1 Introduction

Various e-learning course designs are used for foreign language learning and teaching systems. E-learning courses aim to provide learners with required teaching and learning services [1]. Due to a lack of thorough evaluation in traditional methodologies, present methods suffer from uncertainty, which hinders the creation of effective e-learning environments. Avoiding inefficiencies and having an imperfect grasp of learner needs requires a combined strategy. On the other hand, professionals working in outlying or rural locations confront obstacles such as a shortage of relevant educational materials, unreliable internet connections, social isolation, and a shortage of qualified trainers and programs. These obstacles make it harder for them to take advantage of foreign language teaching and learning possibilities and advance in their careers. A blended learning approach designs online courses that create effective curricula for learning a foreign language [2,3,4]. The blended approach detests the essential aspects required of learners and implements them in e-learning courses. The blended approach minimizes the complexity of foreign language learning and teaching processes [5, 6]. The cloud computing system is commonly used in many fields to enhance the performance of applications [7]. Cloud-based methods and analysis are also used to teach and learn foreign languages. An effective cloud-based solution is used in courses to produce optimal services for teachers and learners [8,9,10]. The cloud-based solution predicts and solves the issues by providing relevant solutions. Cloud-based analysis is also used to analyze the appropriate data required to perform tasks in teaching processes. Cloud-based analysis enhances the efficiency of teaching systems [11, 12].

The fuzzy logic-based methods are used for the foreign language teaching assessment process [13]. A fuzzy logic-based method analyzes the information in the database, producing relevant features for the assessment process [14]. Teachers’ communication skills, content knowledge, and interaction capabilities are evaluated using fuzzy logic [15]. The fuzzy-based method minimizes the energy consumption level in assessment and evaluation processes. The fuzzy logic-based assessment method increases the accuracy of evaluation, providing adequate information for the performance improvement process [16, 17]. Fuzzy logic-based methods are also used for foreign language learning assessment systems [18]. A fuzzy logic method based on strengths, weaknesses, opportunities, and threats (SWOT) analysis is used for learning assessment. The SWOT analysis predicts the exact learning abilities of students in foreign languages [19]. The SWOT analysis produces optimal data for learning assessment, effectively impacting the learning process [20]. The SWOT analysis identifies the difficulties which are faced by students during the learning process.

The fuzzy logic method minimizes the complexity of the assessment process [21]. Decision-making systems play a significant part in improving user experiences in data mining. E-learning’s development as a forward-thinking approach to online education contrasts with the more traditional model of face-to-face instruction in its focus on accommodating a wide range of student backgrounds [22, 23]. While many students can benefit from online education, the wide range of students means that standard teaching methods cannot always be applied. Commonly employed to deal with this issue, recommender systems include drawbacks such as longer query processing times and lower accuracy. It highlights the requirement for developing improved recommendation systems for online education.

Personalized recommendation systems need improvement as online learning platforms proliferate. Existing recommender systems have problems, including inconsistent absolute error predictions and skewed recommendations. Because of these holes, learners are hindered in their capacity to use this knowledge and make good decisions. Moreover, the present methods do not sufficiently address the various learning behaviors and preferences within varied student groups. This study proposes a cutting-edge hybrid recommender system that utilizes transductive support vector machines and fresh optimization techniques to fill these voids.

The research proposes a novel approach for building adaptive foreign language e-learning courses. It unites cloud computing, fuzzy logic, and referenced analysis. Cloud computing is used for multimedia-driven course creation, adaptability and understanding are assessed, and successful courses are used to guide new designs. The method optimizes course recommendations and incorporates mathematical formulas for incremental learning.

This study focuses on developing and evaluating a novel hybrid personalized recommender system for online education [24]. The inherent ambiguity in teaching and learning foreign languages impedes the development of efficient online classrooms. Quantum iterative learning control (ILC) is the subject of study of Tao et al. [25], who employ an encoding and decoding technique. The uniform quantizer and the encoding and decoding process work together to drastically reduce network strain and the impact of quantization errors on system monitoring efficiency. The Bernoulli random variable model describes data dropouts and a gradient-based ILC law is created simultaneously. The asymptotic zero-error tracking performance for the uniform quantizer has been convincingly established for this learning system. As it stands, current recommender systems suffer from issues including biased suggestions and unpredictable absolute error forecasts. Better dynamic event-triggered mechanisms (DETM) with proposed security controls in linear parameter-varying partial differential equation systems are discussed in [26]. When estimating the complex terms of the systems under study, the Takagi-Sugeno fuzzy model is used. The study suggests a new idea for constructing adaptable online language classes. Referenced analysis, fuzzy logic, and cloud computing are all combined. In [27], the problem of controlling the performance of switched nonlinear systems in the face of unpredictable external disturbances and performance requirements is discussed in terms of composite adaptive fuzzy finite-time prescriptive performance control. The nonlinearity’s approximation accuracy is improved by compensating for prediction errors in conjunction with the piecewise shifted composites parameter update rule. This system employs a transductive support vector machine-based strategy to make informed and tailored student recommendations. The suggested method uses cutting-edge tools, including a modern transductive support vector machine, to accurately assess habits and interests and optimize optimization procedures to boost efficiency. The ultimate objective is to improve recommendation quality, eliminate the drawbacks of current recommender systems, and give students access to more relevant and individualized educational opportunities. This study aims to improve the efficacy and efficiency of e-learning platforms and contribute to the field of personalized recommendation systems. Therefore, this study proposes a “Cloud-assisted Course Design using Referenced Analysis” that presents a comprehensive technique using cloud-based resources, fuzzy logic, and reference analysis. This novel combination fills crucial gaps, most notably the inability to adapt to and understand languages other than one’s own. This method improves uniformity and verifies the effectiveness of the suggested method compared to existing alternatives by providing representation recommendations and applying source-dependent quantitative and graphical analysis.

The benefits of this study include suggesting personalized course designs, using fuzzy logic for assessment precision, and using resources in the cloud for language adaptation. Enhancing E-learning platforms through integrating cloud technology, fuzzy logic, and bibliometrics is one of the unique points. This study closes a knowledge gap by discussing the importance of adaptive language learning methods and customized recommendation systems for distance learning.

The proposed Cloud-assisted Course Design using the Referenced Analysis method offers several advantages, including fuzzy logic for language adaptability, leveraging cloud resources to prevent repetitive information, translating recommendations into representations for validating information and using most adaptable references from prior iterations to enhance new course design, thereby ensuring course reachability and minimizing extraneous data.

