An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

Learning Behaviors and English Language Needs of Chinese Postgraduate Students Studying in Thai University

This study is aimed at investigating Chinese postgraduates`learning behaviors,language problems and needs,and also the ways they deal with these problems.It attempts to analyze factors that may affect the way they learn.A set of questionnaires and interviews were used in the study.Implications are then discussed in learning styles and the Chinese culture of learning.

A study of the effects of students' cognitive styles on their learning behaviors of online database search strategies

Purpose: Based on a quasi-experimental design, this study sought to investigate the effects of two different cognitive styles, field independence and field dependence, on students' learning behaviors of online database search strategies.Design/methodology/approach: An experiment was carried out among senior students in a Chinese university.Findings: The field independent(FI) subjects performed better in terms of their search strategy scores. When comparing how many people in each cognitive style group learned the targeted search strategies, more field dependent(FD) subjects were successors, whereas the FI subjects were more inclined to learn from their past experience. When analyzing the reasons for the subjects' selection of search strategies, we found that the FI subjects demonstrated more rational thinking behaviors than the FD subjects.Research limitations: Only 28 students participated in the study, which was a relatively small sample size. A larger sample will give more information and therefore more precise results.Practical implications: This research can provide some suggestions to the information system designers on how the system interface can be better designed to suit the cognitive styles of different users.Originality/value: So far, few studies have been published about the effects of users' cognitive styles on their learning behaviors of online database search strategies.

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.

Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs

Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.

The Influence of Second-Language Self-Orientation on Learning Motivation of Medium Level EFL Proficiency

EFL motivation is a hot research field of second language learners.In recent years,many researchers have focused on Dornyei’s second language self-system as a theoretical framework.The purpose of this study is to examine the predictive power of self-orientation of college medium level English learners to EFL learners’motivation,and to find ways to enhance their motivation and EFL proficiency.The subject of this paper is a medium level students of an ordinary college.Using linear regression analysis to collect data through questionnaires,it is found that the self-orientation of the subjects can explain the predictive effect on the actu⁃al learning behavior is not ideal.Ideal L2 self and L2 self-confidence are insufficient,and corresponding learning strategies are lacking,which limits self-directed ability to predict L2 motivation.

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.

Efficiency of Managers as Role Models:A Social Learning Theory Perspective

This systematic literature review aimed to analyze and synthesize studies that indicated the importance of behavioral observation in the organizational context.Based on Social Learning Theory and by considering relevant recent findings and theories,the impact of managers as role models for employees is researched and analyzed.The importance of this topic is to determine ways that learning and enhancing performance in the workplace can be applied for people management development.The literature for theory was numerous,however studies on the particular topic were limited and not expanded in the organizational context.The key message of this review is that the impact of managers and leaders can be positive and progressive both for the employees and for the organization.

Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of education.This study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic year.We have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these outcomes.Our goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures promptly.Experimental results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

The Impact of Dominant Predictors on University Students’Creativity through Creative Self-efficacy in Shaanxi China:The Moderating Role of Motivation

Studies on creativity have identified critical individual and contextual variables that contribute to individuals’creative performance.Ceative self-efficacy has also served as a critical mediating mechanism linking a variety of individual and contexual factors to people’s creative performance.However,the factors influence the relationship between creative selfefficacy and creativity have not yet been systematically investigated.In this study,the author explores potential processes that motivation moderate the relationship between creative self-efficacy and university students creativity under the effects of three dominant predictors like openness to experience,learning goal orientation and team learning behavior.

柯式培训评估模型实务应用——以F电子制造服务公司储备干部培训评估为例

储备干部的培训对于企业在进行基层人力资源开发是相当重要的一环,对于发展公司的骨干成员具有一定的影响。本案例以F专业电子制造服务公司为例,并且以柯式(Donald.L.Kirkpatrick)所提出的培训评估模式作为评估架构,作为企业在进行培训效益评估的模式,以利于人力资源发展的精进。

Schisandra N-butanol extract improves synaptic morphology and plasticity in ovarectomized mice

Preliminary work by our research team revealed that Schisandra, a renowned traditional Chinese medicine, causes learning and memory improvements in ovariectomized mice. This activity was attributed to active ingredients extracted with N-butyl alcohol, named Schisandra N-butanol extract. In this study, ovariectomized mice were pretreated with Schisandra N-butanol extract given by intragastric administration. This treatment led to the enhancement of learning, and an increase in hippocampal CA1 synaptic, surface and postsynaptic density. A decrease in the average size of the synaptic active zone was also observed. These experimental findings showing that Schisandra N-butanol extract improved synaptic morphology indicate an underlying mechanism by which the ability of learning is enhanced in ovariectomized mice.

Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering

Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use questionnaires designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning result than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.

