develop the studies, which are the scenarios with which participants interact. E-learning environments are the most used: courses embedded in Learning Management System (LMS) platforms, mainly Moodle and Blackboard. These are followed by the Internet and to a lesser degree, hypermedia environments.

Figure 5 shows the domains of knowledge that frame the development of research activities, according to the evidence gathered. As it can be observed, the field where most publications converge is Computer Science, with 63% (digital literacy, Internet,

Figure 3. Distribution of higher education participants.

Figure 4. Types of computational environments.

Figure 5. Knowledge areas of study.

Table 1. Production per year in social sciences and humanities journals.

hardware, and software), followed by Science and Technology (15%), and to a lesser extent Mathematics (8%), Languages (5%) and Education and Psychology (4%).

4.2. Bibliographical Aspects

Table 1 shows the number of publications found in this review by year of publication. The year 2010 is the period with the most publications on the subject matter with a total of 13 articles, followed by the years 2011, 2013, and 2015, each one with 10 articles. In contrast, the years 2007 and 2009 have the lowest number of publications: 5 articles per year. An important increase can be observed in the number of publications starting from the year 2010 and up to the year 2015, this period represents 70% of total papers. An increase can be observed in the number of publications in the last five years as a result of the heightened academic interest in the subject matter of self-efficacy associated with the use of a computer.

Articles on the subject matter located in this study have been published in a total of 31 refereed scientific journals. Table 2 shows the list of the journals’ names, the amount

Table 2. Included journals that have published on self-efficacy associated with the use of a computer in learning situations.

of articles published on the subject matter during the period of observation, indexing type according to the 2015 Colombian National System of Indexing and Official Approval of Specialized Journals of Science, Technology, and Innovation (PUBLINDEX) and the impact factor. Among the journals that stand out are two from the United Kingdom, Computers & Education, with 27 publications on the subject matter, and Computers in Human Behavior, with 11 publications. These journals are the basis of the high productivity in the field of the present study. The rest of the journals have between 1 and 3 publications, with the exception of Procedia-Social and Behavioral Sci- ences, which has 7 publications.

Table 3 shows the relationship between the number of articles and the number of citations per year. A total of 3317 citations were found during the period of observation, of which 2744 citations correspond to the set of publications yielded by the Science Direct database, 532 correspond to Scopus, and 9 to SciELO. The year 2006 is the period with the largest number of citations, with 744 citations in 7 articles; followed by the year 2010, with 587 citations, and the year 2008, with 554 citations. The year 2015 had the lowest number of citations.

The number of citations received by each article was obtained through Google Scholar. The range is between 0 and 175 citations. The most cited articles are “The relationship of e-Learnerʼs self-regulatory efficacy and perception of e-Learning environmental quality” by Lee and Lee (2008) , with 175 citations; “Computer use and the gender gap: The issue of access, use, motivation, and performance” by Imhof, Vollmeyer, & Beierlein (2007), with 169 citations; and “University Students’ Internet Attitudes and

Table 3. Number of citations per year.

Internet Self-Efficacy: A Study at Three Universities in Taiwan” by Wu & Tsai (2006) , with 159 citations, during the 2006-2015 period.

4.3. Author Characterization

In the 81 studies analyzed in this review, 194 authors were identified. Table 4 shows the relationship between widely published authors in the subject matter and the number of articles in which they have participated, whether in an author or coauthor condition. Sixteen authors have participated in two publications; two authors participated in three publications; one author participated in four publications; and one author participated in eleven publications. As can be observed in Table 4, Dr. Chin-Chung Tsai, professor at the Graduate Institute of Digital Learning and Education of the National Taiwan University of Science and Technology, is the most cited author, with 11 publications on the subject matter.

Similarly, Table 5 shows the coauthorship index or signatories/article per period. This Bibliometric indicator refers to the quotient between the total number of signatories (authors) and the number of published articles per year. As can be observed, the authors’ mean oscillates between 2.0 authors, corresponding to the years 2009 and 2012, and 2.8 authors, in the years 2007 and 2013.

