Learning Behavior Data Research Articles

Research on E-learning Behavior Evaluation of Students Based on Three-way Decisions Classification Algorithm

With the popularization and application of E-learning platform, it is feasible to collect learning behavior data of students, which provides data basis for analyzing the knowledge and rules contained in learning process of students. At present, the common e-learning platform only realizes the simple statistics and display function for the data, and does not do further in-depth calculation. The data observed by educators are still superficial learning phenomena, and they cannot see the learning rules behind the appearance. It is difficult to effectively guide students, change their learning routes and feedback teaching strategies. In view of this, the main characteristics of learning behavior deeply mined from learning activities and learning evaluation, and the important data feature items affect E-learning effect obtained. Three decision classification algorithms applied to establish prediction and evaluation model for students with learning behavior risk. The overall accuracy of model test is in line with the expectation. This provides a practical application modeling method for further implementation of personalized learning service.

Learning Behavior Analysis and Learning Effect Evaluation in Open Online Courses

In the context of globalization, the trend of globalization in the field of education is becoming more and more significant. With the maturity of Internet and cloud computing, a large online open course learning platform providing courses and educational services for global users has emerged in the past few years. This is not only the innovation of Internet applications, but it is also believed that it will trigger a change in higher education and social development. The main body of online open courses is learners, and its biggest feature is that there are a large number of learners and a variety of learner groups. Due to the characteristics of Internet technology, all learning behaviors of learners on the online open course platform will be recorded in the form of rich and diverse data. Therefore, it is necessary to analyze learners’ learning behavior. This paper proposes a dual-channel clustering algorithm, which analyzes and mines a large number of learning behavior data of more than 5000 learners in online open courses of a university. This method takes fine-grained data as the core, obtains the types of learners in different models through dual-channel clustering calculation, and finally characterizes learners based on the fused model. Compared with three state-of-the-art clustering algorithms, the experimental results show that the proposed dual-channel clustering algorithm can enhance the cohesion of clusters, cluster learners more accurately, and characterize learners’ profiles more deeply and comprehensively.

Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the BP neural network are compared on the dataset. Experiments show that BP neural network model has stronger generalizability than common machine learning models. The BP neural network with L2 regularization has better fitting ability than the original BP neural network model. It achieves better performance with improved accuracy.

Research on the differences of students’ learning styles and knowledge integration in SPOC learning: a case study of course “Basic Portuguese”

Problem and goal. With the advent of the information age, Internet-based online learning has also become one of the learning methods chosen by many learners. They can use these online learning platforms to complete knowledge construction while learning offline. Methodology. Most studies of learning behaviors focus on the discovery of the best learning model and disregard the possible impact of different learning behaviors on knowledge construction. Therefore, based on the Felder - Silverman learning style model, this article uses the Solomon learning style scale to improve the design of the questionnaire and collect four-dimensional differential learning behaviors data. In order to further understand the influence of learning styles on the effectiveness of online learning, we also use online learning data on the Small Private Online Course platform and general cognitive intelligence knowledge integration theory to clarify the relation between learning modes and individuals differences. Results. This study observes and analyzes the learning behavior data of 46 students of Nankai University in the SPOC learning platform, also analyzes the differences in learning styles and knowledge construction of students in the SPOC environment. Compared with the traditional Basic Portuguese teaching method, the blended teaching model based on the Chaoxing Learning platform has unparalleled advantages. Interactions inside and outside the classroom, improving student participation and promoting teaching diagnosis. Conclusion. Through a comprehensive analysis of questionnaire data and online data, we found that some learning styles have different effects on the effectiveness of online learning, ignoring the individual differences of learners will still cause problems in knowledge construction.

Research on the Application of Computer-based Visual Big Data in Online Education and Teaching

According to the analysis of big data, With the rise of online education and teaching in domestic universities, students’ learning behavior data has grown exponentially. How to deal with the massive data, explore the deficiencies in the teaching environment, help the teachers optimize the teaching content and teaching process, and gradually become the focus of education researchers. Based on the analysis of the data characteristics of the teaching platform, this paper proposes a series of visual big data methods for teaching scenarios. Through the programming realization of visual big data method, teachers can intuitively present knowledge for students, timely understand the students’ learning status, and carry out targeted classroom teaching and teaching evaluation. Students can form knowledge framework and interconnection system to promote knowledge expression and Internalization, and complete self-assessment and discover their own learning problems; teaching managers can master the teaching effect of teachers and students’ learning, and adjust the goals of teaching management and decision-making. This paper combines the self-developed online education and teaching - Shidayun classroom, to apply the visual big data method to the actual teaching, which has produced good practical effects and effectively promoted the development of wisdom education.