This approach marks a significant step forward in developing innovative strategies for teaching and learning a second language.

The contributions are listed below:

  • Introducing a Cloud-assisted E-learning Course Design Analysis method for improving foreign language adaptability and understandability

  • Providing representation recommendations for interactive course design with high adaptability to different language students

  • Performing a source-dependent statistical and graphical analysis for consistency check

  • Performing a comparative analysis study to verify the proposed method’s efficiency with the other existing methods

This section describes the ever-changing landscape of language instruction, which features innovative approaches like blended and online learning that aim to reach students from a wide range of demographics. Educators and students alike have benefited greatly from the increased efficiency of cloud computing technologies in the classroom. Methods based on fuzzy logic reduce power consumption and improve the precision of evaluations. There is a need for cutting-edge recommendation systems specifically designed for online education because traditional teaching methods have difficulty adapting to various student backgrounds. Personalized suggestions are provided using a hybrid recommender system that employs transductive support vector machines and optimization methods. Combining cloud-based resources with fuzzy logic and reference analysis, the “Cloud-assisted Course Design using Referenced Analysis” technique ensures consistent and effective interactive course design across varied language contexts by addressing language adaptation and understanding gaps. The findings of this study led to the development of effective and efficient e-learning platforms and personalized recommendation systems.

This paper is structured as follows: Sect. 2 describes the background study of this research. Section 3 discusses the proposed Cloud-assisted Course Design using the Referenced Analysis model. The results and discussion are illustrated in Sect. 4, and finally, the paper concludes in Sect. 5.

2 Related Works

Yanes et al. [28] developed a prospects identification method using a fuzzy logic technique for foreign language learning through game theories. Strength, weakness, opportunities, and threats (SWOT) analysis is used here to predict the essential data for the identification process. The developed method maximizes the accuracy in prospect identification, enhancing the learning systems’ efficiency range. It combines fuzzy logic and game theory to improve prospect identification’s accuracy and learning efficiency in language acquisition. Could potentially simplify or ignore complex aspects that impact the results of language acquisition. Wijayanti et al. [29] introduced a new investigation method for bilingual word embedding in automatic text summarization. The main aim is to investigate bilingual work methods for the English language learning process. A multilingual unsupervised and supervised embedding (MUSE) is used here for comparison. BiVec’s joint methods fared better than competing approaches in the study’s intrinsic evaluation, particularly in CSLS retrieval. Streamlines the process of learning English by introducing MUSE, a system for bilingual word embedding. It uses embedding techniques extensively, which can call for a lot of power and knowledge from the computer.

Carbajal-Carrera [30] proposed a challenge identification method in videoconferencing language learning systems. The proposed method identifies the issues and problems the learner faces during videoconferencing. The proposed method improves the performance range of remote learning systems. The study’s findings shed light on serious difficulties that arose during the shift from in-person to online education in the wake of the epidemic. Enhances comprehension of the classroom environment by foretelling student activism through interactional practices. The unresolved adaption issues occur when moving from traditional classroom instruction to online learning. Tsuboi and Francis [31] designed a new bilingual enhancement effect for the foreign language learning process. The developed model is mainly used to improve the bilingual proficiency level of the learners. This research casts doubt on the belief that bilinguals have an innate advantage when acquiring a foreign language’s vocabulary, instead highlighting the importance of one’s cognitive abilities. Using efficient learning methodologies and decreased computational energy consumption improves multilingual proficiency. The results cast doubt on the idea of a natural benefit to being multilingual and point to the possibility of disparate personal outcomes. Li et al. [32] proposed a problem-based learning model for English teaching systems. The proposed model is commonly used for the natural language processing (NLP) teaching process using significant data text summarization. The Student Perceptions of Opportunities for Learning (SPOT) survey results show that our PBL approach piques students’ curiosity about and enthusiasm for problem-solving in computer science. Computer science students’ interest, curiosity, and excitement for solving problems are piqued by the PBL approach. The processing and integration of big data into educational systems necessitate a robust infrastructure.

Poláková [33] developed a mobile learning application-based teaching method. The main aim of the developed method is to improve the learning process for foreign vocabulary. Both qualitative and quantitative methods are used to examine the vocabulary range of the learners. The developed mobile learning application maximizes the effectiveness and feasibility range of the systems. Experimental results show that the developed method improves the quality of experience (QoE) ratio in learning, improving the learners’ proficiency level—positive student perceptions of MALL, showcasing its possible advantages. There may be limitations on the app’s use and capabilities due to its technical features. Klimova [34] introduced an investigation method for virtual reality (VR) in non-native language learning and teaching (NLLT) systems. The VR provides effective learning services to language learners, minimizing the difficulties in learning. The VR-based NLLT enhances students’ overall performance and academic growth. The study reaches a high level of verification accuracy in the use of virtual reality technology for language learning. Despite their usefulness, several obstacles can prevent virtual reality technologies from being widely used in NLLT. Brena et al. [35] designed an automated evaluation approach using machine learning (ML) for foreign language performance. The actual goal of the approach is to evaluate the speaking skills and abilities of the students. A supervised ML method is used here to identify the students’ pronunciation and fluency rates. The designed approach improves the accuracy of the evaluation process, enhancing the efficiency level of the foreign language learning process. The research makes supervised ML-based speech evaluation more accurate, which speeds up language acquisition. The dependency on machine learning models can require regular upgrades and calibration to ensure accuracy.

Frances et al. [36] proposed a new vocabulary-learning technique for foreign and native languages. The proposed technique explores contextual diversity’s actual effects during language learning processes. Contextual diversity is evaluated, such as the number of texts presented in the context and their meanings. The impact of ethnicity and language on performance on a matching task was investigated using a mixed-model ANOVA. Determines the most practical and trustworthy ways to study the effects of contextual diversity on language acquisition. Application in educational contexts may be complicated due to the need for extensive investigation of the impact of contextual diversity. In learning systems, Lee and Ye [37] developed a new investigation method for the foreign language classroom anxiety scales (FLCAS). A generalizability theory (G theory) is used here to predict the exact reliability level of FLCAS. Multiple types of research using factor D indicated the highest reliability was achieved with 7 items each for communication anxiety, test-related anxiety, and fear of unfavorable assessment—advances above conventional techniques in anxiety assessment dependability. The reliability and validity of anxiety assessment tools could be compromised by subjectivity. Barros-del Río et al. [38] presented practicum management using an online tool for foreign language education systems. The proposed management is an interactive digital notepad (IDN) prototype that reduces the complexity of the foreign language learning process. The proposed method enhances foreign language learning systems’ performance and reliability levels. Using an IDN prototype makes learning a foreign language more accurate and efficient. It may fail to consider particular educational settings and differing real-world situations.

