Evaluation Of Learning Effect Research Articles

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective: To analyze the technical indexes of students’ online learning behavior analysis based on Kirkman’s evaluation model, sort out the basic indexes of online learning behavior, and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis. Methods: The online learning behavior data of Physiology of nursing students from 2021–2023 and the first semester of 22 nursing classes (3 and 4) were collected and analyzed. The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation. Results: The study found that the demand for online learning of nursing students from 2021–2023 increased and the effect was statistically significant. Compared with the stage assessment results, the online learning effect was statistically significant. Conclusion: The main indicators for evaluating and classifying online learning behaviors were summarized. These two indicators can help teachers predict which part of students need learning intervention, optimize the teaching process, and help students improve their learning behavior and academic performance.

Supervised representation learning based on various levels of pediatric radiographic views for transfer learning

Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the prevailing practice of transfer learning predominantly relies on pre-trained models designed for the natural image domain, which may not be well-suited for the medical image domain in grayscale. Recognizing the significance of leveraging transfer learning in medical research, we undertook the construction of class-balanced pediatric radiograph datasets collectively referred to as PedXnets, grounded in radiographic views using the pediatric radiographs collected over 24 years at Asan Medical Center. For PedXnets pre-training, approximately 70,000 X-ray images were utilized. Three different pre-training weights of PedXnet were constructed using Inception V3 for various radiation perspective classifications: Model-PedXnet-7C, Model-PedXnet-30C, and Model-PedXnet-68C. We validated the transferability and positive effects of transfer learning of PedXnets through pediatric downstream tasks including fracture classification and bone age assessment (BAA). The evaluation of transfer learning effects through classification and regression metrics showed superior performance of Model-PedXnets in quantitative assessments. Additionally, visual analyses confirmed that the Model-PedXnets were more focused on meaningful regions of interest.

Application of augmented reality models of canine skull in veterinary anatomical education.

Veterinary anatomy plays a crucial role in the curriculum for veterinary medicine and surgery. The integration of modern information technology in veterinary education can greatly benefit from innovative tools such as augmented reality (AR) applications. The aim of this study was to develop an accurate and interactive three-dimensional (3D) digital model of an animal skull using AR technology, aiming to enhance the learning of skull anatomy in veterinary anatomy education. In this study, a canine skull specimen was isolated, and the skull bones were scanned using a structured light scanner to create a 3D digital model of the canine skull, which was found to be indistinguishable from the original specimen by measurement of skull proportions. Furthermore, the interactive AR model of the canine skull, displayed using Unity3D, was subjected to testing and evaluation by 60 first-year veterinary medical students attending the gross anatomy of the animal. The students were divided into two groups: the traditional group and AR group. Both groups completed an objective test and a questionnaire. The evaluation of learning effectiveness in the test revealed no significant difference between the traditional group (which learned using textbooks and a canine skull specimen) and AR group (which learned using AR tools). However, in the questionnaire, students displayed high enthusiasm and interest in using the AR tool. Therefore, the application of AR tools can improve students' motivation for learning and enhance the comprehension of anatomical structures in three dimensions. Furthermore, this study exemplifies the use of AR as an auxiliary tool for teaching and learning in veterinary anatomy education.

Intelligent English Teaching Based on the Pedagogy of Performing Another Culture and ChatGPT Technology

In the background of digital education, the intelligent capabilities of ChatGPT-based technology in teaching have emerged as a powerful tool for facilitating teachers’ preparation for lesson, improving students’ learning motivation and effectiveness, and promoting the development of intelligent curriculum. This study put forward a novel model of ChatGPT-based English teaching grounded in the “Pedagogy of Performing Another Culture” proposed by Galal Walker. From the three aspects of teaching design, application scenarios, and learning effect evaluation, this model explores the application of ChatGPT in the four fundamental language domains of listening, speaking, reading, and writing, with the objective of enhancing students’ language proficiency and cultural sensitivity, while also fostering their digital learning capabilities. To uphold academic integrity and ethical use of ChatGPT in English teaching, a C++-based ChatGPT application has been designed and tailored to the specific needs of English language learners. The school-based intelligent courses based on ChatGPT are available for students to carry out more diverse and effective English learning, promoting the rational use of ChatGPT in learning.