Interaction recognition and intervention based on context feature fusion of learning behaviors in interactive learning environments

ABSTRACT Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are integrated. Through the training set, the recursive multi-layer feedback neural network model based on the fusion of context features is constructed. The recognition algorithm is designed. From relative standard deviation (RSD) and prediction accuracy (P.A), the model and algorithm designed in this study are more suitable for the recognition and prediction of learning behavior data. Furthermore, the key topological path and interaction process equation of learning behaviors are designed, three kinds of intervention optimization strategies are constructed to serve the daily teaching processes of three courses. After two semesters of learning behavior feature recognition and relationship analysis, learners’ interest, assessment pass rate, or excellence rate have improved significantly. So the intervention optimization strategies of learning behaviors based on the analysis results of models and algorithms can influence the feature distribution and behavior trend.

Study on the Personalized Learning Model of Learner-Learning Resource Matching

With the development of service integration technology, online learning platforms have gathered a large number of learning resources, causing learners to get lost in a variety of course information and it is difficult to obtain learning resources that match their own needs. The proposal of personalized learning gives the problem a direction to solve. However, current personalized learning resource recommendation services facing problems such as excessive candidate resources, sparse history and cold starts. In addition, the learning resources provided also show problems of "difficult or easy, uneven quality". For this article researches the personalized learning recommendation model of learner-learning resource matching. The main content includes three parts: First, build a demand model based on learner registration information, learning behavior and other data. Second, analyze the access behavior of learning resources and assess their quality. Third, calculate the matching degree between learners and learning resources based on the demand model and the quality information of the learning resources, and recommend them.

The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm

With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students’ academic performance based on the learning behavior data of online learning, so as to intervene in students’ learning in advance, which is the key problem that needs to be solved at present. Taking students’ learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students’ performance, and evaluates students’ performance. Firstly, this paper collects the high‐dimensional behavioral characteristic data of students’ online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students’ performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students’ achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students’ achievement, which has a practical reference value for effectively serving online learning and teachers’ teaching.

Autonomous Learning Mode based on a Four-Element Teaching Design for Visual Communication Course

With the development and improvement of information transfer efficiency in this era, visual communication has people's life and communication. However, the thinking in the traditional visual communication course is fixed, and students' learning enthusiasm is weak. Hence, this study proposes an autonomous learning mode by using a four-element teaching design. First, we propose relevant knowledge and ability, learning task, supporting program, and special practice as four elements related to education program. Second, we integrate them into the autonomous learning model to help students achieve learning transfer. Third, we use the new K-means algorithm to collect students' autonomous learning behavior data in the online course of visual communication and divide the data into three levels (high, medium, and low levels) for analysis. Finally, we design autonomous learning teaching cases for visual communication by using the proposed four-element teaching design and analyze the teaching effect. Our results show that the proposed learning mode enables a long learning video watching time for students, which in turn produces a high percentage of completed tasks. This result verifies that the application of the autonomous learning mode based on four-element teaching design can improve students' autonomous learning ability. Therefore, this mode is worth promoting.

Learner behaviors in synchronous online prosthodontic education during the 2020 COVID-19 pandemic