Liang [39] introduced a long short-term memory (LSTM) model using the internet and multimedia (MM) environments for foreign language teaching. The LSTM model predicts the effects of MM on the learning process. Experimental results show that the introduced method maximizes accuracy in decision-making, improving the learning systems’ performance. Determines the availability of resources that are critical for successful learning protocols. Dependence on MM environments could hinder scaling due to the significant resources required. Saleem et al. [40] analyzed whether or not there is a correlation between the variables retrieved from electronic learning administration systems and student performance. The steps in the process are as follows: gather data, clean and organize it, pick and engineer features, select a model with five classic ML algorithms, train and evaluate the model using ensemble methods, and finally, consider the model. Educators are helped by their higher predictive abilities compared to traditional techniques. Expertise in data engineering may be necessary for managing and understanding information retrieved from e-learning platforms.

El Fazazi et al. [41] developed a transductive support vector machine-based hybrid personalized recommender system for online education environments. Data collection, data preparation, improved clustering technique, TSVM evaluation, modified anarchical society optimization, hybrid recommender design, simulation, and evaluation are all parts of the methodology. Improves the efficiency and quality of online learning by creating individualized recommendation systems. Dependent on intricate TSVM and clustering algorithms, which may restrict scalability, the subject of non-technical issues is not explored. Nebot et al. [42] proposed an online learning toolbox that employs fuzzy inductive reasoning (FIR) for data mining with fuzzy logic. The extraction of logical rules about students’ pedagogical practice is made possible by add-ons like Causal Relevance and Linguistic Rules Extraction. Improves online learning pedagogy and behavior among learners, analysis with the FIR toolset for fuzzy logic data mining. It depends on Latin American datasets, limiting generalizability; long-term academic performance effects are poorly investigated.

Fuzzy logic, game theories, multilingual embedding, and machine learning techniques are only a few of the methods found to be effective in improving second-language acquisition and have been extensively discussed in the literature on second-language teaching and learning. However, knowledge gaps exist regarding the multi-faceted nature of learning a foreign language, which involves the acquisition of vocabulary and grammar and the development of communicative competence. There is also a need to know how these approaches affect students from a wide range of linguistic and cultural backgrounds with different cognitive strengths and weaknesses. Referenced analysis in the cloud is proposed to fill these knowledge gaps. Adaptability, interactivity, and user engagement in e-learning platforms are all improved by this method’s incorporation of cloud computing, fuzzy logic, and bibliometrics. By taking this tack, e-learning platforms can fill in the gaps in the current body of knowledge and provide a more all-encompassing, flexible, and learner-centric setting for foreign language instruction.

This research is significant since there is a lack of research regarding personalized recommendation systems and adaptive language learning procedures in E-learning. This study demonstrates that a more adaptable and learner-centered setting for language classes can be achieved by blending cloud computing concepts, fuzzy-based logic, and bibliometrics. This study successfully addresses the limitations of previous research by providing students from different cultural backgrounds with adaptive teaching approaches and personalized recommendations. The novel method taken by this study emphasizes the significance of improving e-learning systems for language acquisition by strengthening language adaptation, assessment precision, and the development of courses in online learning.

3 Method

3.1 Introducing the Data Source and Considerations

This section introduces the design of the foreign language learning course using cloud resources. In this process, the data from https://data.world/garyhoov/us-language-spoken-at-home is accounted for identifying information repetition, inconsistency, etc. These data are utilized for designing the course, as represented in https://vertabelo.com/blog/a-language-school-database-model/. The course design model is illustrated in Fig. 1 based on the second source.

Fig. 1
figure 1

Course design model illustration

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The inputs from the course structure are utilized to generate novel representations in several dimensions, which are then refined based on readability and malleability. The “margin of error” is applied to evaluate comprehension and flexibility, pinpointing problem spots. By providing a portal via which students can access course materials, interact with their peers and teachers, and monitor their progress in a digital environment, e-learning windows in the architecture play a critical role in enabling distant and flexible learning experiences. Several sources are drawn upon to improve curriculum blueprints, and the frequency with which specific components recur is calculated, all of which contribute to iterative development. This method enhances the education process by pinpointing places where alterations are required.

3.2 Cloud-Assisted Course Design Using Referenced Analysis

Cloud Computing is the organization of computer maintenance such as reminiscences, assistants, data warehouses, arrangements, and many more through the Internet, preferably using a corporeal repository for productive and more expert performance. Cloud-based services are responsible for satisfying such learning requirements through correlational and multimedia-based course design. This method introduces a Cloud-assisted Course Design using Referenced Analysis to accommodate multi-level enhancements in the e-learning platforms. An E-Learning platform is the freight of learning and tutelage through digital resources. Although eLearning depends on approved learning, it is executed through electronic devices such as computers and digital resources connected to the Internet. It also helps the students to learn multiple subjects according to their interests. Foreign language teaching and learning needs students’ multi-level words, sentences, grammar knowledge, and interests. The E-learning-based course design satisfies these creative and communicative teaching and learning factors. Foreign language education uses cloud computing and the e-learning platform to teach students an unofficial modern language. Fuzzy logic is a method of variable operation that permits conglomerates feasible truth values to be operated through the same variable. Fuzzy logic involves resolving problems with a patulous, counterfactual array of information and interrogatives that makes it attainable to determine a range of precise decisions. Course design is the procedure and approach to innovating standard learning environments and proficiencies for students. Through intentional and meticulous exhibits of commandment resources, learning enterprises, and communication, students can ingress information, attain learning skills, and practice higher thinking and innovation abilities. The maximum representations and identified resources are tabulated in Table 1 based on the data provided from the different sources.

