Design of a multiplexed virtual classroom

doi:10.21203/rs.3.rs-4613536/v1

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Design of a multiplexed virtual classroom

https://doi.org/10.21203/rs.3.rs-4613536/v1

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The purpose of this research is to design a multiplexed virtual classroom model (a technique for combining two or more contents or activities so that a single teacher can help students learn a topic) and to address the issue of classroom management, where the teacher has many activities and little planning time. To determine which contents will be multiplexed, an analysis of learning strategies was conducted, and the strategy was named with the acronym POSTTIE. Consequently, the design of the multiplexed virtual classroom model is presented as a living, cyclical, feedback system that can be implemented by a single teacher or several teachers in different stages of planning, execution, and evaluation, using at least three strategies.

To begin, we analyzed which learning strategies were reported by sixth-semester engineering students from a public university to use during their study time. The learning strategies were identified through a questionnaire with an open-ended question, where students were asked to classify their 10 responses. In another section, they listed only one word from those 10 responses to use the first method of natural semantic networks. The results obtained from the questionnaire were then reaffirmed through factor analysis as the second method. Subsequently, a comparison of the results of the applied analysis methods was conducted. Finally, eight learning strategies were named that coincided with both methods and served to validate the results.

A quantitative factor analysis was conducted. The exploratory part of the method involved 111 participants; their responses were analyzed using Method 1 and ATLAS.ti© software. The confirmatory part involved 142 participants, and their responses were analyzed using Method 2 with dimension reduction via SPSS and the R language, with a confidence level of 70% and an error < 1.12%.

Educational Philosophy and Theory

Virtual Classroom

Natural Semantic Networks

Factor Analysis

Exploratory Method

Confirmatory Method

Learning Strategies

In ancient Greece, when Aristotle taught Alexander the Great, the classroom was outdoors, and both were seated on stones. The current classroom remains predominantly face-to-face but can also be remote (synchronous) or online (asynchronous). A significant difference from antiquity to the present is the increase in the number of students per classroom, the reduced time allocated to listen to each student and understand their thinking, and the advancements in technology, which have led to working through computers and the rapid acquisition of knowledge.

Therefore, this research aims to design a multiplexed virtual classroom model (a technique for combining two or more contents or activities so that a single teacher can help students learn a topic) and to address the issue of classroom management, where the teacher has many activities and little planning time.

The term "multiplexed" originates from a multiplexer (used in the field of digital design computing), a device that has one input and several outputs. The most common use is to route the input to the given output. What is interesting about this device is that when several signals enter and the speed is enormous, it appears to transmit simultaneously to all outputs, even though it initially sends to output 1, then to output 2, and so on. With this idea in mind, the use of this proposed technique is defined.

Definition of POSTTIE: (An acronym for the learning strategies found in the exploratory and confirmatory analysis by two methods)

The teacher transmits their knowledge (an input), and the students receive what they need to learn in various ways (multiple outputs), as detailed later. This definition was applied at the end of the analysis.

Virtual education is an important element that allows for the development of the teaching and learning process through information and communication technologies (ICT), enabling educational work from and to any place with a suitable laptop for internet access, memory space, among other requirements. In the article by Díaz-Vicario, A., Mercader, Juan C., & Gairín Sallán, J. (2019), a problematic use of basic ICTs among young people is observed, such as distractions in class, increased time alone, among other situations.

In addition, the 2019 pandemic highlighted inequalities in access to ICT. For example, regarding the highest educational level, there is 70% availability of internet and computers only in higher education, while with less education, it is less than 30%, as seen in the article by (Bearzotti, L. A., 2020, p. 215).

However, ICTs are not the only good or bad aspect of virtual education. Whether face-to-face, remote, or online, the increase in student enrollment is disproportionate, with a single teacher in the classroom who must teach, grade work, respond to student queries, and give a final grade. The teacher must continue performing their duties even with the increase in administrative tasks.