Training and Application of Teachers’ Informationalized Teaching Skills in a Blended Teaching Model for Accounting

Abstract Blended teaching combines the benefits of both traditional classroom lecture instruction and online learning, allowing students to tailor their learning path based on their knowledge base. It also eliminates time and space constraints, effectively counteracting the limitations of traditional teaching methods. Based on teachers’ informational teaching skills, this study adopts the improved PSO algorithm to summarise the data generated by the teaching process and builds a blended teaching model based on accounting training in the context of big data. Subsequently, we employed a questionnaire survey to explore how the blended teaching model aligns with the informational teaching skills of accounting students, and ultimately, we summarized and generalized the impact of the blended teaching model’s execution. It was found that the accuracy of the data collected by the hybrid teaching model based on the DPSO algorithm ranged from 86.82% to 95.05%, and the energy consumption of the sensors of this model only ranged from 5.46 to 7.76*102 (mJ). Students were very satisfied with 32%-50%, 26%-33%, 30%-50%, and 25%-61% of the blended teaching effect, information technology teaching skills, course design and organization evaluation, and learning effect evaluation, respectively, which indicates that the blended teaching has achieved good results. This paper optimizes the three aspects of teaching and training the accounting profession to provide a reasonable reference for the construction of an information technology teaching mode for accounting professional training and application.

Research on Teaching and Dance Creation of Chinese Folk Dance in the Information Age

Abstract In this paper, we use motion capture technology to collect typical movements of Chinese folk dances and establish three-dimensional character models with the characteristics of human motion continuity and bone length invariance to discriminate the trajectory of skeletal movements. Video is used to export the data of Chinese folk dance. Design methods for extracting folk music and dance features to form a model for creating dances. Suppressing the noise of the original dance music, improving the accuracy of the fundamental frequency extraction of the music signal, and manually scoring the dance creation generation program are all benefits of using the Chebyshev filter. We propose the teaching method of Chinese folk dance in online broadcasting mode and carry out teaching experiments to compare the cooperative learning effect and dance technique evaluation effect between traditional dance teaching method and online broadcasting video lecture teaching method. The dance creation and generation program proposed in this paper is able to achieve a dance movement similarity of 0.92, which is able to preserve the original dance posture of Chinese folk dances to a greater extent. The results of the technical evaluation show that the teaching effects of the two teaching methods present significant differences in the four methods of basic technique, pair cooperation, dance expression, and total score, i.e., P<0.05, which shows that the online live video explanation method is more suitable for the teaching of ethnic folk dance in the information age.

Exploration and Research on the Evaluation of Learning Effectiveness for Students in Innovative Curriculum Teaching Models

Exploration and Research on the Evaluation of Learning Effectiveness for Students in Innovative Curriculum Teaching Models

The Construction of Intelligent Platform in Ideological and Political Education in Colleges and Universities

Abstract Based on the feasibility of a personalized teaching platform and user requirements, this paper puts forward the overall architecture design and database design scheme of a personalized teaching platform in ideological and political education, which mainly consists of three functional modules, namely, the knowledge mapping module, ideological and political education course recommendation module, and learning effect evaluation module. After crawling the initial data based on the LTP model, the ideological and political education course resources were extracted and integrated to complete the construction of the knowledge graph module. The ideological and political education course recommendation module is created using the KGCNN algorithm, and then the learning effect evaluation module is constructed by combining the online behavior of students. After testing the system’s performance, the application effect of the teaching platform is assessed. The results show that KGCNN aggregation layer space in the interval of 10~100 can embed data with power law distribution more effectively, and the KGCNN algorithm also has certain advantages in the field of modeling personalized teaching platforms for ideological and political education. The number of experimental classes and ordinary classes with final grades in the L1 band increased by 21.90% and 7.17%, respectively, compared to the midterm, indicating that the personalized teaching platform for ideological and political education courses can effectively promote the improvement of student’s academic performance, and the enhancement effect of students with high levels is more significant.

Analysis and Optimization of the Effectiveness of College Physical Education Courses Based on Fuzzy Cognitive Evaluation

Abstract Cognitive diagnostic theory can provide a comprehensive and detailed objective evaluation of individual macro learning ability, thus providing an effective reference for targeted teaching and learning. In this paper, a fuzzy cognitive diagnostic model is used to evaluate the degree of mastery of a college student on the physical education course, calculate the probability of mastery of each attribute, and statistically analyze the degree of mastery of the attributes of the individual as well as the class as a whole. Then, a learning effect evaluation standard is constructed to evaluate the learning effects of the physical education course for all students. The empirical results show that the mean value of the evaluation of the students’ cognition of physical education in School X is 0.6587, and the average probability of mastery of physical education emotion is 0.6021, both of which have reached the degree of good mastery. The average probability of mastering sports operations was 0.5909, which indicates that university students have an average level of mastery in all sports skills through the physical education program. The outcome is in line with the current situation, which can serve as a reference for teachers to make targeted teaching decisions. The study has both theoretical value and practical significance in promoting the development of wisdom education.