Table 1 Source and representations
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The representations in Table 1 are extracted from the first data source based on foreign languages practiced by the students at home. These contextual data are used for curriculum design on any e-learning platform. The curricula and the educational resources are required for the fuzzy evaluation process based on the previous references to the procedures. The references are the ones that have the maximum adaptability of the previously designed courses. From there, the non-repetitive resources are eliminated, and then the adaptability and understandability of the students in the foreign language educated by the teachers are determined (Table 1). Depending on these outputs, the references are executed for teaching a foreign language session based on the previous e-learning success. Based on the preferences, the course is designed for cloud computing with the help of the e-learning platform. In the course design process, repetitive information is accredited to maximize understandability and communication through different teaching representations. The fuzzy process estimates the saturated adaptability for precluding temporary course design and determining maximum reliability in cloud recommendations. This method helps increase adaptability and avoid repetitions in reference production. The curriculum required for the e-learning platform is considered for the fuzzy logic procedure for identifying the references for the course design. The educational resources helpful in teaching a foreign language to students are also considered in the fuzzy logic operation. Depending on the curriculum and the educational resources, the fuzzy evaluation process is used to determine the previous references and the non-repetitive references to identify the course design.

The curricula of the e-learning platform have the objectives and the framework for the training session to encompass the target with the standards. The educational resource contains the course materials, software, and the requirements of the students in learning a foreign language. The curriculum and the educational resources play a vital role in the fuzzy logic concept, determining the previous references and eliminating the repetitions in the references. The educational curriculum and the resources help design the foreign language course with maximum reach for the students. It also helps increase the students’ understanding of foreign language courses. These outcomes also help extract new references when designing the course for the students without repetition. The process of removing the educational resources and the curriculum of the e-learning platform for the fuzzy logic concept is explained by the following equations (1) and (2) given below. These equations describe a systematic strategy for developing language courses by integrating available tools, desired outcomes, and fuzzy logic methods. E-learning platforms aim to improve students’ flexibility and comprehension of foreign language instruction.

$$\begin{aligned} & \left. {\begin{array}{ll} X_{1j}=Y_{1j}*U_{1j }j=1,2,\\ X_{2j}=Y_{2j}*U_{2j } j=1,2,3,4,\\ \end{array} } \right\} \end{aligned}$$
(1)
$$\begin{aligned} & \left. {\begin{array}{ll} U_{1}=\left[ {\begin{array}{ll} Y_{11}^{T} & Y_{12}^{T}\\ \end{array} } \right] ^{T}\\ U_{2}=\left[ {\begin{array}{ll} Y_{21}^{T} & Y_{22}^{T} {\begin{array}{ll} Y_{23}^{T} & Y_{24}^{T}\\ \end{array} }\\ \end{array} } \right] ^{T}\\ {\begin{array}{ll} Y_{i}=W_{i}*U_{i}\\ U=[{\begin{array}{ll} Y_{1}^{T} & Y_{2}^{T}] \\ \end{array} }^{T}\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(2)

where X is denoted as the identification of the curriculum of the e-learning, Y is represented as the educational resources, T is denoted as the combination of the educational resources and the curriculum, U is described as the course materials in the resources, W is denoted as the objectives of the course in the curriculum, i is denoted as the framework of the procedure in making the course design, and j is represented as the software presently used in the educational resources in creating the course. Now, the previous references are extracted for a fuzzy logic procedure. Here, the outcome of the last course, which has the maximum adaptability, is considered the reference for the fuzzy logic procedures. After the teaching session, it helps determine the students’ maximum adaptability and understandability in the foreign language. It is used to determine the reach of the course to the students and their interests in the designed course. The previous course outcomes are considered in the fuzzy logic to avoid mistakes and to enhance the part with some lags during the course design process. The outcome of the previous course design processes helps identify whether the teaching session has maximum adaptability for students in foreign languages. Then, the output with maximum adaptability is used in the fuzzy logic concept to enhance the course design. The template of the proposed method is illustrated in Fig. 2.

Fig. 2
figure 2

Proposed method template

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The previous reference with maximized versatility helps the fuzzy concept determine the identified preferences’ non-repetitions. The outcome of the last reference, the educational curriculum, and the resources are combined in the fuzzy concept to estimate the students’ adaptability and understandability in the designed foreign language course. It also helps in developing the course with the maximum outcome of the previous references and then estimating the reach of the course to the students. These outcomes of the educational resources and the e-learning curriculum help in the fuzzy logic along with the previous course design references to determine the productive reference for the course design (Fig. 2). The process of determining the earlier references for the fuzzy concept procedure is explained by the following equations (3) and (4) given below. Each of these equations has a specific function, though they help educational program designers make the most of available data, information, and tools. Effective foreign language instruction can be achieved by integrating fuzzy logic into academic materials and online curriculum design

$$\begin{aligned} & \left. {\begin{array}{ll} \eta =W*U\\ U_{ij}<0<1\\ {\begin{array}{ll} where,\\ U_{ij}=\frac{(\sum \limits _{ij} {\left( U \right) -\eta )} }{(\eta -1)}\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(3)
$$\begin{aligned} & \left. {\begin{array}{ll} \theta _{j}^{K}=\sum \nolimits _{i=1}^n {P_{j}^{K}\left( x_{ij} \right) .U_{ij}} \\ where,\\ \left[ {\begin{array}{lll} \frac{\theta _{1}^{1}}{\sum \limits _{k=1} \theta _{1}^{K} } \frac{\theta _{1}^{2}}{\sum \limits _{k=1} \theta _{1}^{K} } {\begin{array}{ll} \frac{\theta _{1}^{3}}{\sum \limits _{k=1} \theta _{1}^{K} } \frac{\theta _{1}^{4}}{\sum \limits _{k=1} \theta _{1}^{K} }\\ \end{array} }\\ \frac{\theta _{2}^{1}}{\sum \limits _{k=1} \theta _{2}^{K} } \frac{\theta _{2}^{2}}{\sum \limits _{k=1} \theta _{2}^{K} } {\begin{array}{ll} \frac{\theta _{2}^{3}}{\sum \limits _{k=1} \theta _{2}^{K} } \frac{\theta _{2}^{4}}{\sum \limits _{k=1} \theta _{2}^{K} }\\ \end{array} }\\ \frac{\theta _{n}^{1}}{\sum \limits _{k=1} \theta _{n}^{K} } \frac{\theta _{n}^{1}}{\sum \limits _{k=1} \theta _{n}^{K} } {\begin{array}{ll} \frac{\theta _{n}^{1}}{\sum \limits _{k=1} \theta _{n}^{K} } \frac{\theta _{n}^{1}}{\sum \limits _{k=1} \theta _{n}^{K} }\\ \end{array} }\\ \end{array} } \right] \\ \end{array} } \right\} \end{aligned}$$
(4)

where \(\eta \) is represented as the previous references, \(\theta \) is denoted as the references with maximum adaptability, P is denoted as the last design course, and K is denoted as the outcome of the previous cloud resources. The fuzzy concept occurs with educational resources e-learning curriculum, and prior references. The reference that has the highest adaptability is used in the fuzzy logic process to determine the efficaciousness of the teaching session of the foreign language to the students. It also helps identify the student’s interests, and the course reaches them. From this, the repetitive references are eliminated to enhance the efficiency of the course design in cloud computing using the e-learning platform. The educational resources and the e-learning curriculum play a vital role in this fuzzy process, as they help check the present requirements of the course design procedures and maximize the process’s adaptability. The adaptability is the output of the previous course design estimated from 20 sessions, as presented in Table 2.

