The research aimed to establish the relationship between factors such as self-esteem, emotional states, and computer anxiety mediated by self-confidence in creativity in learning in post-pandemic university students. The methodology used for the study was non-experimental research; a questionnaire was applied to a convenience sample of 271 students (n=21; =0.906 =0.917), validity and reliability tests were carried out, and using the Partial Least Squares Structural Equations Modeling (PLS-SEM), the analysis of the proposed model was carried out. The results showed that computer anxiety is accentuated when there is a demand for the use of ICT and a lack of computer resources; it influences self-confidence and creativity in learning and emotional states. In the same way, self-esteem influences self-confidence, creativity in learning, and emotional states. The research provides critical foundations to improve the conditions for developing creative skills in learning.
The educational conditions in which university activities were developed during the pandemic
have been characterized by the use of information and communication technologies (ICT), so
the educational models designed for attendance had to be redesigned. Changing the dynamics
and established routines had an essential impact on their health and psychological well-being
[
The first studies related to this educational problem maintain that students’ most frequent
emotional responses have been fear, uncertainty, distorted perceptions of risk, and anxiety [
On the other hand, self-confidence manifests from a relationship between perfectionism and dificulties in decision-making, especially in university students, due to a lack of understanding and proper orientation [9]. Likewise, the development of creative skills in learning begins by reflecting on diferent strategies to teach and others to learn using framing to foster creativity in learning [10], also in the use of tools based on the interests of students, such as mobile technologies [11], and also the use of storybird to promote the writing of visual poems from the interpretation of images [12]. Similarly, developing an attitude towards technology will also allow students to better design the quality of their lexicon [13]. The state of anxiety in students is strongly associated with emotional states. For example, in the case of music, part of a recognition of musical emotions as a manifestation of the development of social cognition, which is also the premise of musical appreciation [14], the emotional state of students is very changeable, so the internalization of ICT influences emotional states in university students [ 15]. • H1 Computer anxiety has a positive efect on self-confidence. • H2 Computer anxiety has a positive efect on creativity in learning.
• H3 Computer anxiety has a significant efect on emotional states.
Positive self-esteem, or subjective feelings about oneself, is integrally related to self-concept [16]. Self-esteem should be afected by positive or negative information that is frequently presented in the form of social feedback [17]. There is a link through the support of classmates with prosocial behavior in which the role of self-esteem plays a significant moderating role [18]. For this reason, it is necessary to implement collaborative strategies for learning in which relationships and support from classmates play a fundamental role [19] in such a way that constructivist virtual environments have served as great afective support for students, especially in times of pandemic [20]. Thus, there are critical factors for the success of e-learning, such as overcoming computer anxiety, the quality of collaboration between teachers and students, the quality of information, the quality of service, the quality of the computer system or learning management system (LMS), and the use of computer devices [21], the university student will improve his self-esteem, have greater self-confidence, and will strengthen their creative skills in learning and have better emotional control.
• H4 Self-esteem has a significant efect on self-confidence. • H5 Self-esteem has a positive efect on creativity in learning.
• H6 Self-esteem has a positive efect on emotional states.
Diversity, participation, and creativity in learning require the same attributes in organizational and systemic leadership [22]. Fostering collaborative and playful environments for learning emphasizes creativity in learning and human flourishing in education [ 23]. Developing creative abilities today is one of the cornerstones of students’ abilities to achieve learning significance, especially in science, technology, engineering, arts, and mathematics (STEAM) education [24]. Therefore, creativity is becoming an emerging area of research in education [25]. The freedom that enables creative action also allows self-confidence in the person.
• H7 Self-confidence positively influences creativity in learning.
Social learning is closely related to the emotional dimension of the person, so today we can talk about social and emotional learning (SEL)[26], as proposed by Jones, who states that the social, emotional, and cognitive domains are interconnected in the learning process [27]. Managing educational resources well means adding design elements that make lessons more engaging. This is because students’ grades measure how they think and feel. In the same way, the cognitive processes are turned on, which leads to learning results like retention and transfer [28]. Also, it’s vital to set up learning sequences for students so that you can figure out their patterns of behavior [29]. On the other hand, a pedagogical approach to creativity [30] says that creativity in learning is linked to students’ creative self-eficacy, which relates to self-confidence and emotional balance.
• H8 Emotional states have a positive efect on self-confidence.
• H9 Emotional states positively influence creativity in learning.