Research Methods

With respect to research methods, Table 6 shows those identified. Correlational type studies are predominant, with a frequency of 51 studies (63%), followed by studies of an experimental nature, with a frequency of 14 studies (17%), and quasi-experimental nature, with 7 studies (9%); all of these with a quantitive approach. Descriptive and documentary type studies are used to a lesser extent, each one with three studies, and the exploratory type studies and case study, each one with one study. These results are similar to those reported by Tsai et al., (2011) , who in a literature review study during the period between 1999 and 2009 found, in the set of studies analyzed (46 in total), that research with a quantitive and correlational approach prevails.

Table 4. Authors of publications on the subject matter.

Table 5. Coauthorship index per year.

Table 6. Research methods employed.

Table 7. Instruments with the highest frequency of use in the studies.

4.4. Use of Instruments

In regard to the instruments used in the studies, a significant and important number of scales and questionnaires were found, which are used and elaborated by the authors themselves; in some cases, as information gathering sources. A total of 62 questionnaires to measure self-efficacy were counted, which are characterized for being self- reporting, Likert-type scale, whose purpose is to evaluate participants’ perception with respect to their self-efficacy in different domains. Among the latter, those mainly associated with self-efficacy with the use of a computer, with the use of Internet and Web or online learning environments, and academic self-efficacy, were identified.

Table 7 shows the most frequently used instruments in the studies considered. Those that stand out because of their use in research are: the Self-efficacy subscale of the MSLQ ( Pintrich, Smith, Garcia, & McKeachie, 1991 ), which is used in 14 studies, followed by the Internet Self-efficacy Scale (ISES) and the Web-based Learning Self-Effi- cacy Scale (WBLSES) ( Tsai & Tsai, 2003 ), used in 9 studies, and the Computer Self-ef- ficacy Scale (CSES) ( Compeau & Higgins, 1995 ), used in 4 studies.

5. Discussion and Conclusion

The bibliometric study of the published articles on the role of individuals’ self-efficacy when they interact in computational environments in refereed journals and extracted from the Science Direct, Scopus, and SciELO databases, period 2006-2015, allows evidencing some charactersitics of the research developed globally in this field: its diverse position, methodologically plural, supported in the design and use of different instruments to evaluate self-efficacy, and strongly based on the theoretical constructs elaborated by Bandura (1986, 1997) . In addition to this study’s quantitative results already presented, it was possible to discern qualitative trends, which need to be mentioned in this section.

In the first place, the interest of the subject matter has aroused in other geographical lattitudes is noteworthy. Tsai, Chuang, Liang & Tsai (2011) , in the results of their literature review study, discussed the lack of papers originating in Europe; additionally, no result is reported as proceeding from South America. In contrast, the findings indicate that these data have changed during the last five years: even though Asian countries continue leading the scientific production, the progress made by European and South American countries, which together reported 30% of total papers analyzed in the pre- sent study, is noticeable.

In the second place, a discernable trend is the development of studies with higher education students. Of the 81 analyzed articles, 48 studies were identified as having been developed within universities. The samples’ demographic characteristics reveal that university students turn out to be the preferred subjects for these studies. Additionally, it was possible to recognize a favorable trend, in recent years, to mobilize this type of papers towards initial levels of education, such as basic and middle education, which accounted for 20 studies, of which eight were conducted in basic primary education ( Aesaert & Van Braak, 2014 ; Barak, Ashkar, & Dori, 2011 ; Kao & Tsai, 2009 ; Kao, Wu, & Tsai, 2011 ; López, Sanabria, & Sanabria, 2014 ; López & Triana, 2013 ; Meluso, Zheng, Spires, & Lester, 2012 ; Shank & Cotten, 2014 ).

In this regard, a growing interest to analyze the subject matter in students enrolled in Education or Bachelor’s programs in higher education was observed. An important group of publications refers to papers conducted in this context. With respect to the latter, research was found that inquires into the beliefs of self-efficacy of pre-service teachers associated with: the use of a computer ( Bustos, 2012 ; Ekizoglu & Ozcinar, 2010 ), the use of Internet ( Ekizoglu & Ozcinar, 2010 ; Gürol & Akti, 2010 ; Kaya & Durmus, 2010 ; Liang & Tsai, 2008 ; Sirakaya, Başarmak, & Baltaci, 2015 ), learning in Web environments ( Kao & Tsai, 2009 ; Kao et al., 2011 ; Liang & Tsai, 2008 ), and ICT teaching ( Bustos, 2012 ), for which a specific instrument was used ( Wang, 2004 ).