Table 2 Adaptability output for 20 sequences
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The fuzzy logic operation helps in producing precise information on the acquired resources and the e-learning curriculum. Thus, it aids in producing references for the course design process. It considers previous references to eliminate unwanted content and interactions during the course design procedures. The outcome of the fuzzy process aids in determining the adaptability and understandability of the course design to the students (Table 2). The course teaches the student its efficiency in undemanding the courses without any mistakes, which are also identified using the fuzzy logic concept’s outcomes. The process of fuzzy concept with the educational resources and the curriculum, along with the maximum adaptability reference of previous course design,is explained by Eqs. (5) and (6) given below. These equations, taken as an ensemble, provide a basis for employing fuzzy logic in evaluating the flexibility and comprehension of language learners undergoing formalized foreign language instruction. The technique tries to reveal how well students understand and apply the language curriculum by quantifying membership degrees within specific intervals. The values define intervals of interest, and the resulting values are used to assess how well the instructional materials and course structure are helping students develop their language skills and knowledge.

$$\begin{aligned} & \left. {\begin{array}{ll} P_{j}^{1}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin \left[ \alpha _{1}+\eta \right) \\ \frac{x-\alpha _{1}}{1-\alpha _{1}} & x\in [\alpha _{1},\eta )\\ 1 & x\in [1+\eta )\\ \end{array} } \right. \\ P_{j}^{2}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin \left[ \alpha _{1}+1 \right) \\ \frac{x-\alpha _{2}}{\alpha _{1}-\alpha _{1}} & x\in [\alpha _{2},\alpha _{1})\\ \frac{1-x}{1-\alpha _{1}} & x\in [\alpha _{1},1)\\ \end{array} } \right. \\ {\begin{array}{ll} P_{j}^{3}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin \left[ \alpha _{1},\alpha _{1} \right) \\ \frac{x-\alpha _{3}}{\alpha _{2}-\alpha _{3}} & x\in [\alpha _{3},\alpha _{1})\\ \frac{\alpha _{1}-x}{\alpha _{1}-\alpha _{2}} & x\in [\alpha _{2},\alpha _{1})\\ \end{array} } \right. \\ P_{j}^{4}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin \left[ -\eta ,\alpha _{2} \right) \\ \frac{\alpha _{2}-x}{\alpha _{1}-\alpha _{2}} & x\in [\alpha _{3},\alpha _{2})\\ 1 & x\in [-\eta ,\alpha _{3})\\ \end{array} } \right. \\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(5)
$$\begin{aligned} & \left. {\begin{array}{ll} \psi _{j}^{K}=\frac{\sum \limits _j^K {(\eta )} }{\sum \limits _{j=1} \psi _{j}^{K} }\\ \psi _{j}^{1}=\frac{\sum {(\eta )} }{P_{j}^{1}\left( x \right) +P_{j}^{2}\left( x \right) +P_{j}^{3}\left( x \right) +P_{j}^{4}\left( x \right) }\\ \psi _{j}^{1}=\frac{\sum {(\eta )} }{K_{j}^{1}\left( x \right) +K_{j}^{2}\left( x \right) +K_{j}^{3}\left( x \right) +K_{j}^{4}\left( x \right) }\\ \end{array} } \right\} \end{aligned}$$
(6)

where \(\psi \) is denoted as the outcome of the fuzzy concept, \(\alpha \) is represented as the unnecessary contents and intentions in the course designing process. The fuzzy concept helps determine the student’s understanding and adaptability to the foreign language. Adaptability denotes the reach of the students in terms of foreign language training with the necessary educational curriculum and resources. The understandability represents the efficiency of the students in understanding the language within the period after the language training session. The fuzzy process for adaptability and understandability verification is illustrated in Fig. 3.

Fig. 3
figure 3

Adaptability and understandability verification

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The adaptability and understandability help estimate the efficacious references to the upcoming course design procedure. These outputs can enhance the course design process and execute the course’s precise information for students in cloud computing using the e-learning platform. From the adaptability and understandability of the students, references are made to the upcoming course development procedures and also, based on the previous success of the e-learning platform, helps in the course management operation (Fig. 3). The process of extracting the adaptability and understandability of the students about the foreign language is explained by the following equations (7) and (8) given below. Equations (7) and (8) are meant to provide a structure for evaluating students’ flexibility and comprehension in the context of second-language acquisition. The equations here allow for the extraction of numerical indicators of these characteristics. Educators and course designers can use these metrics to create lessons uniquely suited to their students’ needs and skill levels, optimizing their time spent studying a language.

$$\begin{aligned} & \left. {\begin{array}{ll} K_{j}^{1}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin (-\eta ,\beta _{2}]\\ 1 & x\in (-\eta ,\beta _{1}]\\ \frac{\beta _{2}-x}{\beta _{2}-\beta _{1}} & x\in [1+\eta ]\\ \end{array} } \right. \\ K_{j}^{2}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin (\beta _{1},\beta _{3}]\\ \frac{x-\beta _{1}}{\beta _{2}-\beta _{1}} & x\in (\beta _{1},\beta _{2}]\\ \frac{\beta _{3}-x}{\beta _{3}-\beta _{2}} & x\in [\beta _{2},\beta _{3}]\\ \end{array} } \right. \\ {\begin{array}{ll} K_{j}^{3}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin (\beta _{2},1]\\ \frac{x-\beta _{2}}{\beta _{3}-\beta _{2}} & x\in (\beta _{2},\beta _{3}]\\ \frac{1-x}{1-\beta _{3}} & x\in [\beta _{3}+1]\\ \end{array} } \right. \\ K_{j}^{4}\left( x \right) =\left\{ {\begin{array}{ll} 0 & x\notin (\beta _{3}+\eta ]\\ \frac{x-\beta _{3}}{1-\beta _{3}} & x\in (\beta _{3},1]\\ 1 & x\in [1,\eta ]\\ \end{array} } \right. \\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(7)
$$\begin{aligned} & \left. {\begin{array}{ll} \lambda _{i}^{'}=\frac{\eta _{i}^{'}-1}{P}\\ where \{(\lambda _{i}\lambda _{i}^{'})\vert \lambda _{i}^{'}>x\}\\ {\begin{array}{ll} \lambda _{i}=\frac{\eta _{i}-1}{P}\\ where \{(\lambda _{i}\lambda _{i}^{'})\vert -x+\lambda _{i}\le \lambda _{i}^{'}\le x\}\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(8)