Memristor based onα-In_(2)Se_(3)for emulating biological synaptic plasticity and learning behavior

Nowadays,memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses,making them become a new generation of research hotspots for brain-like neurocomputing.In this work,we prepare a memristor based on two-dimensionalα-In_(2)Se_(3)nanosheets,which exhibits excellent electrical properties,faster switching speeds,and continuous tunability of device conduction.Meanwhile,most basic bio-synapse functions can be implemented faithfully,such as short-term memory(STM),long-term memory(LTM),four different types of spike-timing-dependent plasticity(STDP),and paired-pulse facilitation(PPF).More importantly,we systematically study three effective methods to achieve LTM,in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve.Therefore,we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.

A Study of Learning Behavioral Engagement in Live Teaching

Based on the related theories and research results of learning behavioral engagement,this study constructed an evaluation framework of learning behavioral engagement in live teaching,which included 24 indicators in three dimensions:compliance with norms,learning participation and social participation.A small-class live English learning for younger students on the ClassIn was taken as a case study program.Five younger students attended this English learning course of 16 lessons totaling 950 minutes.The preset indicators were preliminarily examined based on the teaching records and the recorded course data.Then,experts in the field of educational technology were invited to develop the learning behavioral engagement dimensions and indicator weightings by using the Analytic Hierarchy Process,and to determine the evaluation indicator system for the evaluation of learning behavioral engagement.Finally,based on this framework,the characteristics of learning behavioral engagement of the case course were analyzed,and the influences of students’individual factors,teaching and environmental factors on learning behavioral engagement in live teaching were investigated.

Effects of subconvulsive electrical stimulation to the hippocampus on emotionality and spatial learning and memory in rats

Objective To observe the effects of repeated subconvulsive electrical stimuli to the hippocampus on the emotional behavior and spatial learning and memory ability in rats.Methods One hundred and eight male Wistar rats were randomized into 3 groups. Animals in group SE (n = 42) were given subconvulsive electrical stimulation to the hippocampus through a constant pulsating current of 100 μA with an intratrain frequency of 25 Hz, pulse duration of 1 millisecond, train duration of 10 seconds and interstimulus interval of 7 minutes, 8 times a day, for 5 days. In the electrode control group or CE group (n = 33), animals were implanted with an electrode in the hippocampus, but were not stimulated. Group NC (n =33) animals received no electrode or any stimulation. The emotional behavior of experimental rats was examined by activity in an unfamiliar open field and resistance to capture from the open field, while the spatial learning and memory ability was measured during training in a Morris water maze.Results The stimulated rats tested 1 month after the last round of stimulation displayed substantial decreases in open field activity (scale: 10. 4±2. 3, P<0. 05) and increases in resistance to capture (scale: 2. 85±0. 56, P < 0. 01 ). The amount of time for rats in group SE to find the platform (latency) as a measurement for spatial bias was prolonged (29±7) seconds after 15 trials in the water maze, P<0. 05). The experimental rats swam aimlessly in all four pool quadrants during the probe trial in the Morris water maze.Conclusions Following repeated subconvulsive electrical stimuli to the hippocampus, rats displayed long-lasting significant abnormalities in emotional behavior, increased anxiety and defensiveness, enhanced ease to and delayed habituation to startlement, transitory spatial learning and memory disorder, which parallels many of the symptoms in posttraumatic stress disorder patients.

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

In response to the imbalance between power generation and demand, demand response(DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers’ DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers’ DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers’ timevarying DR patterns and trace the changes dynamically, we define customers’ DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers’ current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

Towards a Prediction Model of Learning Perfor-mance:Informed by Learning Behavior Big Data Analytics

Using log data of 823 university students collected in two settings:their online learning setting and daily life setting(using campus ID cards for consumption purposes and book-borrowing in the university library),this study created indicators for online learning behavior,early-rising behavior,book-borrowing behavior and learning performance prediction.Five machine learning models were employed to analyze learning performance prediction,with the additional use of Boosting and Bagging to improve the accuracy of the prediction model.The predictability of the proposed model was also compared with that of both the Artificial Neural Network model and the Deep Neural Network model.At the same time,a classification rule set was established by combining decision tree and rule model,and a learning behavior diagnosis model combining decision tree and deep neural network was constructed.Findings showed that multi-scenario behavior performance indicators had strong predictive capabilities while the Deep Neural Network model had the highest prediction accuracy(82%)but was most time-consuming.The model based on the rule set is highly accurate,readable and operable and may be conducive to making accurate teaching interven-tions and resource recommendations.

Human stochastic closed-loop behavior for master-slave teleoperation using multi-leap-motion sensor

abstracting human control strategy in controlling dynamically stable robots.J Intell Robot Syst,2009,55：39-54

Analysis of Learner’s Behavior Characteristics Based on Open University Online Teaching

The analysis on the learning behavior characteristics based on big data is beneficial for improving the learning resource construction,teaching mode and interactive mode of online course platforms.Multiple aspects of analysis were conducted on nearly three million pieces of learning behavior data,which is from seven courses of 3,315 learners in the same major at a university.According to the quantity of course resources and policy of course scoring,four typical learning behaviors were selected,and the correlation between final exam results and learning behavior were analyzed.The analysis of behavior influences on the final exam results were also conducted.The analytical results give suggestions for online teaching and learning.