This study was carried out with a sample of 271 university students from the professional art career; 90 men represent 33.2%, and 181 women represent 66.8%. The ages are between 18 and 30, with a mean of 22.9 and an SD= 3.58. The instrument was applied in April and May of the year 2022. The methodology applied for the study is a non-experimental investigation, and the sample extraction was for convenience. The instrument has been adapted and approved for the study, as the creativity in the learning variable precedes the research: Switching to online learning during COVID-19: Theorizing the role of IT mindfulness and techno-eustress in facilitating productivity and creativity in student learning [31] and the impact of technostress on end user satisfaction and performance [32]. For the self-esteem variable, the Rosemberg self-esteem scale [33] was used; the emotional states variable has been taken from the research: digital competence of non-university education students: predictive variables [34]. Additionally, statistical tests were carried out, such as exploratory and confirmatory factor analysis, respectively, to guarantee the robustness of the instrument.
Reliability tests were performed using Alpha Cronbach’s ( =0.906) and McDonald’s Coeficient (=0.917), with satisfactory results. Likewise, the analysis of communalities was carried out, obtaining results ranging from 0.536 to 0.904, which implies that the items would explain the model in the worst case by 53.6% and the best case by 90.4%. To analyze the adequacy of the items with their factor, the Kaiser-Meyer-Olkin test (KMO= 0.834) was performed, which indicates a reasonable adjustment of the analyzed items with their elements. Bartlett’s Sphericity Test had the following results: 2= 4233.895; df= 210, and p=<0.000, whose assessment is reasonably significant. The total variance is explained by 74.177% for the five factors of the model. Table 1 presents the rotated component matrix using the principal components analysis extraction method, with the varimax rotation method with Kaiser normalization. t = 5.854, p < 0.000), results that support H5; Self-esteem (AUT) has a positive efect on emotional states (EE ( AUT→EE = -0.535, t = 10.926, p < 0.000), results that support H6; Self-confidence (CO) positively influences creativity in learning (CA) ( CO→CA = 0.099, t = 1.816, p < 0.035), which supports H7 and emotional states (EE) positively influences creativity in learning (CA) ( EE→CA = 0.383, t = 5.691, p < 0.000), which supports H9. Contrarily, emotional states (EE) have a positive efect on self-confidence (CO) ( EE→CO = 0.021, t = 0.303, p < 0.381), results that do not support H8. 2 considering Creativity in learning as an endogenous variable, an 2 of 0.479 is obtained, that is to say, that the variance is explained by the model in 47.9%, being the exogenous variables, Self-esteem, the emotional states computer anxiety and acting as a mediation variable self-confidence; On the other hand, according to the structural model, considering the emotional states variable as an endogenous variable, the 2 is 0.376, that is, 37.6% of the variance is explained by the model, with the exogenous variables being Self-esteem and Computer anxiety.
Table 2 shows the results of the reliability and construct validity tests. It shows that, when we consider the correlation coeficients, the reliability and construct validity expressed in Cronbach’s alpha, the values obtained vary between 0.836 and 0.916, being very acceptable. The values of the average variance extracted, Average Variance Extracted (AVE), are between 0.664 and 0.856, which is above the 0.500 recommended by several authors, in such a way that it can be afirmed that the convergent validity is acceptable. For the analysis of the composite reliability, it is recommended as an acceptance criterion that the values obtained exceed 0.6, so that reasonable levels of internal consistency reliability would be demonstrated for each of the variables, with the result being valued between 0.895 and 0.947. The coeficient (rho_A) is used to verify the reliability of the values obtained in the construction and design of the model; it is recommended as an acceptance criterion that its values exceed 0.7, and the results vary between 0.841 and 0.932; consequently, the values of obtained evidence of very acceptable levels of reliability and validity, that is, of internal consistency of the model.
Table 3 presents the significant indirect efects obtained through the bootstrapping resampling test; according to the proposed model, self-confidence (CO) operates as a mediating variable between emotional states (EE) and creativity in learning (CA). According to the results, we can say that the total indirect efect of the relationship is (0.002), so it is not significant (t=0.255, p<0.399). Therefore, we can afirm that there is no complementary mediation.
Table 4 shows the discriminant validity test; for this purpose, the Fornell and Larcker criterion [35] was used; they propose that when two or more latent variables share the variance between pairs of constructs, it is less than the variance extracted by each individual construct. Discriminant validity exists [36], and validity tests are carried out to determine to what extent a specific construct is diferent from other constructs [ 37], essentially independent variables. Taking these criteria into account, there is discriminant validity.
