A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU

Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.

The Relationships between the Short Video Addiction,Self-Regulated Learning,and Learning Well-Being of Chinese Undergraduate Students

Background:With the global popularity of short videos,particularly among young people,short video addiction has become a worrying phenomenon that poses significant risks to individual health and adaptability.Self-regulated learning(SRL)strategies are key factors in predicting learning outcomes.This study,based on the SRL theory,uses short video addiction as the independent variable,SRL strategies as the mediating variable,and learning well-being as the outcome variable,aiming to reveal the relationships among short video addiction,self-regulated learning,and learning well-being among Chinese college students.Methods:Using a cross-sectional study design and applying the snowball sampling technique,an online survey was administered to Chinese undergraduate students.A total of 706 valid questionnaires were collected,with an effective response rate of 85.7%.The average age of the participants was 20.5 years.Results:The results of structural equation modeling indicate that 7 hypotheses were supported.Short video addiction was negatively correlated with self-regulated learning strategies(preparatory,performance,and appraisal strategy),while SRL strategies were positively correlated with learning well-being.Additionally,short video addiction had a mediating effect on learning well-being through the three types of SRL strategies.The three types of SRL strategies explained 39%of the variance in learning well-being.Conclusion:Previous research has typically focused on the impact of self-regulated learning strategies on media addiction or problematic media use.This study,based on the SRL model,highlights the negative issues caused by short video addiction and emphasizes the importance of cultivating self-regulation abilities and media literacy.Short video addiction stems from failures in trait self-regulation,which naturally impairs the ability to effectively engage in self-regulation during the learning process.This study confirms and underscores that the SRL model can serve as an effective theoretical framework for helping students prevent short video addiction,engage in high-quality learning,and consequently enhance their learning well-being.

Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.

Civil Engineering Students'English Metacognitive Strategies,Autono-mous Learning Competence and ListeningPerformance

In order to improve Englishlistening more effectively, the application status of metacognitive strategies and autonomouslearning competence is exploredbased on a questionnaire investigation among 120 civil engineering major in BJUT. The methods of correlation analysis and an independent sample t-test are employed.The results indicated that 1) their metacognitivestrategies, and the planning, monitoring strategy have positive significant correlations with the students'listening performance and listening auton-omous learning competence;2) There are significant differences in the use of metacognitivestrategies,the level of autonomous learn-ing competence between different levels of listening performance;3) The more accuracy of self-judgment is, the higher level Eng-lish listening performance is.

Research on the Correlation between the Learning Strategies on English Listening and the Corresponding Performance among College Students

Listening is very important for the learning of a language.We must ensure that there are language standardization and foundation of practice while learning any languages.By improving listening,the language communication ability can be improved.Learning other languages requires listening as the foundation of learning.In college English teaching,listening also takes very important position.However,due to the current situation of Chinese education and the characteristics of students’learning,Chinese students generally have a lower level of listening skills,and usually,most of them score very low in the listening part in level 4 or level 6 of the College English Tests.The root cause of this phenomenon is that listening test in some underdeveloped areas of China is not included as part of the test results of the middle and high school entrance examinations,and very often,listening was neglected in English lessons.As a result,many students end up with poor listening performance result.However,in college,the teaching of listening still follows the traditional education model of the past,and there are many lessons allocated to this aspect of English learning.In addition,there is almost no strategic guidance in the learning process,making students unable to begin on listening practice.Although both teachers and students tried to improve their listening skills,the results were unsatisfactory.Therefore,it is imperative to solve the problem of poor English listening level of students.Experts such as Huang Zidong and Wang Yu believe that strengthening strategic training is currently a very important method to improve students’English listening skills.

Improving Adaptive Learning Rate of BP Neural Network for the Modelling of 3D Woven Composites Using the Golden Section Law

Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.

Impact of Personal Factors on China’s English Learners’ Pro?ciency

With the development of China’s economy and technology in recent years,English has gained a large popularity in China.The research concerning the Second Language Acquisition is growing deeper and deeper.Many scholars home and abroad have shared their thoughts to this topic.From the perspective of Chinese personal learners,three of main factors which influence their Second Language Acquisition will be briefly discussed in this essay,including learning strategy,learners’personality and learners’motivation.