Statement of problemSynchronous online prosthodontic courses became a popular learning mode during the 2020 COVID-19 pandemic crisis. Nonetheless, the extent of learner participation and completion of these courses remains unknown. PurposeThe purpose of this study was to assess learner behaviors in synchronous online prosthodontic continuing education lectures in China during the 2020 COVID-19 pandemic. Material and methodsAll live online prosthodontic courses held by an online dental school in China from February to May 2020 were retrieved. The no-cost lectures could be accessed anonymously and viewed repeatedly on the day of broadcast. Learning behavior data (teacher speaking time, audience total, timing of first visit to the online classroom, viewing time, and completion rate) were obtained. Learning progress was calculated by dividing viewing time by teacher speaking time. When a learner progressed through 95% of a lecture, the lecture was considered completed. ResultsA total of 41 781 learners participated in 18 online prosthodontic courses, which had a mean duration of 77.2 ±15.8 minutes. For each lecture, 2321 ±1454 participants attended, with 510 ±404 participants completing each session. There were 13 098 participants (31.35%) who viewed the lectures for less than 1 minute. Approximately half of the participants viewed the lectures for less than 10 minutes, with their learning progress failing to pass 10%. The average completion rate was 21.97%, with variation in completion rate dependent on when a learner first visited the online classroom. Significant differences were found among the lecture completion rates and the timing of the first visit to the online classroom (P<.001). ConclusionsSynchronous online prosthodontic education courses in China had a high number of participants but low learning progress and completion rates during the 2020 COVID-19 pandemic.

MOOC student dropout prediction model based on learning behavior features and parameter optimization

ABSTRACT Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC research. Due to there are huge differences in the learning behaviors, study habits and learning time of different students in MOOC, i.e. the students' learning behavior data containing rich learning information, so it can be used to predict the students' dropout status. In this paper, according to the students' learning behaviour data, a feature extraction method is firstly designed, which can reflect the characteristics of weekly student learning behaviors. Then, the intelligently optimized support vector regression (SVR) model is used as the student dropout prediction (SDP) model. In this SDP model, the three parameters of SVR are not randomly selected but determined by an improved quantum particle swarm optimization (IQPSO) algorithm. Experimental results from both direct observation and statistical analysis on public data indicate that the proposed SDP model can achieve better predictive performance than various benchmark SDP models.

A Scalable Operating System Experiment Platform Supporting Learning Behavior Analysis

Contribution: The design of an operating system (OS) experiment course with a gentle learning curve is proposed and a scalable OS experiment platform supporting learning behavior analysis is presented. Background: In the teaching practice of the OS experiment course, several problems were faced. First, the learning curve for the students is too steep. Second, the OS experiment is hard to scale to a large number of students. Finally, it is difficult to track the learning behaviors of the students and to provide feedback to the teaching plan. Intended Outcomes: By smoothing the learning curve and providing targeted and rapid feedback, students using the system can more effectively master the knowledge of the OS by completing experimental tasks. Application Design: A large number of students can log in to the experiment system and complete multiple labs online. In addition to using automated testing to provide fast feedback, the lab system also collects learning behavior data of the students, enabling the teachers to analyze the data and adjust their teaching plan accordingly. Findings: The performance and learning-behavior data of the students from 2015 to 2018 showed that the OS experiment is effective to scale to a large number of students with satisfiable teaching effectiveness.

A Hybrid Teaching Mode Based on Machine Learning Algorithm

Background: Hybrid teaching mode is a new trend under the Education Informatization environment, which combines the advantages of educators’ supervision offline and learners’ self-regulated learning online. Capturing learners’ learning behavior data becomes easy both from the traditional classroom and online platform. Methods: If machine learning algorithms can be applied to mine valuable information underneath those behavior data, it will provide scientific evidence and contribute to wise decision making as well as effective teaching process designing by educators. Results: This paper proposed a hybrid teaching mode utilizing machine learning algorithms, which uses clustering analysis to analyze the learner’s characteristics and introduces a support vector machine to predict future learning performance. The hybrid mode matches the predicted results to carry out the offline teaching process. Conclusion: Simulation results on about 356 students’ data on one specific course in a certain semester demonstrate that the proposed hybrid teaching mode performs very well by analyzing and predicting the learners’ performance with high accuracies.

Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning behavior using 8 tracking variables: the total number of content pages seen in the learning process; the total number of concepts; the total online score; the total time spent online; the total number of logins; the stereotype after the initial test, the final stereotype, and the mean stereotype variability. The previous measures were used in a four-step analysis that consisted of data preprocessing, dimensionality reduction, the clustering, and the analysis of a posttest performance on a content proficiency exam. The results were also used to construct the decision tree in order to get a human-readable description of student clusters.