where \(\beta \) is the adaptability level of the students and \(\lambda \)is denoted as the understandability of the students of the foreign language. Based on this adaptability and understandability, the references are made for the course design in cloud computing with the help of the e-learning platform. The success of the previous e-learning process helps make the references for the course design. The fuzzy output for understandability and adaptability for the languages considered from the data source is presented in Table 3.

Table 3 Fuzzy output for understandability and adaptability
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In Table 3, the \(\beta \) and \(\lambda \) for the increasing\( \eta \) are tuned using fuzzy decisions. At the initial stage, as the fuzzy optimization relies on\( y \forall T=X+Y\), the \(\eta \) is the only reference for\( K\) As the session increases, the need for reducing repetitions increases, for which W is required. Across the various \(\left( K,P \right) \) combinations of the \(\alpha \) mitigation using \(U\in Y\) are validated, preventing inconsistencies. Therefore, the adaptability increases with high reference counts. The outcomes of the fuzzy logic concept with the educational resources, educational curriculum, and the previous responses help manufacture the new references for the course design operation in the cloud with the help of the e-learning platform. The process of forging references for the upcoming course design based on the successful previous e-learning platform is explained by the following equations (9) and (10) given below. Equations (9) and (10) are meant to be used in quantitatively analyzing the features and efficacy of a prior e-learning process to guide the design of a subsequent course. Educators and course designers can incorporate lessons learned and what worked well for the previous iteration of the course by analyzing the success and variability metrics.

$$\begin{aligned} & \left. {\begin{array}{ll} {\overline{X}}_{Ti}=\frac{1}{n}\times \sum \limits _{j=1}^n X_{ij} \\ G_{ij}=\sqrt{\frac{1}{n}\times \sum \limits _{j=1}^n {{(X}_{ij}-{{\overline{X}} _{Ti})}^{2}} } \\ {\begin{array}{ll} H_{ij}=\frac{1}{n}\times \sum \limits _{j=1}^n Y_{ij} \\ G_{ij}^{'}=\sqrt{\frac{1}{n}\times \sum \limits _{j=1}^n {{(H}_{ij}-{{\overline{H}}_{Ti})}^{2}} } \\ {\begin{array}{ll} \overline{\theta }_{i}=\frac{1}{n}\times \sum \limits _{j=1}^n G_{ij} \\ H_{i}=\sqrt{\frac{1}{n-1}\times \sum \limits _{j=1}^n {{(\theta }_{ij}-{\overline{\theta }_{Ti})}^{2}} } \\ \end{array} }\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(9)
$$\begin{aligned} & \left. {\begin{array}{ll} \phi _{i}^{*}={\overline{X}}_{Ti}\\ \phi _{j}^{*}={\overline{Y}}_{Ti}\\ {\begin{array}{ll} \varsigma _{i}^{*}=\phi _{i}^{*}-\phi _{j}^{*}={\overline{Y}} _{Ti}-{\overline{X}}_{Ti}={\overline{G}}_{i}\\ \theta \left[ \phi _{i}^{*} \right] =\theta \left[ {\overline{X}}_{Ti} \right] =\theta _{i}\\ {\begin{array}{ll} \theta \left[ {\overline{Y}}_{Ti} \right] =\theta _{i}^{'}\\ \theta \left[ \varsigma _{i}^{*} \right] =\left[ {\overline{G}}_{i} \right] =\varsigma ^{i}\\ \end{array} }\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(10)

where G is denoted as the references for the course design process, H is represented as the previous successful e-learning process, \(\phi \) is denoted as the outcome of identification of the reach of the foreign language among the students, and \(\varsigma \) is expressed as the results of the references with the precise information about the previous process. The references exploited from the resources are analyzed in Fig. 4.

Fig. 4
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References analysis

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The\( \eta \) analysis for 7 different foreign languages is presented in Fig. 4. The study relies on the distinguishable recommendations and representations provided. The design objectives increase the understandability based on the previous adaptability. The previous\( \theta \) and \(K\in P\)increase the chance of\( T\) utilization for a successful course design. The evaluations based on e-learning recommendations are used for eliminating\( \alpha \) and maximizing \(\beta \) by identifying precise\( G\in i\). It is to be considered that the number of sources and learners’ density influences the \(\eta \) for K generation (Fig. 4). Based on the references, the course on a foreign language is designed for cloud computing by an e-learning platform. It also helps enhance the students’ adaptability and understanding of the new references. Repetitive information is avoided to increase the students’ adaptability and efficacious understandability. The fuzzy process identifies the saturated adaptability necessary for preventing temporary course design and determining maximum authenticity in cloud suggestions. The method of designing the course based on the productive references in cloud computing with the e-learning platform is explained by Equations (11–13) below. Applying statistical measures and estimated values to drive the course design process, Equations (11–13) aim to improve students’ adaptation and comprehension in a second-language classroom. The equations are considered valuable references, and the results of the prior e-learning process suggest a more efficient course layout.

