Narrow Results

An Intelligent Exam Management System (IEMS) improves education information and handles an important role in education administration and management. Test optimization is the practice of successfully reducing test execution time and expenses. However, this can come at the expense of accuracy, efficiency, and the quality of testing that existed prior to optimization. In addition to being time-consuming and labor-intensive for teachers, grading essay responses may lead to inequity since it is impossible to apply consistent requirements to all of the responses. Creating assessments is a challenging task that involves optimizing parameters while adhering to several limitations. The Genetic Algorithm (GA) has been enhanced to address the issues of speed and low paper quality in intelligent test paper development. Educational management at universities has been the suggested approach for IEMS-GA to facilitate smarter education, test and course management, promote the improvement of ideas related to school administration, and provide a more equal system of evaluation. Students rely on exams as a means of gauging their own academic strengths and weaknesses. Students’ test scores provide valuable information about their academic strengths and areas for improvement, allowing them to better plan for the future and attain their academic goals. Results are an important part of the educational process as they reveal a great deal about the way students are doing in class. Knowledge and skills necessary to succeed in the real world are evaluated by such assessments.

An instructional approach identified as a personalized learning path aims to adjust regulations to the particular interests’ necessities, talents, and skills of each pupil. Research on personalized path recommendation is critical for the development of sophisticated learning systems. The challenge emerges from the difficulty in developing personalized learning paths that properly fit every pupil’s unique characteristics and learning needs. This paper examines personalized educational routes and how deep learning (DL) algorithms can be used to construct them, with a particular emphasis on English language learning. This research proposed a novel fruit fly optimizer-tuned adjustable recurrent neural network (FFO-ARNN) to employ the students learning performance and effectively personalized English learning. For this study, gathering data on student’s demographics and English proficiency levels through assessments and educational records, in that Data on the linguistic competency of English linguistic Learners (ELLs) in grades 8–12 was collected. The data were pre-processed using tokenization for the obtained data. Term Frequency-Inverse Document Frequency (TF-IDF) is a method using extracts the feature from pre-processed data. These personalized learning approaches encourage self-directed learning and independence while increasing student engagement and academic achievement. The proposed method is implemented using Python software. In comparative analysis, the proposed method evaluates various performance metrics. The results show that the proposed method achieved superior performance in personalized learning paths in English learning. This study demonstrates how DL has the potential to revolutionize language instruction by providing customized solutions for every student.

In order to optimize the evaluation of students’ English creative thinking ability under the multimedia assisted teaching mode, a training model of students’ English Creative Thinking Ability Based on big data analysis is proposed. Establish a big data block fusion structure analysis model, analyze the fuzzy parameters of constraint indicators, obtain a quantitative table for the state allocation of evaluation constraint indicators, formulate an evaluation quantitative analysis scoring table, and use hierarchical structural reconstruction to construct the clustering model parameters for the evaluation of students’ English creative thinking ability training. Through the quantitative index feature analysis, the optimization design of teaching activities and teaching methods for the evaluation of the cultivation process of students’ English creative thinking ability are realized. In the multi-dimensional hierarchical structure parameter model, the parameter configuration of students’ English creative thinking ability training indicators is realized, and the evaluation characteristic index fusion clustering processing is carried out on the parameter configuration results to form a pair of classification prediction and index analysis model. According to the hierarchical distribution density and grid clustering of indicators, the evaluation of students’ English creative thinking ability under the multimedia-assisted teaching mode is realized. The results of empirical analysis show that the fuzzy parameter analysis ability of the constraint index of process evaluation using this method is strong, the evaluation results are accurate and reliable, and the reliability and confidence level of evaluation are improved.

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English.

Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills.

Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms.

Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and $F$1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.

The social and ideology education, focus instruction, hands-on learning, and a variety of means, students are cultivated in their political quality, moral character, and sense of social responsibility while it is being guided in understanding the nation’s important policies and social current affairs. Ideological and political disciplines are implementing the problem of transformation within the framework of the major criteria of the new curriculum reform. In the conventional teaching process, students face several challenges in achieving the essential quality of ideological and political education. The artificial intelligence-assisted interactive modeling on the Internet of Things platform (AI-IM-IoT) is able to generate a smart system for ideological and political education, addressing various problems in the current situation. Performance in conventional ideological and political learning platforms, an investigation on the construction of intelligent media ideological and political learning platforms are based on AI technology. Hence AI-IM-IoT has been the Ideology of ideas and beliefs that influence social behavior and can possess an effect on educational achievements. Ideological viewpoints on education can influence educational institutions’ aims, values, and priorities, as well as how resources and opportunities are allocated. Students are encouraged to develop an understanding of social, economic, and political issues via ideological and political education programs.