Learning Resource Recommendation Research Articles

Research on the promotion of intelligent entertainment voice robots in personalized English learning based on data mining and gamified teaching experience

With the development of educational technology and the arrival of the intelligent era, intelligent entertainment voice robots have gradually become a research hotspot in the field of education. This study aims to explore the application of intelligent entertainment voice robots based on data mining in the promotion of personalized English learning, and combine with gamified virtual teaching to improve learners’ learning interest and learning effect. The study uses data mining technology to analyze learners’ learning data, interest preferences and learning performance, and provides learners with targeted learning resources and gamified virtual teaching environment based on personalized recommendation. The study creates a virtual teaching environment presented in the form of games, which contains various interesting tasks, challenges and reward mechanisms. Learners can participate in the virtual teaching environment at the same time, experience the fun and stimulation of games, so as to stimulate the interest and motivation of learning. Learners can engage in interactive speech learning through conversations with intelligent entertainment speech robots. The robot can provide real-time answers to learners’ questions, provide personalized learning suggestions, and provide encouragement and feedback. By interacting with robots, learners can get more flexible and personalized learning support, improving learning effectiveness and satisfaction. Through experiments and data analysis, it is found that intelligent entertainment voice robot based on data mining and gamification teaching experience can effectively improve learners’ learning interest and learning effect. Learners show higher engagement and motivation in personalized recommended learning resources and gamified virtual teaching environments.

Personalised Recommendation of Literary Learning Resources Based on a Mixed Recommendation of Learning Interest and Contextual Awareness

In an effort to improve the efficiency and recommendation accuracy of mobile learning resources, the study proposes a hybrid mobile learning strategy based on Collaborative Filtering (CF), context and interest. Analyse from the perspective of situational awareness, construct a personalised recommendation model for text learning resources based on GimbalTM, and obtain a recommendation form. The experimental results show that the RMSE and MAE of Context-Collaborative filtering (C-CF) are lower than those of traditional CF. The Precision and Recall values of C-CF are higher than those of CF at 10 s, the recommendation growth rates of traditional CF and C-CF are 2.09% and 1.67%, respectively. The Gimbal software enables a certain degree of learner location detection and can trigger contextual rules based on time and location contexts to provide users with personalised text-based learning resources. The research results indicate that in specific applications, over time, under the recommendation system, students’ grades steadily increase, which is also beneficial for improving their learning efficiency.

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.

Multi-knowledge enhanced graph convolution for learning resource recommendation

In recent years, E-learning has gained immense popularity as a prominent mode of education. However, accurately recommending learning resources from a vast amount of data remains a significant challenge. This study addresses two primary challenges impacting recommendation performance. Firstly, an imbalance exists between the abundance of available learning resources and the limited interaction behavior of learners. Secondly, the existing algorithms often overlook dynamic preference information, focusing primarily on learners’ short-term, static preferences only by learning interactive behavior but disregarding the multi-correlation between learning resources and learners. To tackle these challenges, we propose MkEGC (Multi-knowledge Enhanced Graph Convolution), a novel framework for learning resource recommendation. We approach the recommendation process as a Markov decision process. Initially, we construct a dual knowledge graph convolutional network, operating in learning resource-knowledge and learner-knowledge domains. This network facilitates the extraction of vector features from learning resources, enhances the learner vector representation, and captures higher-order preferences. Subsequently, we design hierarchical and attention weighting strategies to effectively extract latent hierarchical information from the knowledge graph. Finally, we integrate the learning resource state, the learning interaction state, and the sequence state to represent a multi-dimensional learner-state within the Markov decision framework, enabling prise learning resource recommendations. To validate the effectiveness of MkEGC, we conduct extensive experiments, comparing multiple sets of metrics with six state-of-the-art recommendation algorithms, utilizing two real-world datasets.