$$\begin{aligned} & \left. {\begin{array}{ll} F_{i}=\frac{\sqrt{n} (\varsigma _{i}^{*}-\alpha _{1}\alpha _{2})}{\beta _{i}}\\ n\left( 0,1 \right) for n\rightarrow \infty ;\\ {\begin{array}{ll} F_{i}=\frac{\sqrt{n} (\varsigma _{j}^{*}-\beta _{1}\beta _{2})}{\alpha _{i}}\\ \varsigma _{j}^{*} and G_{i}\\ n\left( 0,1 \right) for n_{1}\rightarrow \infty ;\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(11)
$$\begin{aligned} & \left. {\begin{array}{ll} A_{p}=\frac{\left\{ \left( x,\alpha \right) \vert {Z_{\beta }-Z_{\frac{\alpha }{2}}\le x\le Z_{\beta }-Z_{\frac{\alpha }{2}},1\le x\le 1}\right\} }{F_{i}}\\ A_{U}=\frac{\left( Z_{\beta }+Z_{1} \right) -(Z_{\beta }-Z_{1})}{G_{i}}\\ {\begin{array}{ll} =\frac{2\times Z_{1}}{G_{1}}=\sum \limits _{ij} {(2Z_{1})} \\ A_{ij}=\frac{\left\{ \left( x,\alpha \right) \vert {Z_{\beta }-Z_{\frac{\alpha }{2}}\le x\le Z_{j},1\le \alpha \le \beta }\right\} }{F_{i}*G_{i}}\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(12)
$$\begin{aligned} & \left. {\begin{array}{ll} \tau ^{*}=\frac{1}{n}\times \sum \limits _{j=1}^n x_{ij} \\ G_{i}={\sqrt{\frac{1}{n-1}\times \sum \limits _{j=1}^n \left( \varsigma _{ij}-\varsigma _{ij}^{*} \right) ^{2} }} \\ {\begin{array}{ll} Z_{i}=\frac{\sqrt{n} (\varsigma _{ij}^{*}-1)}{G_{i}}\\ for i=1,2, \ldots ,n\\ \end{array} }\\ \end{array} } \right\} \end{aligned}$$
(13)

where F is represented as the outcome of the productive references, Z is denoted as the course designing procedure with the given references, and A is denoted as the increased adaptability. This process helps enhance adaptability by eliminating repetitions in the references. Also, this procedure reduces the inconsistency and the recommended time for the course design process. Based on the previous successful e-learning process, recommendations are given for the course design process to enhance the adaptability and understandability of the students after taking the foreign language course. Finally, the average inconsistency observed for the different languages and\( \eta \) values is tabulated in Table 4.

Table 4 Inconsistencies for different \(\mathbf {\eta }\)
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The average inconsistency identified for the sessions and languages is presented in Table 4. The inconsistency is high for fewer resources due to fewer references and adaptability. Based on\( G\), the H and \(\phi \) are consistently verified for\( \beta \) maximization. This process is validated using P and T such that\( X+y\) generates high\( G\).

Essential technologies for providing online access to computing resources include cloud computing and e-learning platforms. Cloud-assisted course design aims to improve e-learning platforms by analyzing references to better serve students at varying levels of knowledge acquisition. Fuzzy logic is utilized for problem-solving in situations where the truth values of a variable operation are ambiguous. To create a curriculum and online learning resources, course designers must gather representations and information about the target languages students will learn from various sources. The concepts of adaptability and understandability are introduced in the text, emphasizing whether course content is appropriate for students and how well students grasp the subject. Equations and calculations are used to apply fuzzy logic to assess the efficiency of the curriculum design and make adjustments based on prior references. Improving the course and doing away with redundant material is essential. The proposed method exemplifies how the fuzzy logic process enhances adaptability and the student’s ability to understand the course material. The researchers evaluate the course design method by quantifying and analyzing design inconsistencies across various contexts. In conclusion, the proposed approach can improve foreign language teaching by making course design more flexible and understandable through cloud computing, e-learning platforms, and fuzzy logic.

4 Discussion

The discussion briefs the comparison study using information repetition, recommendations, inconsistency, recommendation time, and representations for the varying resources and representations. The methods SML-AE [35], FLLPIS [28], and ILSTM\(+\)GA [39] from the related works section are augmented in this comparison. This research compares the efficacy of several approaches to improving course designs in cloud computing using an online learning platform, employing metrics including the frequency with which material is repeated, the quality of recommendations, the degree of inconsistency, and the amount of time it takes to make a recommendation. Eliminating redundant content from teaching materials and lesson plans is a crucial strategy for maximizing the effectiveness of course design and student learning. Recommendations are vital in directing the course design process to improve learning outcomes and skill development. Addressing inconsistency is crucial for enhancing the quality of the educational experience and preserving the course material’s credibility. The time it takes to generate recommendations is helpful for educators and designers since it allows for faster adaptability to shifting pedagogical needs. Students’ understanding and interest in the information are influenced by the representations used in its presentation and delivery. Including all of these criteria in the comparison framework allows for an in-depth analysis of various approaches to course design, guaranteeing results that are reliable, useful, and grounded in the realities of the classroom.

Performance assessment indicators are selected to evaluate and enhance e-learning course designs using cloud-based fuzzy logic. These indicators include the following: degree of inconsistency, recommendation time, representation quality, recommendation quality, frequency of information repetition, and quality of recommendations. These metrics confirm that students retain their lessons, personalize their educational experiences, keep information consistent, make the system more responsive, and boost engagement with successful teaching strategies. The criteria for their selection include making informed comparisons between various educational techniques, satisfying user needs, being practically measurable, and improving pedagogical efficacy.

4.1 Information Repetition

The repetition of the information is eliminated from the fuzzy logic concept based on the previous references and the educational resources and curriculum. The curriculum and the educational resources play a vital role in the fuzzy logic concept, determining the earlier references and eliminating the repetitions in the references. It is used to determine the understandability and adaptability of the students in the foreign language. These outcomes also help extract new references when designing the course for the students without repetition. The previous reference with maximized versatility helps the fuzzy concept determine the identified preferences’ non-repetitions. The repetitive references are eliminated to enhance the efficiency of the course design in cloud computing using the e-learning platform. The educational resources and the e-learning curriculum play a vital role in this fuzzy process, as they help check the present requirements of the course design procedures and maximize the process’s adaptability. This information repetition is less in this process using fuzzy logic (Fig. 5). Here, the information repetition and representation are measured as count (n).

Fig. 5
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Information repetition

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4.2 Recommendations

Based on the previous references to the course design process, recommendations are given for the upcoming course design procedure. Fuzzy logic is used in the process of identifying the adaptability and understandability of the students. Adaptability denotes the reach of the students in terms of foreign language training with the necessary educational curricula and resources. The understandability represents the efficiency of the students in understanding the language within the period after the language training session. Based on these outcomes, new references are made to design the course to enhance students’ innovative skills and learning abilities. Fuzzy was used to eliminate the repetitions from the previous e-learning platform. By recommending precise, informative references, efficacious course design is happening in cloud computing with the aid of an e-learning platform. The recommendations are highly recommended for enhancing the foreign language course using previous successful e-learning platform references (Fig. 6).