The Evaluation of Online Learning Effectiveness in Economic Course

The study was conducted to evaluate the effectiveness of online learning process for economic course during covid-19 pandemic. A quantitative approach was used in this study particularly in the form of evaluation using CIPP model. There were four main components; context, input, process, and product. There were 150 participants were involved in which they were three teachers who taught economic course and 147 students who took social and economic department. They were selected by using random sampling technique. The data were collected through observation, document analysis, and questionnaire distribution. The instruments used were observation sheet, and questionnaire. The obtained data were analysed by using descriptive statistical analysis. The results showed that the online learning online learning process for economic course during covid-19 pandemic was effective with good category. The context evaluation was categorized into good category with the percentage of 72.33%. The input evaluation was also found on the percentage of 73.78% categorized into good category. The process evaluation had a good category with the percentage of 73.75%. The product evaluation was categorized into an excellent category. Those four components showed that the online learning had been conducted effectively.

Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique

Abstract In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.

Evaluation of the comprehensive thematic teaching effectiveness and technique/technology in culinary vocational education

PurposeThe research aims to discuss technical and vocational students' long-term and complete evaluation of learning effectiveness under school-wide thematic teaching design.Design/methodology/approachThis study adopted a mixed qualitative and quantitative approach to measure the learning effectiveness of thematic teaching in technical vocational schools using the Kirkpatrick model. Qualitative research conducted in-depth interviews with 32 interviewees, including students, parents, teachers, graduate alumni and the supervisors of off-campus internship units. Quantitative research conducted a questionnaire survey on vocational students. A total of 221 valid questionnaires were collected. In addition, this research conducts another quantitative survey on cooperative enterprises to compare the actual effect of the implementation of the school-wide thematic teaching students with the others, and a total of 35 valid questionnaires were collected.FindingsThe results of the research found that the effectiveness of thematic teaching method can achieve the expected goals of each level of Kirkpatrick model. The students taking thematic teaching are significantly better and fitting in the industry expects. Therefore, this research suggests the comprehensive introduction of school-wide thematic teaching to other school operators.Originality/valueThis research is the first study used the Kirkpatrick model to evaluate the effectiveness of school-wide thematic teaching design in hospitality education and providing a practical case for schools. This research combined qualitative and quantitative research methods to investigate the effectiveness of the teaching method through multiple perspectives. Through the feedback from supervisors of the hospitality industry, the school-wide thematic teaching design provides a good foundation for technical and vocational graduates.

Study on the Evaluation Method of Blended Learning Effect Based on Multiple Linear Regression Analysis

With the development of information technology, blended learning has been widely used in the education field, and the evaluation of blended learning effect has become one of the research hotspots. Taking the automobile theory course as an example, a blended learning process with online and offline is designed, and the main learning behaviors that affect learning effect are analyzed. By extracting data on the main learning behaviors of students during the learning process, correlation and linear regression methods are used to analyze the influencing factors of blended learning effect, and a linear regression prediction model is established. The results show that students' online testing, classroom performance, unit testing, feature assessment, and experimental performance are key indicators for predicting learning performance. According to the analysis of influencing factors of blended learning, the countermeasures and suggestions for improving the effect of blended learning are proposed.

基于网络课程的中国大学生网络学习行为分析

The rapid development of online courses has promoted the transformation of educational and teaching concepts, the updating of teaching content and the reform of teaching methods, but it also increases the difficulty of teaching organization management and learning effect evaluation. Based on the monitoring data of “probability theory and mathematical statistics” course of Pingdingshan University’s learning network platform, this paper studies the online learning behaviors of college students, analyzes the correlation between various monitoring data on the learning platform and learning effects, and the existence of online learning behaviors of college students. Finally reasonable improvement suggestions are given to provide strong support for teachers to scientifically guide college students’ online learning behaviors, so as to achieve the purpose of improving learning effects.

Evaluation of learning effect of blended teaching mode ----- Take the international finance course as an example

In the era of big data, the school's teaching mode has gradually transformed its focus from the traditional teaching mode to the blended teaching mode integrating online teaching and offline teaching. In order to explore the learning effect of students under the blended teaching mode and cultivate the all-round development of economic talents, this paper conducts an in-depth investigation on the blended teaching effect of international finance course, the core course of finance major. A total of 161 students majoring in finance in 2019 were selected as research objects. According to the scores of international finance courses, the subjects were divided into research group (n=83) and control group (n=78). The research group selected students with high scores of international finance courses, while selected students with low scores as the control group. According to the results of the questionnaire and innovation satisfaction survey of students studying international finance courses, the following conclusions can be drawn: Students in the research group accept the blended teaching mode more than those in the control group (p<0.01), and students in the research group are better than those in the control group in the cultivation of innovation ability. Therefore, the blended teaching model should not only improve the ability of students with high performance, but also pay attention to the learning effect of students with low performance, so as to avoid extreme differentiation of learning effect.