The student must be self-taught in traditional teaching to interpret what the teacher asks for, achieve a passing grade, and, if time allows, learn.

The problem lies in some students' inability to understand a university subject in the teaching and learning processes between teachers and students, a factor to be addressed from the perspective of classroom management.

Knowing or understanding science is not the same as communicating it to students (Bargallo, 2005). This was the motivation to attempt to change something to improve the teaching and learning processes.

The contribution of this work to the field of study is to propose a new classroom format that utilizes most of the didactic resources known to students, to strengthen student learning, provide guidelines for teachers to better organize their teaching and learning processes in the classroom.

In the initial bibliographic research, we worked with the SCOPUS database, with search keywords classroom management and learning strategies, where 8491 keywords were found in 2139 articles, of which "VOSviewer" selected 1000 keywords. The result is seen in the Fig. 1. From here, it was separated into ideas: Classroom management, types of classroom, Teaching and learning methods, and learning strategy. Are detailed below.

2.1 Classroom management

School management is defined as a "process involving teamwork responsibility, encompassing the construction, design, and evaluation of educational work: generating a diagnosis, objectives and goals, strategies, and resource organization" (Cabrera, M. T. F., & Adan, I. A. R., 2017, pp. 45–62).

(Cabrera et al, 2017) considers that management can be approached from various perspectives. In his article, he discusses his proposal where strategic management establishes relevant elements that help address school dynamics. For this, it is necessary to develop an analysis of its implementation under particular social conditions to understand its results, viability, challenges, and limitations.

(Gairín Sallán, J., & Martín Bris, M., 2004) proposes a framework for analyzing school organization, considering the school's dimensions: "institutional, pedagogical, didactic, orientative, organizational, and specific, from the perspective of society and organizations. A school organization serves as practice and technology, as scientific knowledge, as a discipline, viewing it through three approaches: scientific-rational, interpretive-symbolic, and critical" (p. 63).

For this author, the curriculum, autonomy, institutional quality, and strategies are important: organizational development, school review, departmental meetings, collaborative development, and in-center training. One aspect of school management is classroom management, which is the focus of this research.

Classroom management refers to the actions and strategies teachers use to maintain order in the classroom. (Rufai, M. M., Alebiosu, S. O., & Adeakin, O. A. S., 2015) state that part of classroom management involves providing good and simplified course materials, while another part ensures effective delivery. These authors explain this in the context of the traditional classroom type, which will be discussed in the following section.

There is also what is called effective classroom management by the teacher, which has been mentioned since the 1980s, where the teacher's role in classroom management and educational improvement is emphasized.

In the article of (López, A. P., Prados, M. Á. H., & Romera, C. G., 2013), the effective school is one that continuously promotes each student's ability to reach the highest possible level in all aspects of their performance and overall development, beyond what would be expected given their initial performance level and social, cultural, and economic situation.

Effective schools claim that their objective is to increase and ensure the quality of teaching and learning processes, but in reality, many contextual factors influence the outcomes, making it effective for some and not for others.

The main limitation is that they focus solely on the primary or secondary education level.

2.2 Types of classrooms

In a traditional classroom, there is direct contact between students and the instructor. This facilitates the instructor's ability to enforce rules aimed at effective classroom management. This physical contact is difficult to achieve in a virtual classroom, yet effective classroom management is still desired. The virtual classroom is useful for student review (Rufai, M. M., Alebiosu, S. O., & Adeakin, O. A. S., 2015).

A virtual classroom is an asynchronous online learning environment that offers courses and materials to students, providing collaboration and interaction using an asynchronous forum as the main platform to support students' independent study (Rufai et al., 2015).

(Segovia Olmo, F., & Beltrán Llera, J., 1998). asserts that the smart classroom is a set of knowledge reflected in a unique pedagogy, which advocates for a change in the educational model through the total reengineering of the educational system, based on a particular conceptualization of what quality in education would be.