The comparison between traditional Chinese literacy strategies and English vocabulary strategies

There is a positive transfer from native language vocabulary learning strategy to that of the second language. The comparison between them shows that the traditional Chinese character learning strategies have profound effect on English vocabulary learning on the basis of morphology, lexicon as well as discourse categories. If the mutual effect can be applied in English vocabulary learning effectively, positive transfer emerges.

基于flink的在线教学学生聚类分析系统的开发

在线学习中产生的学习行为数据经过处理后,可更好地支持教师选择个性化的教学策略,达到更佳教学效果。利用网络教学平台的学习行为历史数据形成前期学生标签信息,同时构建以Flink框架为核心的学生画像后台,获取学生在网络教学平台中的实时操作数据,不断补充和更新学生数据,完善学生在线学习的行为画像。将学生画像结果应用于教学策略选择,让教师快速识别学习效果不理想的学生,督促其养成良好的学习习惯,制定个性化的帮扶措施,改进教学设计。

神经架构搜索综述

近几年,深度学习因具有强大的表征能力,已经在许多领域中取得了突破性的进展,而神经网络的架构对它的性能至关重要。然而,高性能的神经网络架构设计严重依赖研究人员的先验知识和经验,神经网络参数量庞大,难以设计最优的神经网络架构,因此自动神经架构搜索(NAS)获得了极大的关注。NAS是一种使用机器学习的方法,可以在不需要大量人力的情况下,自动搜索最优网络架构的技术,是未来神经网络设计的重要手段之一。NAS本质上是一个搜索优化问题,通过对搜索空间、搜索策略和性能评估策略的设计,自动搜索最优的网络结构。从搜索空间、搜索策略和性能评估策略这3个方面详细且全面地分析、比较和总结目前NAS的研究进展,方便读者快速了解神经架构搜索的发展过程和各项技术的优缺点,并提出NAS未来可能的研究发展方向。

大学生在线学习动机、学习策略对在线学习绩效影响机制——自我效能感的中介作用

以高校大学生为研究对象,基于887份有效问卷,运用结构方程模型和Bootstrap中介检验法系统探究了大学生在线学习动机、学习策略对在线学习绩效的影响机制。研究表明:在线学习动机直接正向影响在线学习绩效,但没有通过自我效能感间接影响在线学习绩效;学习策略直接正向影响在线学习绩效,同时通过自我效能感间接影响在线学习绩效,自我效能感在学习策略对在线学习绩效正向影响中起完全中介作用;自我效能感是正向影响在线学习绩效的最重要因素,其次是在线学习动机和学习策略。基于此,提出了增强大学生在线学习的自我效能感,适度提升大学生在线学习动机,优化大学生在线学习策略,进而提高在线学习绩效的建议。

基于学生差异性的混合式教学模式研究——以高职计算机专业为例

高职学生学习自觉性差,个体差异性大,在自主学习的过程中存在动力不足、意志力较弱、学习习惯不良等缺点。文中以高职计算机专业学生为调查对象,对学生的学习兴趣、学习态度与学习习惯的差异进行了调查、分析、研究,总结了高职学生在自主学习过程中存在的主要问题及其原因,并提出了相关建议,为教师在混合式教学环境下开展个性化教学提供了参考。另外,以Python基础编程为例,阐述了混合式教学模式下针对学生差异的个性化教学策略、具体实施方案、考核方案等。

个性化教育策略研究——结合自适应学习技术和混合式学习

随着教育技术的发展,混合式学习成为越来越受关注的教育形式。通过构建一个新的理论框架,综合多种教育策略及其在自适应学习技术应用中的优劣,在此基础上提出一系列具有针对性的个性化教育策略与建议,涉及课程设计、教学方法、学习资源、评估与反馈等,旨在帮助教育工作者更有效地利用自适应学习技术以实现混合学习模式下的个性化教育,为教育工作者在自适应学习技术应用中提供有益的指导和建议,推动混合学习模式下个性化教育的发展。