CO
CA
EE 0.868 -0.228 -0.292 0.815 0.618 0.825
Another criterion to establish the discriminant validity is the Heterotrait Criterion - Monotrait -HTMT; it is used to evaluate the discriminant validity through the criterion of values of less than 0.9 of the constructs. Therefore, Table 5 does not present values that exceed the suggested value of 0.9, which is equivalent to saying that there is discriminant validity.
CO
CA
EE Computer Anxiety (LCA)
Self-esteem (AUT) Self-confidence (CO)
Emotional states (EE) Creativity in Learning (CA) 0.346
Regarding the evaluation of the global or estimated model, the adjustment index, Standardized Root Mean Square Residual (SRMR), which defines the diference between the observed correlation and the predicted correlation, according to Hu and Bentler [38], the lower values of a < 0.08 is considered a good fit; therefore, the results for the estimated model the SRMR is 0.079, and for the saturated model it is 0.079, which is equivalent to saying that the measurement model is well evaluated and that the model estimated does not provide more data than the model transmits. Therefore, the model cannot be rejected, so it is deduced that the model is valid. the substitution of the original sample [39]. Considering the significance level for the P-Value
The findings of this research confirm that computer anxiety as a manifestation in university students is accentuated when there is a demand for the use of ICT and a lack of computer resources in such a way that it influences self-confidence and creativity in the learning and emotional states of university students. Similarly, self-esteem, integrally related to self-concept, is a predictor variable and would influence self-confidence, creativity in learning, and emotional states. The self-confidence associated with the student’s emotional balance would positively influence creativity in learning; Regarding emotional states, there would be a positive efect on creativity in learning, especially when we promote collaborative and playful environments. However, emotional states would not influence the student’s self-confidence because emotional states are interconnected in the learning process with social and cognitive domains. Regarding self-confidence, which operates as a mediating variable between emotional states and creativity in learning, it has not been possible to demonstrate that this mediation efect exists[40].
We want to express our gratitude to all the people who have collaborated on this research, especially the students and graduates of the School of Arts at the National University of San Agustin and the students of the Professional School of Advertising and Multimedia at the Catholic University of Santa Maria de Arequipa, Peru. [5] K. D. Sandoval, P. V. Morote-Jayacc, M. Moreno-Molina, A. Taype-Rondan, Depresión, estrés y ansiedad en estudiantes de medicina humana de ayacucho (perú) en el contexto de la pandemia por covid-19, Revista Colombiana de Psiquiatría (2021) 139767– 139767. URL: https://doi.org/10.1016/j.scitotenv.2020.139767https://linkinghub.elsevier. com/retrieve/pii/S0034745021001682. doi:10.1016/j.rcp.2021.10.005. [6] F. Araque-castellanos, C. Jur, S. O. Gonz, C. Jur, S. R. Jimena, C. Jur, S. I.-l. Nuv, C. S. Pol, H. O.
Medina-ortiz, C. Jur, S. Received, Bienestar psicológico y características sociodemográficas en estudiantes universitarios, Archivos Venezolanos de Farmacologia y Terapeutica 39 (2021) 999–1004. doi:http://doi.org/10.5281/zenodo.4544052. [7] J. M. Meneses, Computer anxiety and ict utilization of program heads in a state university, International Journal of Information and Education Technology 12 (2022) 1104–1109. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136041871&doi=10.18178% 2fijiet.2022.12.10.1727&partnerID=40&md5=679e88df1435fd4aca30ad3db10f92a3. doi: 10. 18178/ijiet.2022.12.10.1727. [8] X. Hu, J. Zhang, S. He, R. Zhu, S. Shen, B. Liu, E-learning intention of students with anxiety: Evidence from the first wave of covid-19 pandemic in china, Journal of Afective Disorders 309 (2022) 115–122. URL: https://www.sciencedirect.com/science/article/pii/ S0165032722004657. doi:https://doi.org/10.1016/j.jad.2022.04.121. [9] H. Chen, L. Pang, F. Liu, T. Fang, Y. Wen, “be perfect in every respect”: the mediating role of career adaptability in the relationship between perfectionism and career decision-making dificulties of college students, BMC Psychology 10 (2022). URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130808123&doi=10.1186% 2fs40359-022-00845-1&partnerID=40&md5=dc17a8905245acbeadc0da21c142e3c6. doi:10. 