Abstract:Social networks as e-learning devices in higher education institutions in the context of Covid-19 in Burkina Faso 

Bapindié Ouattara§,

Benjamin Sia,

Dimkêeg Sompassaté Parfait Kaboré


Félix Compaoré

Summary: The health crisis linked to covid-19 led to the suspension of teaching activities in higher education establishments. In Burkina Faso, some higher education establishments used the possibilities offered by ICTs to ensure the continuity of teaching activities. The study concerned higher education students who had experimented with these networking tools as an e-learning device in the context of covid-19. Based on the UTAUT model, it aims to analyze the factors determining the intention to adopt networking tools as an e-learning device. The results indicate that "social influence" and "expectation of use" exert a positive and significant influence on the intention to accept and use social networks as a distance learning device. Conversely, the central UTAUT variables "performance expectation", "effort expectation" and "facilitating conditions" have no significant influence on the intention to accept social networks.

Keywords: Virtual social networks, intention to use, social influence, e-learning, resilience, covid-19

Abstract: The health crisis related to covid-19 has led to the suspension of pedagogical activities in higher education institutions. In Burkina Faso, some higher education institutions have used the possibilities offered by ICT to ensure the continuity of pedagogical activities. The study focused on higher education students who have experimented these networking tools as an e-learning device in the context of covid-19. Based on the UTAUT model, it aims to analyze the factors determining the intention to adopt networking tools as an e-learning device. The results indicate that "social influence" and "expectation of use" have a positive and significant influence on the intention to accept and use social networks as a distance learning device. In contrast, the central variables of the UTAUT, "performance expectation", "effort expectation" and "facilitating conditions" have no significant influence on the acceptance intention of social networks.

Keywords: Virtual Social Networks, Intention to Use, Social Influence, E-Learning, Resilience, Covid-19


Enthusiasm for information and communication technologies for education has been revived with covid-19 to ensure the continuity of pedagogical activities in universities and higher education institutions. According to the 2018-2019 tableau de bord de l'enseignement supérieur (MESRSI, Report, February 2020), some 113 Institutions d'Enseignement Supérieur among which 13 public establishments with more than 132,569 students including 21.0% from the private sector, are affected by the effects of the pandemic. At the time of the closure of classes in March 2020, many voices within the educational community were raised to demand the switch to distance learning. This request presented decision-makers and managers of the education system with a dilemma concerning the choice of computer technologies. Indeed, some schools have resorted to virtual social networks (WhatsApp, Facebook, Instagram, Telegram, etc.) as an online teaching and learning device. Achieving the objectives pursued through such devices requires mastery of various factors, including those determining their acceptance by students. So, how have students welcomed these social networking tools? What are the potential factors behind their acceptance? In other words, what are the determinants of student acceptance of social networks as distance learning devices in the context of the covid-19 pandemic? By adapting the UTAUT model to the context of the present study, then, the objective is to identify the factors likely to influence the intention to use social networks by learners in higher education institutions in Burkina Faso. Specifically, we will examine the link between this intention to use and performance expectations, effort requirements, facilitating conditions, social influence and usage expectations.

1. Theoretical and conceptual framework

1.1. The contribution or potential of social networks

Some studies of social networks in education use the services and functionalities (communicating, collaborating, sharing content) of these media to deduce possible contributions in the field of teaching and training. These are generally empirical studies, according to (Wenger 2). This author notes that social networks are important learning sites for people with a common interest who are willing to collaborate. Other research has focused on the use of these media by educational players. These various studies have revealed two categories of use as far as learners are concerned.