航海类大学生学习困难群体的个性特点及教育对策研究

本文从研究学习绩优学生与学习困难学生的个性差异及学习困难学生的年龄差异入手,分析了学习困难学生的个性特点,并提出建立以提升智力素质为基础的心理素质教育模式;培养学生坚强的意志,提高有恒性;关注低年龄段的学困生,注重其心智发展及个性培养等教育对策。

构建高校学生个性化学习环境的策略与挑战

教育的核心目标是促进学生的全面发展,但每个学生具有独特性和差异性,于是“因材施教,有教无类”的个性化教育理念应运而生,通过实施分层教学、小班教学和学徒制等多元化教学手段,在一定程度上增强了教学效果。随着信息技术的发展,基于个性化学习的自适应学习系统,实现了为每个学生量身定制学习规划、内容及路径的目标。高校以学生为中心构建多元化学习目标、内容、平台、方法、评价等个性化学习环境,通过注重学生自主学习能力的培养、教师教育教学角色的转变、注重创建个性化学习的物理空间、网络空间和实践基地,以及提供个性化学习的咨询服务等策略,激发学生的学习兴趣和潜能,促进学生的全面发展。

基于学科知识图谱的个性化学习模型构建研究

针对个性化学习服务能力不足的问题,该文利用学科知识图谱的理论和方法,提出了基于学科知识图谱的个性化学习模型,阐述了学科知识图谱的构建方法。根据学习者的基本属性和学习特征,构建了学习画像模型,设计出了基于学习者画像的个性化学习路径推荐策略。结果表明,模型的性能符合个性化学习的基本要求,对促进个性化学习具有一定的参考作用。

技术支持的基于深度认知诊断的个性化学习效果研究——以高中数学“排列组合”内容为例

技术促进学习是教育技术学研究的核心问题,但其有效性备受质疑。基于此,文章采用准实验研究方法,面向上海市两所公立高中的607名高二学生展开研究,针对高中数学的“排列组合”章节内容,利用深度认知诊断得到学习者的三阶认知起点(表象阶、理由阶和信念阶),同时据此设计具有针对性的学习干预并结合技术开展实验教学,以检验技术支持的基于深度认知诊断的个性化学习对学业成绩提升的影响。结果显示,相比传统教学,技术支持的基于深度认知诊断的个性化学习显著提高了学习者的数学成绩,且基于更深阶次的认知起点进行干预可以取得更加显著的学习效果。文章通过研究,旨在为一线教学场景实现高效的个性化学习提供实证支持和理论指导。

基于个性化教学的初中语文作业设计

许多学生对于传统的作业设计感到乏味。文章结合个性化教学理念,提出了一种新的初中语文作业设计模式。该模式包含一些开放性和选择性的作业内容,能够激发学生的学习兴趣和学习主动性,有效提高学生的学习效果和学习动力,值得在实际教学中推广应用。

学习者画像模型构建及个性化学习策略推荐

在线学习环境下,通过探索学习者行为偏好可以大幅度降低学习者学习的盲目性,提高学习效率。研究以学习者画像为基础,利用Python语言将在线学习的学习者根据学习特征用K-means算法分为四类,根据个性化学习推荐的精准性、及时性和可实施性原则为学习者推荐个性化的学习策略,并在此基础上通过前后测试卷进行了基于画像的个性学习策略的效果检验。结果表明,个性化学习策略能够帮助学习者有效提高学习成绩。

学习花园:跨学科学习场域设计与实践

个性化学习场域的设计与实践是落实跨学科教学的重要载体。“学习花园”这一创新概念,不仅超越了传统教室的界限,更颠覆了固有的教学模式,将学生的学习空间扩展至教室之外,融入到那些充满教育意义的花园之中。文章阐述“学习花园”个性化学习场域的内涵,分析“学习花园”个性化学习场域的价值,探讨跨学科学习在“学习花园”学习场域的实践策略,旨在提升学生跨学科素养,实现个性化教育理念的落地生根。