Academic framing of computational representations in the virtual classroom during Covid-19

Juan Garza-Sanchez

Academic framing of computational representations in the virtual classroom during Covid-19

Juan Garza-Sanchez

Department of Communication, Nuevo Leon University, Monterrey, Mexico

*Corresponding Author:

Juan Garza-Sanchez
Department of Communication, Nuevo Leon University, Monterrey, Mexico
E-mail: juan.garza@uanl.mx

Received: 03-April-2023, Manuscript No. Gmj-23-97976; Editor assigned: 05-April-2023, PreQc No. 97976; Reviewed: 19-April-2023, QC No. Q-97976; Revised: 24-April-2023, Manuscript No. Gmj-23-97976 (R); Published: 29-April-2023, DOI: 10.36648/1550- 7521.21.62.369

Visit for more related articles at Global Media Journal

Abstract

The objective ofthis work wasto establish the framing ofthe socialrepresentations of the information, processed in computers encoding data in academics. A retrospective study was conducted with a non-probabilistic selection of authors and judges who are experts in the field, considering publications from 2019 to 2021 in leading repositories such as Scopus and Web of Science. The differences between the groups were established, discussing their implications in the decision making for the development of research projects, although the research design limited the results of the work, extending its scope to other repositories.

Keywords

Agenda; Framing; Social representations; Expectations; Computer

Introduction

The development of research projects often requires the competition of computers, software and data analysis techniques, but the acceptance, appropriation and intensive use of them presents limitations in terms of utility and risk expectations [1]. The information sciences, behavioural sciences and educational sciences have addressed this problem from predictive models of stigma, risk or rejection against utility, acceptance, appropriation and intensive use [2].

Some explanatory models of human capital formation suggest that the formation of talent or intellectual capital in intangible assets of organizations is due to habitus [3]. It is a process of teaching and learning of skills, knowledge and professional dispositions that are transferred through the codification of information [4].

On the other hand, cognitive models of technology acceptance have developed predictions of the intensive use of computers based on perceptions [5]. Computational self-efficacy has been documented as a predictor of intensive use, but the risks of identity theft, espionage or stress determine the rejection of information technologies, devices and networks [6].

On the other hand, the training and training models highlight the formation of talents through information and communication technologies, although in the case of computer computers it is data processing [7]. The determinants of these Behaviors and skills are in academic or job training [8].

The models focused on the framing or bias of informative sources have explained the emergence of risk thresholds around which the decision-making can be carried out, establishing exclusion and inclusion criteria in terms of negative dimensions and positive.

However, the predictive models of the social representations of these determinants have not been observed in the explanation of the relations with the intensive use of technologies, devices and electronic networks.

Therefore, the objective of the present work was to establish the academic link relative to the social representations of computer computers, considering the dimensions of the organizational, educational and cognitive models.

Methodology

A documentary, retrospective and exploratory study was carried out with a selection of sources indexed to international repositories Table 1, considering the indexing period from 2019 to 2021, as well as the search by allusive keywords for negative (stigma, risk, rejection) and positive (utility, acceptance, appropriation) (Table 1)

Content analysis and opinion matrices were used, considering the inclusion of findings, ratings and comparisons of coded data such as; -1 for negative dimensions (stigma, risk and rejection) and +1 for positive dimensions (utility, acceptance and appropriation)

The qualitative data analysis package was used, considering equation (1) in which the contingency relations and the proportions of probabilities of taking risks in permissible thresholds of human capital formation stand out The contrast of the null hypotheses was made from the estimation of these parameters.

Results

The normality and contingency values show that the judges' scores are distributed in such a way that probability ratio analyses can be performed in order to establish risk thresholds around the decision to reject or accept the random homogeneous effects reported in literature (Table 2).

The percentages between the negative dimensions (stigma: 15% in authors and 17% in judges; risk: 16% in authors and 18% in judges; rejection: 17% in authors and 19% in judges) and positive (utility: 19% in authors and 16% in judges; acceptance: 17% in authors and 18% in judges; appropriation: 16% in authors and 12% in judges) were different between authors and judges. This means that the criteria for exclusion and inclusion of data are different, but consistent in terms of the dimensions of the social representations of computer computers.

Contingent relationships indicated significant differences between authors and judges with respect to both negative ⌠χ2 = 14,35 (12 df) p< ,05⌡ and positive ⌠χ2 = 15,46 (15 df) p < ,05⌡ dimensions, suggesting that the criteria are consistently different in every one hundred observations that are made with these same data of results published in the repositories.

In order to establish the risk thresholds, the probability proportions were estimated, considering the categories of analysis with respect to the extracts of findings rated by expert judges on the subject (Table 3).

The proportions of probability for the positive dimensions [OR = 14, 35 (12, 34 to 21, 11)] indicate that decisions made based on the authors' findings and the ratings of judges would be set at a permissible or tolerable threshold of risk. This is because the significant differences between the two sources are at a consistent threshold of results.

In order to be able to observe the structure of relationships between categories, objectification and naturalization, with respect to each of the extracts, a model of structural equations was developed (Figure 1).

The structure shows the prevalence of the two learning categories with respect to the qualifications of the expert judges in the topics. It is about the objectification of data that supposes a continuous and systematic processing with respect to naturalization which suggests the application of information in teaching and learning. Both categories make up the representation of the judges regarding the learning process in data processing. The contrast of this model in other learning scenarios such as the virtual classroom and in other samples such as professional practitioners and social servants, will allow anticipating the formation of intellectual capital in the traditional classroom [8].

Discussion

The contribution of the present work to the state of the matter lies in the establishment of a threshold of proportion of decisionmaking probability based on permissible risks, although the research design limits the results to the information search scenario.

In relation to the authors' findings that identify negative and positive dimensions of computational representation in the academic and professional training process, the present work warns that decisions made based on these positive dimensions of utility, acceptance and appropriation of technologies, electronic devices and networks would be located in a tolerable threshold of informative risk, although the authors report other findings regarding commitment, satisfaction and innovation in the training process of capital expert in computational programming.

Regarding the qualifications of expert judges in the social representations of computer computers where negative and positive dimensions are established coinciding with authors' findings, the present study warns that only in the case of positive dimensions of utility, acceptance and appropriation, excluding commitment, satisfaction and innovation, a permissible threshold of decision-making prevails based on published and processed information.

Future lines of research related to the positive dimensions of commitment, innovation and satisfaction will allow observing the cycle of social representations around the computational processing of information, considering homogeneous thresholds between the published findings and the qualifications of experts in the field.

Conclusion

The purpose of the present investigation consisted in the establishment of the academic framework with respect to the findings published from 2019 to 2021 and the qualifications of experts in the field regarding negative and positive dimensions of computational information processing, although the design of the research limited the results to the search results in the Scopus and WoS repositories.

Within the framework of strategic alliances between universities and companies, the formation of intellectual capital consists of computational information processing, social representations are crucial. The establishment of an academic agenda with the negative and positive dimensions will allow decisions based on information located at a permissible threshold of consistency and risk.

The inclusion of the positive dimensions of commitment, innovation and satisfaction will allow the development of a more complex algorithm in order to be able to estimate path structures and relationships between explanatory factors of the computational formation.