1186/s40359-022-00845-1. [10] S. R. Jónsdóttir, M. A. Macdonald, Using framing to foster creativity in learning: Reflective tool to analyze and discuss practice, Frontiers in Education 7 (2022). URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136212422&doi=10.3389% 2feduc.2022.865893&partnerID=40&md5=db6903281883e31d92b2f285b97a3722. doi: 10. 3389/feduc.2022.865893. [11] L. Zheng, R. Liu, X. Zhang, Efects of an interest-driven creation approach on students’ mobile learning performance and creativity in learning science in a science museum, Journal of Computers in Education 8 (2021) 159–182. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090957502&doi=10.1007% 2fs40692-020-00175-4&partnerID=40&md5=1181441ddcf511988759c64a5910f2da. doi:10. 1007/s40692-020-00175-4. [12] O. A. Gutierrez Aguilar, L. C. Salas Valdivia, Implications of storybird to promote the writing of poems graphics on the basis of the interpretation of images in the institutions of primary and secondary level of perú, in: Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018, ????, pp. 49–52. doi:10.1109/LACLO.2018.00021. [13] K. Villalba-Condori, R. Avello-Martínez, M. Berrios-Espezua, S. Castro-Cuba-Sayco, Students and technological media: Use and attitude for lexicon formation, Informacion Tecnologica 31 (2020) 141–148. URL: https://www.scopus.com/inward/record.uri?eid= 2-s2.0-85085251013&doi=10.4067%2fS0718-07642020000200141&partnerID=40&md5= aef78997fa609959b095a5126aa447be. doi:10.4067/S0718-07642020000200141. [14] J. Yang, J. Chai, T. Qiu, X. Quan, M. Zheng, Relationship between empathy and emotion recognition in chinese national music: An event-related potential study evidence, Acta Psychologica Sinica 54 (2022) 1181–1192. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137908583&doi=10.3724% 2fSP.J.1041.2022.01181&partnerID=40&md5=903f045b3ae8330e153a391fe266fb6b. doi:10.3724/SP.J.1041.2022.01181. [15] S. C. Huanca, A. G. Aguilar, A. B. D. Perez, O. G. Aguilar, The internalization of ict and its influence on emotional states in university students, in: Iberian Conference on Information Systems and Technologies, CISTI, ???? URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115744946&doi=10.23919% 2fCISTI52073.2021.9476543&partnerID=40&md5=97df76110465e8f828869f970fadc776. doi:10.23919/CISTI52073.2021.9476543. [16] A. G. Greenwald, F. S. Bellezza, M. R. Banaji, Is self-esteem a central ingredient of the self-concept?, Personality and Social Psychology Bulletin 14 (1988) 34–45. [17] R. C. Amey, J. B. Leitner, M. Liu, C. E. Forbes, Neural mechanisms associated with semantic and basic self-oriented memory processes interact moderating self-esteem, iScience 25 (2022) 103783. URL: https://www.sciencedirect.com/science/article/pii/S2589004222000530. doi:https://doi.org/10.1016/j.isci.2022.103783. [18] C. Ma, S. Mastrotheodoros, X. Lan, Linking classmate autonomy support with prosocial behavior in chinese left-behind adolescents: The moderating role of self-esteem and grit, Personality and Individual Diferences 195 (2022) 111679. URL: https://www.sciencedirect. com/science/article/pii/S0191886922001830. doi:https://doi.org/10.1016/j.paid. 2022.111679. [19] O. G. Aguilar, M. Martinez Delgado, F. P. Quispe, Collaborative learning and its relationship with the classmates afective support with the colles questionnaire application, in: Proceedings of the 15th Latin American Conference on Learning Technologies, LACLO 2020, ???? doi:10.1109/LACLO50806.2020.9381176. [20] O. Gutiérrez-Aguilar, A. Duche-Pérez, O. Turpo-Gebera, Afective support mediated by an on-line constructivist environment in times of covid-19, in: Smart Innovation, Systems and Technologies, volume 259 SIST, 2022, pp. 458–468. doi:10.1007/978-981-16-5792-4_ 45. [21] S. Chicana-Huanca, C. V. Sharmila, O. Gutierrez-Aguilar, Key factors for the success of e-learning in high school students in times of covid-19, in: Proceedings - 2021 16th Latin American Conference on Learning Technologies, LACLO 2021, ????, pp. 383–388. doi:10.1109/LACLO54177.2021.00047. [22] J. Durrant, Special issue: Diversity, participation and creativity in learning, Improving Schools 15 (2012) 93–100. URL: https://doi.org/10.1177/1365480212452657. doi:10.1177/ 1365480212452657. [23] J. B. Jensen, O. Pedersen, O. Lund, H. M. Skovbjerg, Playful approaches to learning as a realm for the humanities in the culture of higher education: A hermeneutical literature review, Arts and Humanities in Higher Education 21 (2021) 198–219. URL: https://doi.org/ 10.1177/14740222211050862. doi:10.1177/14740222211050862. [24] X. Weng, O.