In the first place, use for distraction is predominant in the student environment. Indeed, several studies show that learners use social networks to maintain contact, spend time with friends, react to or appreciate the contributions of members of their networks (Hart 37; Thivierge). These authors, who examined the case of Facebook, one of the networks most used by young people worldwide, reveal that students' use of social networks is focused on aspects of sociability such as maintaining social ties, sharing information and so on. The study by (Koutou), whose target audience was schoolchildren in the African context, notably in Côte d'Ivoire, came to the same conclusion. Learners prefer to use social networks to download music, share photos, make contacts and so on. These uses and practices are influenced by the representations of young people, who see these media as tools in the fight for transparency (Damome et al.29). This predominance of non-academic uses affects learners' ability to respect the rules of written production in French (Dia et al.).

Secondly, the results of studies carried out by (Mian; Beauné; Dakouré) highlight the use of social networks for educational purposes. These studies present interesting and diverse results concerning the educational uses of Facebook. They show that, overall, students react positively to the idea of developing such uses, or a posteriori, after having experimented with such uses. "Pupils and students use Facebook for academic purposes, as well as for entertainment: downloading sounds and images, chatting, playing online games, messaging, visiting different news sites, etc." In the same vein, (Ch.), a study of the use of Facebook for educational purposes, shows that students are more inclined to use it for entertainment purposes than for academic purposes. In the same vein, (Chomienne and Lehmans 2) go further, looking at the effectiveness of digital social networks in building a knowledge community and in helping students appropriate knowledge in a process of collective construction through information seeking, writing and sharing. (Alava and Message-Chazel 55) have focused on the digital skills required by "community practices" and their impact on the learning strategies of ODL learners. Their study shows that learners' use of social networks not only strengthens their autonomy, but also encourages them to adhere to team learning practices.

Beyond the potential for learning and the conditions of effectiveness, it is also important to question the relationship of these new media to the learning of African university students in the context of covid-19 and more specifically in Burkina Faso. Indeed, the beliefs of (McLoughlin, Wang and Beasley) and environmental factors such as cultural profile and adoption of digital tools (Collin and Karsenti 206) can affect the successful implementation of an elearning device.

1.2 Theoretical model

The intention to use a technology, defined as a decision made by the individual to interact with a technology, has its origins in the theories of reasoned action (Fishbein & Ajzen), planned behaviour (Ajzen 316) and the Social Cognitive Theory of (Wood and Bandura 380). Among the models that serve as a basis for theories of technology acceptance are the TAM (Technology Acceptance Model) by (Davis 322) and UTAUT (Unified Theory of Acceptance and Use of Technology) proposed by (Venkatesh et al.). The TAM emphasizes perceived usefulness (performance expectancy) and ease of use as variables determining technology acceptance. The UTAUT of (Venkatesh et al. 447), synthesizes the previous models by retaining the most significant variables: expected performance, expected effort, social influence and facilitating conditions. (Karahanna and Straub 200) have also shown that (Davis 1986)'s technology acceptance model can be enriched with additional factors such as social presence and technical support. We have drawn on the research findings of (Karahanna and Straub197, 199), which reveal the variables performance expectancy, effort expectancy, social influence, facilitating conditions and usage expectations as factors influencing the acceptance of a technology in learning through a measurement scale.

Our research model is as follows:

Figure 1: Search model

1.3 Research hypotheses

Based on our research model, we formulate the general hypothesis that students' use of social networks as an e-learning device is a function of intention-to-use (ITU).

In terms of specific assumptions, we retain :

  • Hypothesis 1 (H1): Performance expectancy has a significant effect on learners' intention to use social networks. Performance expectancy (PDA) is the degree to which a person believes that using social networks can help him or her achieve performance gains in their studies (Venkatesh et al. 447).
  • Hypothesis 2 (H2): Students' expectation of effort to use social networks influences their intention to use them. Expectation of effort (EoE) represents the degree of ease that is associated with students' use of social networks in their learning activities (Venkatesh et al. 450).
  • Hypothesis 3 (H3): Social influence has a significant effect on learners' intention to use social networks for learning. Social influence (INS): this is an individual's perception of the influence of certain important people on his or her intention to accept the use of social networks for learning activities (Benali et al.). The learner's decision to accept this technology for learning can then be influenced either by fellow students, teachers, sponsors or even the school administration.
  • Hypothesis 4 (H4): Facilitating conditions are linked to intention to use. Facilitating conditions (FDC) represent the availability of temporal, technical and financial resources needed to support the use of social networks. In the context of our research, students would be more inclined to accept social networks for educational purposes only if they felt the institutional, infrastructural and financial environment was favorable.
  • Hypothesis 5 (H5): learners' expectations of social network use influence their intention to use. Usage expectations (UX) are defined as the perceived benefits of using social networks for learning. It refers to the perception that social networks enable learners to achieve their learning goals more quickly and improve their academic results.