Research on the application of artificial intelligence aesthetics in the cultivation of aesthetic literacy of art-normal students

The aesthetic literacy of art-normal students is an important aspect of cultivating outstanding art education talents. However, the traditional aesthetic education model has problems, such as low personalization and unclear evaluation standards. Therefore, it is of great significance to study the application of artificial intelligence (AI) aesthetics in the cultivation of aesthetic literacy of art-normal students. This research uses literature review and analysis methods to systematically sort out the relevant research results and theoretical foundations of artificial intelligence aesthetics and the cultivation of aesthetic literacy of art-normal students and discusses the application methods of artificial intelligence aesthetics in the aesthetic cultivation of art-normal students. The research has found that artificial intelligence aesthetics can provide personalized learning and guidance for art-normal students and improve the learning effect by recommending learning resources and works suitable for their needs and levels.

Geometric Matrix Completion via Graph-Based Truncated Norm Regularization for Learning Resource Recommendation

In the competitive landscape of online learning, developing robust and effective learning resource recommendation systems is paramount, yet the field faces challenges due to high-dimensional, sparse matrices and intricate user–resource interactions. Our study focuses on geometric matrix completion (GMC) and introduces a novel approach, graph-based truncated norm regularization (GBTNR) for problem solving. GBTNR innovatively incorporates truncated Dirichlet norms for both user and item graphs, enhancing the model’s ability to handle complex data structures. This method synergistically combines the benefits of truncated norm regularization with the insightful analysis of user–user and resource–resource graph relationships, leading to a significant improvement in recommendation performance. Our model’s unique application of truncated Dirichlet norms distinctively positions it to address the inherent complexities in user and item data structures more effectively than existing methods. By bridging the gap between theoretical robustness and practical applicability, the GBTNR approach offers a substantial leap forward in the field of learning resource recommendations. This advancement is particularly critical in the realm of online education, where understanding and adapting to diverse and intricate user–resource interactions is key to developing truly personalized learning experiences. Moreover, our work includes a thorough theoretical analysis, complete with proofs, to establish the convergence property of the GMC-GBTNR model, thus reinforcing its reliability and effectiveness in practical applications. Empirical validation through extensive experiments on diverse real-world datasets affirms the model’s superior performance over existing methods, marking a groundbreaking advancement in personalized education and deepening our understanding of the dynamics in learner–resource interactions.

Research on the realization path of ideological and political education goals in colleges and universities based on Bayesian decision theory

Abstract With the continuous development of innovative education and the growth of the demand for personalized learning, this paper explores the evaluation of students’ learning ability and resource recommendation methods in Civic Education under the guidance of OBE (student-centered, outcome-oriented, continuous improvement) educational philosophy to better achieve the educational goals. By constructing a learning ability evaluation model for Civic Education, this paper establishes a learning ability evaluation and Civic Education resource recommendation model based on Bayesian decision making by using Bayesian Decision Theory’s a posteriori probability for uncertainty reasoning and combining the personalized recommendation function of collaborative filtering algorithm. By analyzing the data of a university’s ideological and political education course in the fall of 2020, this paper not only realizes the accurate evaluation of students’ learning ability, but also tests the model’s effectiveness in learning ability evaluation and resource recommendation. The findings show that students perform well in effective communication, collaboration and independent learning ability, whose evaluation mean values are 0.62, 0.58 and 0.55, respectively. In addition, some students’ learning ability significantly improves after receiving the recommended test questions, with the increase ranging from 34.36% to 36.67%. These findings demonstrate the model’s excellent learning ability evaluation and resource recommendation performance. The research in this paper provides valuable references for evaluating students’ learning ability and resource recommendation in Civics and Political Science education, which can help further improve students’ learning effectiveness and achieve educational goals.

SPOC Flipped Classroom Blended Teaching Reform Practices in the Context of Digital Transformation

Abstract The article establishes a blended teaching model of SPOC flipped classroom in colleges and universities, and uses probability matrix decomposition and time effect techniques to personalize the recommendation of combined learning resources. Taking the English teaching course in University A of City D as a case study, the study investigated the effects of the teaching model on students’ essential quality, basic skills, learning attitude and satisfaction. The study results showed that in the experimental class, the average score of students in the self-introduction expression program was 16.46 points higher than that of the control group. In addition, the emotional experience improved the most before and after the experiment, and the improvement in the experimental class amounted to 36.41 percentage points. Meanwhile, over 80% of the students expressed great satisfaction with this teaching mode. The study shows that relying on the SPOC flipped classroom blended teaching mode not only enhances the attractiveness of the course to students, but also significantly improves students’ learning effectiveness and satisfaction.