-L. Ng, Z. Cui, S. Leung, Creativity development with problem-based digital making and block-based programming for science, technology, engineering, arts, and mathematics learning in middle school contexts, Journal of Educational Computing Research (2022) 07356331221115661. URL: https://doi.org/10.1177/07356331221115661. doi:10.1177/07356331221115661. [25] G. Ozkan, U. Umdu Topsakal, Exploring the efectiveness of steam design processes on middle school students’ creativity, International Journal of Technology and Design Education 31 (2021) 95–116. URL: https://doi.org/10.1007/s10798-019-09547-z. doi:10.1007/ s10798-019-09547-z. [26] L. O. Rogers, C. Grifin, C. A. Warren, Race and social emotional learning for black students in urban schooling contexts, Urban Education 57 (2020) 187–190. URL: https: //doi.org/10.1177/0042085920933337. doi:10.1177/0042085920933337. [27] S. M. Jones, J. Kahn, The evidence base for how we learn: Supporting students’ social, emotional, and academic development. consensus statements of evidence from the council of distinguished scientists, Aspen Institute (2017). [28] X. Wang, R. E. Mayer, M. Han, L. Zhang, Two emotional design features are more efective than one in multimedia learning, Journal of Educational Computing Research (2022) 07356331221090845. URL: https://doi.org/10.1177/07356331221090845. doi:10.1177/ 07356331221090845. [29] F. Bernal, J. Maldonado-Mahauad, K. Villalba-Condori, M. Zúñiga-Prieto, J. VeintimillaReyes, M. Mejía, Analyzing students’ behavior in a mooc course: A process-oriented approach, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12425 LNCS, ????, pp. 307–325. doi:10.1007/978-3-030-60128-7_24. [30] I. Capron Puozzo, C. Audrin, Improving self-eficacy and creative self-eficacy to foster creativity and learning in schools, Thinking Skills and Creativity 42 (2021) 100966. URL: https://www.sciencedirect.com/science/article/pii/S1871187121001814. doi:https://doi. org/10.1016/j.tsc.2021.100966. [31] A. Shirish, S. Chandra, S. C. Srivastava, Switching to online learning during covid-19: Theorizing the role of it mindfulness and techno eustress for facilitating productivity and creativity in student learning, International Journal of Information Management 61 (2021) 102394. URL: https://www.sciencedirect.com/science/article/pii/S0268401221000876. doi:https://doi.org/10.1016/j.ijinfomgt.2021.102394. [32] M. Tarafdar, Q. Tu, T. Ragu-Nathan, Impact of technostress on end-user satisfaction and performance, Journal of Management Information Systems 27 (2010) 303–334. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951919849&doi=10.2753% 2fMIS0742-1222270311&partnerID=40&md5=46c6f0ec487e2347213c2bae2c02481e. doi:10. 2753/MIS0742-1222270311. [33] M. Rosenberg, C. Schooler, C. Schoenbach, F. Rosenberg, Global self-esteem and specific self-esteem: Diferent concepts, diferent outcomes, American sociological review (1995) 141–156. [34] J. d. Pablos Pons, M. P. Colás Bravo, J. Conde Jiménez, S. Reyes de Cózar, La competencia digital de los estudiantes de educación no universitaria: variables predictivas, Bordón, 68 (2) (2016). [35] C. Fornell, D. F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, Journal of marketing research 18 (1981) 39–50. [36] J. A. Martínez-García, L. Martínez-Caro, La validez discriminante como criterio de evaluación de escalas: ¿teoría o estadística?, Universitas Psychologica 8 (2009) 27–36. URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid= S1657-92672009000100002&nrm=iso. [37] J. P. B. Kosiba, R. Odoom, H. Boateng, K. K. Twum, I. K. Abdul-Hamid, Examining students’ satisfaction with online learning during the covid-19 pandemic - an extended utaut2 approach, Journal of Further and Higher Education 46 (2022) 988–1005. URL: https: //doi.org/10.1080/0309877X.2022.2030687. doi:10.1080/0309877X.2022.2030687. [38] L.-t. Hu, P. M. Bentler, Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification, Psychological Methods 3 (1998) 424–453. doi:10.1037/1082-989X.3.4.424. [39] J. Henseler, Partial least squares path modeling, Springer, 2017, pp. 361–381. [40] A. B. Duche Pérez, O. A. Gutiérrez Aguilar, F. M. Paredes Quispe, Satisfacción laboral y compromiso institucional en docentes universitarios peruanos, Conrado 15 (2019) 15–24.