The author defines the smart classroom as a: “Learning community, whose main objective is the development of students' intelligence and values, who plan, carry out, and regulate their own work, under the mediation of teachers, through diversified teaching methods and authentic tasks, evaluated by students and teachers, in a technologically equipped multipurpose open space, organized according to the principles of total quality management” (Segovia et al, 1998, p. 18). This proposal is not applied in practice; it remains theoretical.

2.3 Teaching and learning methods

In a real or virtual classroom, we have certain basic characteristics such as methods that guide a specific way of proceeding in the classroom. These methods organize and direct questions, exercises, explanations, the social management of the classroom, or evaluation activities that are carried out according to an order of actions aimed at achieving the proposed goals (Quinquer, 2004).

There are expository, interactive, and individual methods, depending on the educational purpose of the teacher, the complexity of the tasks, the number of students, and even the students themselves. Quinquer (2004) notes that it is not just about learning knowledge, but also about using it to solve problems, explain new phenomena, and pose new questions.

Other methods include: individual method, expository method, interactive method, case method, problem-based learning, (social) simulations, investigations, and projects, among others.

In another reviewed article, the virtual classroom is examined from the perspective of metacognition (Partida, S. P. ,2006). Metacognition involves the development of skills in a subject that help us understand how a task is developed and, in general, how a student thinks when solving a problem, comprehending a text, or performing another academic activity. Cognitive strategies are those mental acts that allow a student to understand a text, give it meaning, and master its comprehension.

2.4 Learning strategy

In the article by Granda et al. (Granda Asencio, L. Y., Espinoza Freire, E. E., & Mayon Espinoza, S. E., 2019), they discuss teaching tools, mentioning that teaching tools are ICT, which are the technologies needed for the management and transformation of information: computers and programs that allow the creation, modification, storage, protection, and retrieval of information relevant to various fields. However, they leave it very generalized; it is also mentioned that not all teachers manage to have these work skills. (Roncancio-Ortiz, A. P., Ortiz-Carrera, M. F., Llano-Ruiz, H., Malpica-López, M. J., & Bocanegra-García, J. J., 2017), propose using video games as a strategic teaching tool, but only one tool is proposed in their articles.

When considering the term learning strategies, there are also several publications such as the one by (Arias, A. V., Lozano, A. B., Cabanach, R. G., & Pérez, J. C. N., 1999), who conducted a theoretical conceptual review of what was available at the time. They conclude that learning strategies are deliberate, intentional, and controlled actions that the student exercises over activities or integrated sequences of procedures aimed at facilitating their learning. The student must be willing and motivated to carry out the process. The author summarized their information into three strategies: 1. cognitive strategies, 2. metacognitive strategies, and 3. resource management strategies.

(Román, J. M., & Gallego, S., 1994), conclude in their article that teaching is helping to think. They classify strategies into acquisition, encoding, and retrieval of information, and support strategies for processing. The characteristic of these articles is that they are always from the teachers' point of view. However, if the aim is to investigate what the student thinks and does in general, it is reflected in a questionnaire where the student is asked to indicate whether they use certain strategies, as seen in the article by (Gargallo, B., Jesús, S. R., & Pérez-Pérez, C., 2009). Therefore, another technique was used in this article to obtain information from the student, without presenting any specific strategy.

2.5 Statistical analysis technique

Semantic Networks. A network is an interconnection of related points, and a semantic network is an interconnection of semantically related concepts (Vivas, 2007). Natural semantic networks are a network or connections of words that together form a meaning (Rivero, 2008, pp. 113–154) (cited in Valdez, 2000) and "are a useful tool for studying the meanings that certain words or expressions have in a specific social group." Natural Semantic Networks are a useful tool for studying the meanings that certain words or expressions have in a specific social group (Rivero, 2008). These are the ideas and concepts that people construct about any object, topic, or situation in their daily lives, often expressed in colloquial terms (Langle-Flores, A., & Cupul-Magaña, F. G., 2021).