2. Methodology

Convenience sampling is the technique adopted for data collection. It is a non-probabilistic method that makes it possible to be satisfied with people who volunteer to take part in the survey. Pre-testing with the study's target audience revealed that public higher education establishments in the context of covid-19 have not experimented with the use of social networks for pedagogical continuity. In fact, the Ministry of Higher Education, after taking stock of teaching at each university, opted instead for an online platform. An access portal was created with a link for each university center. Lecturers were asked to upload modules scheduled but not yet delivered in PDF format onto the platform dedicated to their university, so that students could access them.

So, the study concerned students at universities or private colleges having opted for social networks as an e-learning device in the context of covid-19. As a prelude to the launch of the questionnaire, an e-mail sent to the institution's founders identified the private schools or institutions that had genuinely experimented with social networks for the continuity of academic and pedagogical activities. Around 125 students from institutes such as Centre de Recherche Panafricain en Management pour le Développement (CERPAMAD), Institut Internationale de Management (IAM), Ecole Supérieure Polytechnique de la Jeunesse (SUP-JEUNESSE), Université Libre du Burkina (ULB) actually responded to the questionnaire.

Table 1. Respondent characteristics

VariablesTerms and conditionsWorkforcePercentage
Age rangeunder 2032,4
20-25 years7761,6
26-30 years old2217,6
31-35 years1411,2
35 and over97,2
School or universityCERPAMAD2822,4
Study levelBachelor's degree 1st year21,6
Licence 2nd year4032,0
Bachelor's degree 3rd year4132,8
Master 11814,4
Master 22419,2
Law and political science2923,2
Economics & Management6753,6

For data collection, a 23-item, 7-modality Likert scale questionnaire (ranging from "(1 = total disagreement, 2 = disagreement, 3 = slight disagreement, 4 = neutral (neither agree nor disagree), 5 = slight agreement, 6 = agreement, 7 = total agreement)" was developed, based on the UTAUT model enriched by (Karahanna and Straub198). It includes headings ranging from identification of the respondent to his/her intention to use social networks, including his/her use of social networks, performance expectations, effort expectations, social influence and conditions favoring learning during this period of covid-19. In parallel with the online questionnaire, the paper version was administered to selected students at the target institutions.

For data analysis, the first step was to analyze the reliability of our questionnaire items using Cronbach's alpha test. In the second stage, to test the hypothetical relationships of our research model, we used ANOVA. The third step was devoted to a linear regression test to represent the linear relationship between our dependent variable, namely intention to use, and the independent variables such as performance expectations of the social networks used, effort expectations, conditions facilitating its use, social influence and usage expectations. The various tests were carried out using IBM SPSS 26 data processing software.

3. Results and discussion  

In this section, we check measurement reliability and present our results. This is also the place for us to show the discrepancies and concordances of our work with other studies.

Checking the internal consistency of questionnaire items

To check the internal consistency of the questions formulated from the UTAU model, Cronbach's alpha test was used. The results presented in Table 2 show an index of 0.834. This is above the minimum threshold of 0.70.

Table 2. Reliability of measurement scales

Reliability statistics
Cronbach's AlphaCronbach's Alpha based on standardized itemsNumber of elements 

So, the scale used to measure acceptance factors is reliable and therefore a predictor of the behavioral variables we wish to verify in our study.