Prediction of College Students' Classroom Learning Effect Considering Positive Learning Emotion

Exploring the influence of positive learning emotion on college students' classroom learning effect facilitates fully understanding college students' online learning effect and emotional state, and is beneficial to improving students' learning quality and teachers' teaching quality. At present, few scholars have summative evaluation of students' classroom learning effect from the perspective of students' learning emotions and prove from the perspective of theory and practice that good emotional state is an important influencing factor to improve college students' classroom learning effect. Therefore, this article fully considers the positive learning emotion, and makes a research on the prediction of college students' classroom learning effect. Firstly, this article defines the behavior data of students in the online learning process based on learning emotions, and studies the correlation between college students' classroom learning behaviors based on Hawkes process. Then, based on the learning participation under the influence of different learning emotions, the online learning effect of students is quantified, and the prediction model of students' classroom learning is constructed by combining the learning behavior sequence analysis results represented by Hawkes process and the characteristic information of students themselves and courses. The experimental results verify the effectiveness of the model, and the significance test results confirm the positive effect of positive emotion on learning effect.

Clustering Analysis of the Evaluation Data on Students’ Learning under a Blended Environment

This paper explores the establishment of the effectiveness evaluation index system for students’ learning under blended learning environment, so as to provide some theoretical reference and guidance for the evaluation of the effect of students’ learning in blended environment. In current studies, there are few quantitative analyses on the application cases or teaching practice, and there is also a lack of evaluation studies on the students as the main body of the learning process. Therefore, this paper carries out a study on the clustering analysis of the evaluation data on students’ learning in blended environment, displaying the learning effect satisfaction model for students under blended learning environment, and providing the idea of constructing a learning effect evaluation model accordingly. This paper characterizes the learning effect of students at each stage in the form of sets and formulates evaluation index system. The analytic hierarchy process-grey clustering evaluation of students’ learning effect in blended environment is carried out. On the basis of analytic hierarchy process, the evaluation index weight is determined and the evaluation grade is fixed based on the grey clustering analysis. The experimental results verify the effectiveness of the evaluation model.

Artificial Intelligence-Based English Self-Learning Effect Evaluation and Adaptive Influencing Factors Analysis

Under the background of continuous development in our country, traditional English education can no longer meet the needs of modern times. Through the evaluation of English autonomous learning effect of artificial intelligence and the analysis of the influencing factors of adaptability, the teaching effect of English class is improved and the students’ awareness of autonomous learning is cultivated. In the pilot study, students now have an overall level of adaptation (3 < M < 4) supported by English proficiency. That is, the standard deviation is 1. The overall level of self-study in English is higher than that of boys (M = 3.40 for females, M = 3.32 for males, and M = 3.32 for males). Compared with non-English majors, English majors are more suitable for self-study of artificial intelligence in English (M = 3.59 for English majors), (M = 3.36 for non-English majors), and students can improve their adaptive ability to learn AI by creating models. Transfer learning is the key to improving learners’ English proficiency, and adaptive learning is the key to achieving this goal. Self-adaptive learning ensures the quality of students’ autonomous English homework. Improper English learning not only affects students’ learning outcomes but also affects their ability to learn English.

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

AbstractSentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real‐time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini‐Xception framework, this article implements the real‐time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini‐Xception model trained by FER2013 data sets has high recognition accuracy for the real‐time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real‐time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

Influence Study of Learners’ Independent Learning Ability on Learning Performance in Online Learning

In this paper, questionnaires regarding the influence of learners’ autonomous learning ability on learning performance in online learning were designed, and the mediating role played by deep learning orientation in the effect of autonomous learning ability on learning performance was analyzed. The results revealed that the overall Cronbach’s α coefficient of the questionnaire was 0.884, the KMO value was 0.817, and the corresponding P value was 0.000, manifesting the good reliability and validity of this questionnaire. The learning performance could be obviously influenced by four aspects of autonomous learning ability: preparation of technology and target plan, utilization of materials in learning contents, regulation of learning process, and evaluation of learning effect. Deep learning orientation played a complete mediating role in the promoting effect of autonomous learning ability on learning performance. Learning frequency exerted a significant (0.01) influence on learning performance. Results are of considerable importance to enriching the literature on online learning environments and the development of autonomous learning ability, helping learners to cultivate autonomous learning ability, and enhancing the effectiveness of guidance provided by teachers to learners in online learning environments.