Exploratory Technique of Natural Semantic Networks: Figueroa proposes nine guiding elements for the semantic network technique: a) the study technique must be entirely empirical. b) Semantic distance must be quantified in a factorial analysis. c) The networks must be hierarchical. d) It must be possible to study a network at all stages from infancy. e) Concepts can be defined and act as definers simultaneously. f) The use of artificial intelligence models should be avoided. g) A multidimensional model in time must be postulated.

Semantic network analysis is a visual representation of the relational patterns between different concepts and can be applied for the systematic review of scientific literature (Langle-Flores et al., 2021)

Factorial analysis is a statistical technique that aims to reduce the number of variables to make them more manageable, allowing for possible correlations to be found between some of these variables (Aráuz, 2015). It is a multivariate statistical technique that analyzes the dependency relationships existing between given variables, where latent or unknown variables (factors) provide a solution to the given problem with a reduced number of dimensions.

This technique operates in two parts: exploratory analysis and confirmatory analysis.

Exploratory analysis is used to create a model, or for the development and validation of tests, to explore the set of latent or hidden variables (inferred variables), given other variables that act as common factors explaining the test results. This exploratory analysis can be performed manually, using natural semantic networks, or with software such as SPSS, ATLASti©, Python, or R language, among others. The difference is that it is not classified; it only shows the most repeated words.

Confirmatory analysis is a method for estimating the confidence between two models; the result indicates whether the test is validated or not. This confirmatory analysis can and should be done manually, proposing the model or possible data grouping to reduce the information.

To validate the model automatically, we use software like SPSS (ATLASti© does not perform this function), Python, R language, or others. The information most aligned with books or articles was processed using R language. When valid, it allows for better interpretation with fewer data.

Structural equation modeling is a multivariate statistical analysis technique that enables the analysis of complex patterns of relationships between variables, comparisons between and within groups, and the validation of theoretical and empirical models.

3.1 Questionnaire design

The questionnaire aimed at identifying didactic tools regarding student learning processes was developed using two techniques. The first involved employing modified natural semantic network techniques for multiconcepts, with manual analysis revealing eight tools. Subsequently, the same dataset underwent factorial analysis as the second technique. Initially, exploratory analysis was conducted, this time employing ATLASti© for automatic analysis. Subsequently, the reliability and validity of the exploratory findings were ensured through confirmatory analysis, verified using SPSS and contrasted with R language. Additionally, there are validated didactic tools referred to as POSTTIE.

3.2 Data collection

A quantitative factorial analysis was conducted. The exploratory phase of the method involved 111 participants; their responses were analyzed using Method 1 and ATLASti© software. In the confirmatory phase, 142 participants took part, and their responses were analyzed using Method 2 with dimension reduction through SPSS and the R language, with a confidence level of 70% and an error rate of < 1.12%.

New Definition of Educational Multiplexing: A technique that combines two or more learning strategies: POSTTIE with content or activities so that a single teacher can assist students in learning a subject. The model proposal was developed based on the analyzed learning strategies, reduced to the 10 necessary ones, along with classroom management research. The chosen work processes were: planning, execution, and evaluation, as shown in Fig. 2. With this information, visual, auditory, kinesthetic, and reader-writer contents are mixed in the new multiplexed format.

5.1 Reliability and validity test

Confirmatory factor analysis was conducted to examine the validity and reliability of the questionnaire. Subsequently, convergent and discriminant validity of the data were analyzed. Estimation, reliability, Cronbach's alpha, and average variance are shown in the SPSS and R language results.

• Natural Semantic Network Analysis.

Natural semantic network analysis was employed, yielding 49 definitions. Data were collected manually in Excel tables, as depicted in Table 1. The exercise definition was the most cited by students and was also chosen as the top hierarchy by most respondents, followed by "videos," then "books," etc.