In the third place, the finding of a great diversity of instruments (questionnaires- scales) used to measure participants’ self-efficacy. As shown in Table 7, researchers tend to use reliable and valid instruments in different contexts. Noteworthy, among these, are the scales elaborated by Compeau & Higgins, 1995 ; Pintrich et al., 1991 ; and Tsai & Tsai, 2003 . The former was designed to measure self-efficacy in the use of a computer, the second to measure academic self-efficacy, and the latter to measure self-efficacy in the use of Internet and learning Web environments. Results evidenced that these scales are translated and adjusted to contexts where they are applicable and validated for their subsequent use.

The fourth aspect worth mentioning refers to the research methods. The results evidenced a large part of the studies to be of a correlational nature and that use self-re- porting surveys and questionnaires as a predominant source of information gathering. These methods are oriented towards identifying what the behavior or what the degree of relationship of the self-efficacy variable is with respect to other educational variables in the same population group. The manner to do this, generally, is through descriptive and correlational studies, which are supported on the application of different statistical tests to approve or reject the proposed hypotheses.

Following this type of studies are the experimental and quasi-experimental studies of a quantitative approach that, in contrast to the foregoing, are characterized by the creation of population groups, whether randomly or previously established, as the case may be. The objective of this type of studies, in most cases, is to verify the effect of the implementation of pedagogical strategies and/or computational scaffolding on other variables involved in educational processes ( López, Sanabria et al., 2014 ; López & Triana, 2013 ; López & Valencia, 2012 ; Moos & Azevedo, 2008 ; Papastergiou, Gerodimos, & Antoniou, 2011 ; Plant, Baylor, Doerr, & Rosenberg-Kima, 2009 ; Schuyten & Dekeyser, 2007 ; Sins, van Joolingen, Savelsbergh, & van Hout-Wolters, 2008 ).

Finally, another finding worth emphasizing refers to the type of digital technologies that are used in the studies. As presented in Figure 4, e-learning technologies are the basis for the research and are used to validate an important number of papers related to the subject matter. It is important to highlight the implementation of LMS platforms like Moodle and Blackboard, which currently drive most of the virtual education and distance education programs. The research conducted by Chen & Tseng, (2012) ; Lee & Lee (2008) , and Tang & Tseng (2013) stand out, whose findings indicate the importance of individuals’ self-efficacy for successful performance in these educational environments, which in turn, influences other factors that enable better attitudes in students’ learning. Hence, self-efficacy constitutes itself as a current topic of interest, which e-learning designers and educators need to take into account in order to put into practice the contributions derived from the research.

Thus, in this spectrum of publications, it is feasible to establish which studies allow gathering sufficient evidence to confront the different theoretical approaches and that may be put into practice, in such a way that teachers and researchers can depend on empirical data that promote the efficient design and development of scenarios and computational scaffolding that contribute to students’ cognitive, behavioral, and motivational components and, consequently, to the achievement of better academic performances.

6. Limitations and Forecasts

Open to the possibility of having the present study’s findings confirmed when extending the analysis towards a broader framework that covers papers published in other international databases, such as Web of Science, EBSCO host Research Data, or ERIC, among others. Additionally, it is important to keep in mind that this study focused exclusively on journal publications in the Social Sciences and Humanities areas.


*This article represents a contribution to the first author’s doctoral thesis.

1Accessed on 03/16/2016 retreieved from http://www.scimagojr.com/index.php

2SCImago Journal Ranking (SJR indicator) is a measurements of the scientific influence of the specialized journals.

Cite this paper
Valencia-Vallejo, N. , López-Vargas, O. and Sanabria-Rodríguez, L. (2016) Self-Efficacy in Computer-Based Learning Environments: A Bibliometric Analysis. Psychology, 7, 1839-1857. doi: 10.4236/psych.2016.714170.

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