Artificial Intelligence-based Learning Behavior Data Mining and Network Teaching Quality Monitoring Mechanism

The integration of artificial intelligence technology and school education has become a future trend, which represents that students receiving education in the current society have to develop to a higher level supported by “intelligent education”. The popularity of network education also means that higher education has a higher demand for “artificial intelligence education”. The purpose of this paper is to verify the feasibility of these methods by data mining and evaluation of students’ learning behaviors through different methods in artificial intelligence, aiming at the new situation and new problems of students’ learning behaviors in modern education and teaching environment. This paper takes the students of school A as the research objects, starts from the perspective of learners and takes the online learning behavior of learners as the starting point, analyzes the effectiveness of online learning from multiple perspectives, and analyzes the quality monitoring mechanism of online teaching in this university by combining artificial intelligence technology, and puts forward targeted Suggestions.

Research on Integration Method of AI Teaching Resources Based on Learning Behavior Data Analysis

Aiming at the problem of long running time and low precision of traditional artificial intelligence resource integration method, a new artificial intelligence teaching resource integration method based on learning behaviour data analysis is proposed. Analyse online learning behaviour data, extract relevant data of artificial intelligence teaching resources, and realise sample collection of artificial intelligence teaching resources. Random forest algorithm was used to classify them, reward and punishment factors were added, and mountain-climbing method was used to search the solution space, so as to realise the integration of artificial intelligence teaching resources. The experimental results show that this method can improve the efficiency and precision of the whole method, and obtain better integral results.

Research on Adaptive Learning Prediction Based on XAPI

In the field of online learning, there is a problem of high student turnover rate. How to accurately identify learners and provide targeted teaching support services is an urgent problem for education researchers. In this paper, 1306 online learners majoring in finance from Shanghai Open University were selected as the subjects, and two kinds of data sets are adopted, which are learning data of online learning platform and learning behavior data of students based on xAPI, to analyze the relationship between learners' various online learning behaviors and learning achievements, and to determine the characteristics related to learning state of learners, describe the personalized learning state portrait, and select a variety of machine learning algorithms to build prediction model based on two data sets, to explore which data is more effective for building prediction models to identify potential risk learners. It is found that data mining analysis based on xAPI data has higher prediction accuracy than traditional online learning data.

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Studying learning path planning can help find useful implicit learning behavior patterns from learners' online learning behavior data, which is conducive to helping beginners or learners with low participation to reasonably arrange the learning sequence of online knowledge points. This paper proposes a learning path planning algorithm based on collaborative analysis of learning behavior through collaborative data analysis of online learning behaviors. The algorithm, based on the learner's online learning behavior data set, first establishes the concept interaction degree model of knowledge points and the directed learning path network, and proposes a local structure similarity measurement method between the knowledge nodes of the directed learning path network. Second, based on the learner's Kullback-Leibler divergence (KLD) matrix, a learning behavior similarity calculation method on the basis of eigenvector matrix similarity is proposed, which is used to perform cluster analysis on learners with similar learning behaviors and to analyze the personalized optimal learning path of each kind of learners. Finally, the clustering algorithm and the evaluation index of the directed complex network have both verified the advantages of the algorithm. This paper employs the online behavior data set and online test data set obtained from the e-learning platform to conduct an empirical analysis of the learning path planning algorithm proposed hereof. The results show that the learner's learning effect has been improved, verifying the validity and reliability of the algorithm.

Online learning behavior analysis based on machine learning

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

Pengaruh Perilaku Belajar terhadap Tingkat Pemahaman Akuntansi dengan Kecerdasan Emosional sebagai Variabel Pemoderasi

The purpose of this study was to determine the effect of each variable of learning behavior and emotional quotient at accounting understanding level, and the influence of emotional quotient moderation on the interaction of learning behavior, at accounting understanding level. Sample selection using purposive sampling for 87 samples. Accounting understanding level data is proxied by the average final grade of accounting subjects in the MAKSI program collected from the SIMAK Unud program, while learning behavior and emotional quotient data are response data collected using questionnaires that have met the validity and reliability tests. All data have met the classical assumption test and the regression model has fulfilled the model feasibility test. Regression test results show that each variable of learning behavior and emotional quotiente has a positive and significant effect on the level of accounting understanding, besides that emotional quotiente increases the influence of learning behavior on accounting understanding level.&#x0D; Keywords : Accounting understanding level, emotional quotient, learning behavior.