Table 1

Definitions of the exploratory study by semantic networks
exercises	videos	books	practice	review
notes	write	read	review	time understand
study	Projects	see	analyze	examples
equipment	explain	interest	memorize ask	practice
reviewing	attention	aid	grasp	concepts
constancy	teach	listen study	do	investigation
reading	reading	organization	to reason	relate
tasks	theory friends	scoring	analysis	application
article	advisory	associate	search	clear

The obtained results are displayed in the levels graph, Fig. 1; the most voted words from the semantic analysis can be seen here, compiled manually using Excel software. The most voted words are shown hierarchically, but there are different words with similar meanings counted separately. For example, "nota," "reseña," "review," among others, are not shown. We also have the same words with or without accents, which differ depending on the software.

The English translation of Fig. 3 is presented below since the data are originally in Spanish:

Videos: Videos. Libros: Books. Practicar: Practice. Repaso: Review. Apuntes: Notes. Escribir: Write. Leer: Read. Repasar: Revise. Tiempo: Time. Entender: Understand. Estudiar: Study. Ver proyectos: View projects. Analizar: Analyze. Ejemplos: Examples. Equipo: Team.

Similar meanings were manually collected and grouped with a representative label, considered as the outcome of the sought problem. Subsequently, the results model of semantic networks by nodes is presented. In response to the inquiry from the university student group: "How is a university subject learned?" The final result is depicted in Fig. 4.

In subsequent results, the confirmatory analysis is presented, where these eight identified groupings were validated.

Automatic exploratory analysis using ATLAS.TI.

In this second method, utilizing ATLASti© software, a word cloud, Fig. 3, is generated, displaying the most mentioned words by students in larger sizes. The process was automated using the provided software. It is noted that, in order of appearance and size, the following words were found: Exercises, videos, study, practice, review, reading, etc.

For further details, a word count was requested from the software. Using ATLASti©, the unclassified word count is shown in Fig. 5.

In Fig. 6 word count, it can be observed that exercises were mentioned 37 times, videos have a frequency of 31, study 18, reading 16. Review 16, practice 15, among others.

Manual Confirmatory Analysis

In this confirmatory analysis, the defining features are extracted and presented in affirmative (or negative) sentences. Subsequently, both results are compared. Two techniques are employed: one manually and the other repeated automatically, this time utilizing the same database but importing it into the R language. To achieve this process, groupings to be validated must first be proposed. Initially, manual groupings are suggested, followed by the automated procedure. These proposals are then analyzed.

Manual confirmatory analysis using factor analysis.

Similar words conveying the same meaning were identified and reduced to 8 out of 49 (+ 1 redundant). The equations are depicted below in Fig. 7. The dimensional reduction of the problem is illustrated by Xi1, Xi2, Xi3, Xi4, Xi5, Xi6, Xi7, and Xi8. In the structural equation model, the X's could also be observed, where in the exploratory analysis, the 45 defining factors are found; each containing a percentage of error Es.

Based on the results obtained, with the aid of Excel manually, the strategies found by the four careers of the Faculty of Engineering are displayed. Figure 8 illustrates that students from all careers need to be more organized in their study approach; conversely, aerospace engineering students prefer working in teams to study a university subject.

Confirmatory factor analysis using R language

Using the factanal() command, which is factorial analysis:

as an example, illustrates it for a single group or, equivalently, without grouping with factors = 1: >modelofactor <- factanal(datosFCHE, factors = 1, method=”mle”

Table 2 presents a summary of the results for the groupings of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 obtained from the confirmatory factorial analysis, similar to Fig. 7 using the R language.

Table 2

Summary of results of the confirmatory factor analysis for groupings from 1 to 10.
No. factores	p-value	No. factores	p-value	No. factores	p-value
1	4.81e-29	4	2.4e-11	7	0.000148
2	5.08e-20	5	4.27e-8	8	0.00159
3	1.37e-14	6	3.42e-6	9	0.0113
				10	0.0475

From Table 2, one should seek a p-value close to 0.001. Therefore, acceptable values occur when grouped into factors: 7, 8, 9, and 10.