We also checked the use of social networks for learning before covid-19

Table 3. Use of social networks for training prior to the covid-19 health crisis


Table 3 shows that before the health crisis caused by covid-19, 64.8% of respondents were already using social networks, compared with 35.2%. The social networks cited were: WhatsApp, Facebook, Instagram, YouTube, Snapchat, Google Classroom.

Checking the relationship between the dependent variable and the independent variables

To verify this relationship, we used the regression test

Table 4. Results of verification of the link between intention to use and expectation of performance (ATP), expectation of effort (ATE), social influence (INS), facilitating conditions (CDF), expectations of use (ATU)

L’ANOVA révèle une valeur de F de 71,82 et p < 0,005. Cela signifie qu’il y a probablement une relation statistiquement significative entre la variable dépendante INU (Intention d’usage) et les variables indépendantes ATP (attente de performance), ATE (attente d’effort), INS (influence sociale), CDF (conditions facilitantes), ATU (attentes d’usage).

Table 5. Summary of models

ModelRR-twoR-two adjustedStandard error of the estimate
a. Predictors: (Constant), ATU, ATE, CDF, INS, ATP

In Table 5 above, the value of the correlation coefficient is 0.88. This value reveals that the data fit the model very well. Indeed, if we consider the R-two value (0.77), this indicates the proportion of variability in the dependent variable (intention to use) explained by the regression model. We can therefore say that factors such as performance expectancy, effort expectancy, social influence, facilitating conditions and usage expectations can explain nearly 77% of the variation in usage intention.

Table 6 below shows the Beta values and their significance.

Table 6. Coefficients

A la lecture de ce tableau, il ressort que les résultats du test de régression pour les variables influence sociale (INS) et attente d’usage (ATU), les valeurs p= sont respectivement de 0,027 < 0.05 et 0,000 également < 0.05. Les hypothèses H3 et H5 sont donc confirmées. Nos résultats révèlent que les variables « influence sociale » et “attentes d’usage” ont un effet significatif sur l’intention d’usage des réseaux sociaux pour l’apprentissage en ligne pendant la covid-19.

On the other hand, for the variables performance expectancy (PTA), effort expectancy (ETA) and facilitating conditions (FCE), whose p-values respectively 0.5540, 0.494 and 0.134 are all greater than 0.05, the test is not significant. This indicates that our hypotheses H1, H2 and H4 are not confirmed. Therefore, the variables "performance expectation", "effort expectation" and "facilitating conditions" have no significant influence on learners' intention to use social networks as an e-learning device during the covid-19 period.

Analysis of the determinants of students' acceptance of social networks as an e-learning device indicates the existence of a relationship between social influence and intention to use. This result is in line with those of (Benali et al.) and (Bere 88), but contrasts with that of (Kouakou 194), who demonstrated that no social influence has an impact on the use of social networks. Given the youth of the majority of respondents (around 64% under 25 years of age), we can deduce that their entourage (peers, parents, teachers, etc.) may have influenced their intention to use social networks. Indeed, as Benali et al. have pointed out, in terms of social influence, the influence of teachers and those close to them (parents, friends and classmates) is particularly noteworthy.

Our results also confirm that expectations of use of social networks as a learning device positively influence learners' intention to use. This result converges with the TAM and UTAUT, which consider expectancy of use as one of the variables significantly related to intention to use a technology. Indeed, task relevance (Kouakou 66) and the expected positive effects of whatsapp use on intention to use for learning were highlighted by (Adjanohoun and Agbanglanon 208). This means that the usage intention of students in our sample to use social networks as a learning device is determined by perceived benefits. 