Fig. 6
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Recommendations

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4.3 Inconsistency

The inconsistency is lessened in this process by executing the perfect preferences and information by avoiding repetitions. The unnecessary intentions and contents are eliminated from the previous preference for successful e-learning course design. The repetitions are eliminated by considering the educational resources, curriculum, and prior references. The fuzzy uses the outcomes for the determination of adaptability and understandability for the enhancement of the course design procedures. The inconsistencies are detected and corrected with the appropriate information about the foreign language course. The execution is precise in the course design process without any inconsistencies using the fuzzy concept from the previous references. This process helps maximize the adaptability level among the students and eliminate repetitions. It also prevents the temporary design process and enhances the efficiency of the course design process with the appropriate information (Fig. 7).

Fig. 7
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Inconsistency

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4.4 Recommendation Time

The time taken for the recommendation production is less than the exact execution from the references of the previous successful process. Based on the last successful e-learning process, references are given to the perfect course design operation. Based on this adaptability and understandability, references are made for the course design in cloud computing with the help of the e-learning platform. The outcomes of the fuzzy logic concept with the educational resources, educational curriculum, and the previous responses help manufacture the new references for the course design operation in the cloud with the help of the e-learning platform. It also helps enhance the students’ adaptability and understanding of the new references. Repetitive information is avoided to increase the students’ adaptability and efficacious understandability. The production of the references happens within a short period of time without any lags. Also, it helps to maintain the course design after the perfect completion, depends on the previous references (Fig. 8).

Fig. 8
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Recommendation time

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Table 5 Comparison summary of resources
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Table 6 Comparison summary of representations
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4.5 Representations

The representations are productive in this method by using the outcome of the fuzzy logic method. Previous references for the fuzzy logic procedure have been extracted. Here, the outcome of the last course, which has maximum adaptability, is considered the reference for the fuzzy logic procedures. After the teaching session, it helps determine the students’ maximum adaptability and understandability in the foreign language. It is used to determine the reach of the course to the students and their interests in the designed course. And the understanding ability of the students can be determined by the outcome of the fuzzy logic operation. Depending on that, references are given to the course on developing procedures in cloud computing. Therefore, representing precise information maximizes the foreign language course’s adaptability and eliminates the repetitions in the previous successful e-learning process. The fuzzy logic helps in executing the efficiency of the course design process with precise information (Fig. 9). Tables 5 and 6 present the summary of the above discussion with the % improvements.

Fuzzy logic, as shown in Fig. 5, helps overcome the problem of redundant content during the course development process. The method finds and eliminates unnecessary components by drawing on knowledge from prior references and using educational resources and curricula. This standardization helps make the e-learning platform’s cloud-based course design framework more efficient. As shown in Fig. 6, the technique provides helpful suggestions for future course design processes by building on the knowledge gathered from successful course design references. Learning flexibility and comprehension are both affected by fuzzy logic. These two characteristics are the bedrock upon which quality instructional content is built, ultimately boosting pupils’ creative capacities and academic prowess. As shown in Fig. 7, using fuzzy logic during the course design process helps reduce inconsistency. A more logical and trustworthy framework for the course is achieved by systematically removing extraneous material and goals from prior preferences. The methodology ensures that the course design meets the specified levels of adaptability and understandability by making good use of the results of fuzzy logic in conjunction with educational materials and curriculum.

This method reduces the time needed to generate recommendations compared to following prior references exactly, as seen in Fig. 8. Drawing on lessons learned from effective e-learning procedures, the technique efficiently produces course design references in the cloud. This freshness, flexibility, and comprehension it fosters in students make for quick and easy course reference creation. Figure 9 shows how using fuzzy logic, which considers results from past courses with the highest adaptation levels, improves the precision of course design. These results indicate how pupils have retained and applied what they have learned. As a result, the methodology generates accurate representations of course design, increases adaptability, and eliminates repeats within the e-learning platform’s framework.

The fuzzy logic approach has eliminated information repetition in e-learning course design. It uses prior references and educational resources to determine non-repetitive instructional content, enhancing the efficiency of e-learning course designs. The approach generates recommendations by analyzing previous course design references, focusing on the adaptability and understandability of students. This ensures that each new course design incorporates proven successful elements while adapting to current educational needs. The approach also reduces course design inconsistencies, enhancing the academic experience’s credibility and quality. The time required to generate recommendations is minimized using fuzzy logic and cloud computing resources efficiently. The approach also provides precise representations of course content, ensuring accurate and relevant content tailored to students’ learning needs.

Fig. 9
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Representations

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5 Conclusion and Future Work

The Cloud-assisted Course Design utilizing the Referenced Analysis method outperformed other adaptive foreign language e-learning course development methods. By combining cloud computing resources, fuzzy logic quantification of adaptability and understandability, and prior iteration analysis, the framework increased productive recommendations by 15.15% and reduced inconsistencies by 10.42% across course representations. Data-driven improvement of course content presentation was possible with fuzzy logic formulations assessing adaptability as a function of previous reference saturation and understandability as a function of interval membership. Similarity analysis eliminated redundant material in cloud resources, whereas referenced analysis recognized and reinforced successful e-learning deployments. Increased research on type-2 fuzzy logic and bio-inspired optimization algorithms like genetic fuzzy systems can improve quantitative modeling of highly subjective learner adaptability and understandability characteristics. Online transfer learning can update the referenced analytical model with new course data and student feedback in a lifelong learning paradigm. Advanced AI technologies like transformer language models and knowledge graphs could improve course material semantics by capturing nuanced linguistic links and context. Multimodal learning frameworks using audio/visual data and VR/AR could make learning more engaging. Supporting millions of concurrent learners requires large-scale cloud systems optimized for dynamic resource scalability, fault tolerance, and low-latency content delivery. Comprehensive user studies across multiple linguistic and cultural contexts are needed to prove the methodology’s real-world efficacy in enhancing language skills and learning outcomes. Finally, adapting quantification models and representations to generalize cloud-fuzzy-referenced principles to STEM, humanities, skill development, and other educational domains could make this a universal framework for intelligent, personalized, and adaptive e-learning solutions. Future research should look at ways to include cutting-edge technology like machine learning and natural language processing to make course recommendations even more personalized to each individual. It would be helpful to have longitudinal studies that track how this strategy affects student involvement and language competency over time.