The result obtained, in the R language, for KMO is 0.72, indicating an acceptable outcome.

The collected sample consists of 111 participants in the exploratory part and 142 participants in the confirmatory part, ensuring a confidence level of 70% and an error rate of less than 1.12%, considering a total population of N = 220 electrical engineering students.

● SPSS Results

As shown in Fig. 9, KMO is 0.721, signifying that factorial analysis can be employed.

The Bartlett's test of sphericity should fall between 0 and 1; here it yields 0.001, which is satisfactory; hence, the factorial analysis is valid for our survey.

Next, we discuss eigenvalues greater than 1, as shown in Fig. 10, suggesting 16 components, explaining 68.125% (locate component 16, in the cumulative percentage) of the variance. If we were to reduce it to 8 components, it would still explain 47.918% of the variance, which is also acceptable.

Part 2. Extraction of components 8, 10, 19, 20, 23, 25, 43, and 4; Another factorial analysis was conducted, yielding nearly the same KMO and Bartlett's test statistics as the previous analysis, as depicted in Fig. 11.

With 14 components, it suggests a 68% cumulative percentage, but with 8, it suffices with a 51%. Therefore, the grouping of 8 is chosen.

Multiplexed CLASSROOM Management Model Proposal

Next, the result of unifying the POSTTIE strategies with the classroom management process (planning, executing, and evaluating) with content multiplexing is presented.

In Fig. 12, the content multiplexing in the planning phase is depicted, where the teacher should have at least three content items planned and prepared representing visual, auditory, kinesthetic, and reader-writer aspects, proposed as: videos, PDF notes, and end-of-class projects.

In the execution phase, the teacher's presentation, evaluation methods, and rubrics should be prepared. Classes can be conducted via platforms like Zoom, Meet, Teams, and methods for addressing queries can include email, in-person interaction, video conferencing, or other social media platforms. For the evaluation phase, it is suggested to assign simple tasks per session, enabling quick grading by the teacher and proposing slightly more challenging tasks on a weekly basis.

In Fig. 13, the multiplexing of processes is observed, where parallel processes will occur, requiring selection. For instance, during the execution phase, one could opt to show the video (specifically created for this topic by oneself or one's working group) while simultaneously addressing queries from students who have already watched the video and grading simple daily tasks. Subsequently, during the execution phase, student videos will be available, allowing teachers to grade assignments at that time.

The idea is to reduce the workload of teachers by having all the content prepared in advance. It would be beneficial if each teacher could teach topics they are passionate about or create content based on their expertise.

This proposal can work effectively not only in classroom management but also in school administration, especially if multiple teachers contribute to a single class, a concept currently known as teacher mobility by subject.

6.1 Discussion

The purpose, consequently, was to analyze the validation of the multiconcept measurement instrument (multiple meanings, not just one). Thus, the exploratory analysis was verified with the semantic analysis, and also, using ATLASti© software, results matched based on the number of repeated words. After cleaning the data and grouping the ideas, several students expressed a single (complex) meaning in different ways, for instance, organizing study to learn a university subject. The following terms had to be grouped: files, organization (space and time), planning, internet search, summarizing, highlighting notes, word associations, keyword associations, forming, supplementing, note repetition, rereading, copying. These had to be grouped repeatedly because a single word was lost in the groupings, potentially carrying significance, yet most engineering students at this university used it in a different manner, lost the grouping when reduced to a single word.

Students were asked to initially write classifications by paragraph, and then to list only the classified words, noting that using only the words could lead to incorrect regrouping according to students' "ideas." We then manually regrouped, obtained results, and began comparing them with commercial software, finding that the number of groupings varied as they were grouped only by words. For instance, "time" was important in manual analysis but disappeared in software analysis. The advantage lay in reducing the complex problem by confronting these two methods, enabling extraction of sought information without doubt.