On the other hand, our research reveals that there is no significant influence of the variables performance expectancy, effort expectancy, facilitating conditions on the intention to use social networks for learning. This contrasts with the studies of (Ayadi and Kamoun 9) and (Ben Romdhane 52, 53) for the variable performance expectation and the UTAUT of (Venkatesh et al.) for all these variables. With regard to the facilitating conditions variable, one of the essential variables influencing the acceptance of a technology according to this UTAUT theory, the respondents, most of whom are young (61.60% between 20 and 25 years of age), belong to the "digital native" generation who are familiar with virtual social networks. Indeed, the majority of respondents (64.8%) were already using social networks before covid-19. The facilitating conditions variable therefore had no effect on their intention to use these technologies in their learning.


The aim of the present study was to identify the determinants of acceptance of social networks as distance learning devices for pedagogical continuity in this context of the covid-19 pandemic. Using the UTAUT model from (Venkatesh et al. 2003), this research was carried out with 125 students from private higher education establishments in Burkina Faso. The results reveal that the variables "social influence" and "usage expectations" are factors that determine the intention to use social networks for learning. In terms of contributing to the design of e-learning devices for higher education institutions, our research highlighted two determining factors to be taken into account. For the acceptance of e-learning through social networks, these institutions need to rely on the influence of the students' entourage and the perceived benefits of using these tools for learning. However, the study does have some limitations in terms of its external validity. Indeed, it did not take into account students enrolled in public higher education institutions and socio-demographic characteristics. These limitations open up new avenues of research. The first avenue is to extend the scope of the study to public higher education institutions, and to take sociodemographic variables into account. Such research would make it possible to identify the variation in determinants according to the status of the institution and the socio-demographic characteristics of the students. The second avenue is to take into account the teaching target, which plays a decisive role in the implementation of e-learning systems.

Works quoted           

Adjanohoun, Jonas, and Sylvain Agbanglanon. "Social networks for learning: a structural model based on the unified theory of technology acceptance and use." PUN 2020 Pédagogie universitaire numérique : Quelles perspectives à l'ère des usages multiformes des réseaux sociaux pour apprendre ?2020.

Ajzen, Icek. "The theory of planned behavior." Organizational behavior and human decision processes 50.2 (1991): 179-211

Alava, Séraphin, and Eléonore Message-Chazel. "Les pratiques en communautique au cœur des apprentissages en ligne". Questions Vives, vol. 7,no 14, 2010, p. 55-70.

Ayadi, F., and F. Kamoun. "Determinants of e-learning system use by students." Proceedings of the 14th AIM Colloquium. 2009.

Beauné, Aurélie. "Using social networks for foreign language learning: the case of Facebook." Adjectif 15.1 (2012).

Benali, Mourad, Toufik Azzimani, and Mehdi Kaddouri. "Mobile phone-mediated learning: what adoption intentions among young Moroccans." Adjectif. net (2018).

Bere, Aaron. "Exploring determinants for mobile learning user acceptance and use: An application of UTAUT." 2014 11th International Conference on Information Technology: New Generations. IEEE, 2014, pp. 84-90.

Brangier, E., and Hammes, S., (2006). Development and validation of a questionnaire to measure the acceptance of information and communication technologies based on the human-technology-organization symbiosis model. In Brangier, E., Kolski, C., Ruault, J-R. (Eds). L'humain comme acteur de performance des systèmes complexes. Proceedings of Ergo'IA 2006. Estia Innovation éditeur, 71-78

Brangier, Eric, and Sonia Hammes. "Elaboration and validation of a questionnaire for measuring the acceptance of information and communication technologies based on the human-technology-organization symbiosis model". Brangier, E., Kolski, C., Ruault, J-R. (Eds), 2006, p. 71-78. ResearchGate,

Caneva, Christiane. "Factors of technology adoption or rejection among teacher educators: the case of the Costa Rican university." journal internationale des technologies en pédagogie universitaire/International Journal of Technologies in Higher Education 16.1 (2019): 15-35.

Chomienne, Elise, and Anne Lehmans. "Social networks and collaborative learning at university." International Colloquium University in the Digital Age 2012. 2012.

Collin, Simon, and Thierry Karsenti. "Usages des technologies en éducation: analyse des enjeux socioculturels." Éducation et francophonie 41.1 (2013): 192-210.