Subsequently, with information grouped in two formats, confirmatory analysis was applied using R language and SPSS, yielding an acceptable KMO of 0.72, improved to 0.7. Bartlett's test of sphericity should fall between 0 and 1, which it does, giving 0.001, indicating the factorial analysis is valid for our survey.

Regarding the groupings found, 8 were proposed manually. In the R language software, the p-value was found to be less than 0.001, making it acceptable for 8 groupings. On the other hand, in SPSS software, there was an improvement in the proposal to group into 16 with 68%, but 8 is acceptable with 47% of eigenvalues in cumulative variance.

Thus, it was possible to test the hypothesis associating learning strategies with students' learning about a university subject.

The learning strategies followed by engineering students from Telecommunications, Computer Science, Electrical-Electronics, and Aerospace Engineering programs were 8, denoted by the acronym:

POSTTIE: Practice, Organize the work environment, Senses (reading, writing), Teamwork, Thinking or reflecting on topics, Interest or what will be the catchy topic in some way, doing Exercises.

Grouped by keywords: Having interest, Working in teams (having good organization, taking notes, discussing among themselves), thinking and using the senses (Doing practices, doing exercises).

It was confirmed in the engineering areas studied, with the strategies found, that the majority of students revert to the Kinesthetic learning style.

In the future, we hope to analyze other engineering programs with the same question and compare results.

In addition to conducting a semantic analysis for each keyword since they have increasingly extensive meanings, perhaps it would be convenient to unify the semantic network area and start making links or coincidences of information between what people generally think and what is written in articles.

Then the same question can be posed for social and humanistic study areas to determine if each group studies differently or in the same way.

A robust and fail-safe virtual classroom was successfully designed for the Electrical-Electronics, Aerospace, Computer Science, and Telecommunications programs of a public university's sixth semester. This achievement was attained through data analysis regarding how students learn a university subject. Learning strategies related to kinesthetic, auditory, and visual styles were uncovered.

As a result, the design of the virtual classroom was proposed using the POSTTIE learning strategies, allowing the instructor to create educational material not only for one learning style but for all styles. An optimal educational system should feature at least three types of materials per learning style: PDF notes, videos of up to 10 minutes, and a final project.

To utilize these diverse educational materials, the use of multiplexed classrooms is proposed, enabling students to strengthen their learning with the strategy they are most familiar with and deepen their knowledge with two additional strategies they may choose.

The multiplexed virtual classroom system is conceived as a robust, feedback-driven system with continuous corrections and improvements in content and classroom management. It can function with a single instructor or multiple instructors responsible for various tasks such as initiating, grading, evaluating, and addressing queries.

Initially, it is recommended to implement the system only for key topics requiring in-depth learning by students. This will prevent overburdening both students and instructors. Once the system is established, its everyday use can be expanded.

Reliable data analysis is available, utilizing exploratory and confirmatory analysis methods such as frequency analysis and natural semantic network technique.

In the future, it is anticipated to analyze other engineering programs as well as social and humanistic study areas to compare results and further enhance the design of the multiplexed classroom.

Funding (1) Supported

Data availability the datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics statement the studies involving human participants were reviewed and approved by Ethics Com- mittee of School. The participants provided the written informed consent to participate in this study.

Conflict of interest None.

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The authors declare no competing interests.

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Design of a multiplexed virtual classroom

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Abstract

Figures

1 Introduction

2 Literature review and research hypotheses

2.1 Classroom management

2.2 Types of classrooms

2.3 Teaching and learning methods

2.4 Learning strategy

2.5 Statistical analysis technique

3. Methods

3.1 Questionnaire design

3.2 Data collection

4. Multiplexed CLASSROOM Management Model Proposal

5. Results

5.1 Reliability and validity test

6. Discussion

6.1 Discussion

7. Conclusions

Declarations

References

Additional Declarations

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