Dakouré, Evariste. "Promotion of multimedia devices in Burkina Faso: Practices, discourses and actor strategies." Adjective (2012 ).

Damome, Étienne, Aminata Ouédraogo, and Élodie Alimata Tapsoba. "Vision and enchanted uses of digital social networks in Burkina Faso." Communication. Information médias théories pratiques 37.1 (2020).

Davis, Fred D. " Perceived usefulness, perceived ease of use, and user acceptance of information technology." MIS quarterly (1989): 319-340

Dia, Mamadou, Oumar Sk Dembele, and Fatoumata Bintou Sylla. "Impact of social networks on student writing in Mali." 2020, p. 16.

Fishebein, M., and I. Ajzen. "Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research Readin." (1975).

Hart, Michael J. A study on the motives of high school and undergraduate college students for using the social network site Facebook.Doctoral dissertation, Liberty University, USA, 2010.

Karahanna, Elena, Detmar W. Straub, and Norman L. Chervany. "Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs." MIS quarterly (1999): 183-213.

Kouakou, Kouassi Sylvestre. "Les déterminants de l'adoption de l'apprentissage mobile par les étudiantes de l'université Cheikh Anta Diop de Dakar." http://www.frantice.net, IFIC, Université de Limoges, with the support of AUF, January 1, 2019.

Kouakou, Kouassi Sylvestre. "Determinants of the adoption of digital social networks in professional situations: an empirical study within Ivorian university libraries". Frantice. net, 2014.

Koutou, N'Guessan Claude. "Usage des réseaux sociaux par les apprenants des établissements secondaires privés en Côte d'Ivoire." Panaf Edu 2.2 (2010).

Lassoued, Tarek, and Mahrane Hofaidhllaoui. "Determinants of e-learning acceptance: an empirical study at La Poste." Management Avenir 2 (2013): 139-156.

Mbengue, Moustapha, Djibril Diakhate, and Mohamed Diop. "Formation Ouverte et à Distance (FOAD) et stratégie numérique de continuité pédagogique en contexte COVID-19: Retour d'expériences." Annals of the University of Craiova. Filosofie (2020), p. 16.

McLoughlin, James A., Lih-Ching Chen Wang, and William A. Beasley. "Transforming the college through technology: A change of culture." Innovative Higher Education 33.2 (2008): 99-109.

Mian, Bi Sehi Antoine. "Usages de Facebook pour l'apprentissage par des étudiants de l'Institut Universitaire d'Abidjan (IUA)." Adjective, (2012).

Ouédraogo, Boukary. "Les déterminants de l'intégration pédagogique des Technologies de l'Information et de la Communication (TIC) par les enseignants à l'Université de Ouagadougou (Burkina Faso)." (PhD Thesis, Ed.), Université de Montréal, (2011).

Thivierge, Josée. Young people, ICT and new media: an exploratory study at Cégep de Jonquière. Cégep de Jonquière, 2011.

Tomé, Mario. "Internet networks and social media for learning foreign language pronunciation." International Journal of E-Learning & Distance Education 25.2 (2011): 1.

Venkatesh, Viswanath, et al. "User acceptance of information technology: Toward a unified view. User acceptance of information technology: Toward a unified view." MIS quarterly (2003): 425-478.

Wenger, Etienne. "Communities of practice: Learning as a social system." Systems thinker 9.5 (1998): 2-3.

Wood, Robert, and Albert Bandura. "Social cognitive theory of organizational management." Academy of management Review 14.3 (1989): 361-384.

How to cite this article:

MLA: Ouattara, Bapindié, Sia Benjamin, Kaboré Dimkêeg Sompassaté Parfait, Félix Compaoré. "Social networks as e-learning devices in higher education institutions in the context of Covid-19 in Burkina Faso". Uirtus 2.1 (April 2022): 70-85.

§ Université Thomas Sankara- Burkina Faso/ [email protected]