A study on the application of quantum computing in improving the efficiency of English language learning

Abstract The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p<0.5) indicated a significant difference from the traditional method. Among them, the performance enhancement rates of superior and intermediate students were more than 45% and 62%, respectively, which effectively enhanced the efficiency of English language learning. After this study, it can provide a high reference value for the application of quantum machine algorithms in the field of education.

Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment

Abstract The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.

MOOC English Online Learning Resource Recommendation Algorithm Based on spectral clustering and matrix decomposition

MOOC English Online Learning Resource Recommendation Algorithm Based on spectral clustering and matrix decomposition

WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learning. Unsupervised educational concept extraction methods based on pre-trained models continue to proliferate due to ongoing advances in semantic representation. However, it remains challenging to directly apply pre-trained large language models to extract educational concepts; pre-trained models are built on extensive corpora and do not necessarily cover all subject-specific concepts. To address this gap, we propose a novel unsupervised method for educational concept extraction based on word embedding refinement (i.e., word embedding refinement–based educational concept extraction (WERECE)). It integrates a manifold learning algorithm to adapt a pre-trained model for extracting educational concepts while accounting for the geometric information in semantic computation. We further devise a discriminant function based on semantic clustering and Box–Cox transformation to enhance WERECE’s accuracy and reliability. We evaluate its performance on two newly constructed datasets, EDU-DT and EDUTECH-DT. Experimental results show that WERECE achieves an average precision up to 85.9%, recall up to 87.0%, and F1 scores up to 86.4%, which significantly outperforms baselines (TextRank, term frequency–inverse document frequency, isolation forest, K-means, and one-class support vector machine) on educational concept extraction. Notably, when WERECE is implemented with different parameter settings, its precision and recall sensitivity remain robust. WERECE also holds broad application prospects as a foundational technology, such as for building discipline-oriented knowledge graphs, enhancing learning assessment and feedback, predicting learning interests, and recommending learning resources.

A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning

This paper mainly studies the content of the recommendation algorithm of learning resource courses in online learning platforms such as MOOC and mainly introduces the automatic encoder neural network that integrates course relevance to realize the personalized course recommendation model. The authors first introduce how to embed a course relevance decoder in an autoencoder neural network. Secondly, the proposed confidence matrix method is introduced to distinguish the recommendation effect of the learned to the unlearned courses, and the training process of the model is introduced. Then, the design content of the experiment is introduced, including the model structure, comparative experiments, parameter settings, and evaluation indicators. Finally, the experimental results are analyzed in detail from the horizontal and vertical aspects. It is hoped that this research can provide a reference for personalized recommendation of learning resources based on deep learning technology and big data analysis.

A Perceptual Machine Model Based Approach to Recommending Online Learning Resources

Abstract The bias values of various learning resources are computed using neuron excitation functions based on the perceptual machine model in this paper. Each learning sample is calculated using the weight vector value of each layer in the learning resources. The difference between the output result of the network and the expected value is calculated and converted into the minimum value of the loss function for solving the normalized processing of the weight matrix of the learning resources. It is found that the average square root error in the online learning resources is 0.0897, the decreasing rate is 35.28% compared with the empirical mixing method, and the bias of the online resource recommendation model is 0.2453, which indicates that the proposed model can learn the mixing weight matrix more quickly and obtain a better mixing analysis field for more accurate and personalized learning resource recommendation.

The Application of Artificial Intelligence Teaching Software in College English Teaching

Abstract This paper first proposes English learning software based on personalized recommendations to enhance students’ interest and motivation in learning college English based on artificial intelligence teaching. Secondly, it consists of constructing a learner model, an English learning resource personalized recommendation algorithm to constitute an English learning resource recommendation model, and using a collaborative decomposition algorithm and a regularization algorithm to integrate the potential, multifaceted trust relationship between users with the traditional scoring matrix, a process that emphasizes that trust between users in English teaching varies by different factors and strengths. Then, a personalized recommendation-based university English learning software was designed, and English learning software applications were analyzed by example. The results showed that the adjusted means of pre and post-test scores of the control group, experimental group one, and experimental group two were 77.37, 80.94, and 75.12, respectively, and the statistical score result F=5.33, P<05 indicated that there was a significant difference in the change of English performance of these three groups, which verified that the teaching software had a facilitating effect on the improvement of English teaching quality.

Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

This study aims to solve the problem of limited learning efficiency caused by information overload and resource diversity in online course learning. We adopt a recommendation algorithm that combines knowledge graph and collaborative filtering, aiming to provide an application that can meet users’ personalized learning needs and consider the semantic information of learning resources. In addition, this article collects and models implicit data in online courses and compares the impact of video and text learning resources on user learning needs under different weights in order to deeply understand the different contributions of video and text learning resources to meeting learning needs. The experimental results show that the video high-weight experimental group performs better than the text high-weight experimental group; students tend to prefer video resources. This experiment can help students cope with the challenges brought by numerous types of learning resources and provide personalized and high-quality learning experiences for learners. At the same time, adjusting and innovating teaching models for teachers has great reference value.

A New Collaborative Filtering Recommendation Algorithm for Course Resources Based on Language-Scoring Data

As digital tools and online resources are widely used in education these days, providing students with personalized learning resource recommendations is now an important task for educators- however, the currently available recommendation algorithms created based on language-scoring data are deficient in aspects of sentence similarity calculation and students’ acceptance of new knowledge, so this study proposed an automatic language-scoring method based on the improved sentence similarity calculation; then, combining with the language-scoring data of students, a new collaborative filtering recommendation algorithm of course resources was proposed for changes in the effect of students’ acceptance of new knowledge. The proposed new algorithm can more accurately evaluate students’ language ability, with students’ interests and capacity taken into consideration, and it can give more suitable and personalized recommendations of learning resources for students. In terms of algorithm design, both efficiency and accuracy have been taken into account and balanced, so the algorithm is applicable to large-scale data processing and real-time recommendation. This study not only helps in improving students’ learning effect, but also provides useful references for educational institutions to optimize course design and teaching methods. Research findings of this study can be used in other disciplines and fields as well.

A generic architecture of an affective recommender system for e-learning environments

AbstractPersonalization of suggestions of contents plays a key role in adaptive virtual learning environments. Good recommendations can raise the interest of students in the learning process, while, on the other hand, bad recommendations can have catastrophic results for the learning process. The affective state of the student is a very influential factor in the learning process. In this work, a generic architecture of an affective recommender system for e-learning environments is developed, to serve as a guide for future implementations of this kind of recommender system. Here, the affective characteristics of students are represented by their personalities, learning styles, emotional states, and expertise levels. Thus, the main contribution is the proposition of a generic architecture of an affective recommendation system for the educational field. The architecture is completely modular, which gives it great flexibility because the emotion engine is separated from the personal characteristics engine, and is not based on specific models of emotions. This work finishes with examples of use cases of the architecture. According to the results in these examples, our architecture is capable of analyzing the polarity of academic documents based on their content, determining the personal characteristics of students (including their emotions), and from there, recommending learning resources to students considering emotions as the main element of the process.

Research on the Construction and Application of Knowledge Graph of Digital Resources in Vocational Colleges

<p>With the development of digital education, colleges and universities are facing challenges in managing and utilizing digital resources. As an effective way of knowledge organization and representation, Knowledge graph can transform digital resources into structured entities, attributes and relationship forms, and provide personalized learning support and resource recommendation. By analyzing the characteristics, construction status and existing problems of digital resources in vocational colleges, this paper puts forward the overall plan and methods of digital resources knowledge graph construction, including data collection and sorting, knowledge graph construction, verification and optimization, and gives the general process design of knowledge graph construction; Then, this paper discusses the typical application scenarios of digital resources knowledge graph in vocational colleges, such as learning resource recommendation, path planning, teaching assistance and resource sharing; Finally, the challenges in this field, including data quality, knowledge graph updating and maintenance, privacy and security, are discussed, and the future trends and research directions are prospected. The research results are of great significance for promoting the informatization and intelligent development of vocational college education.</p> <p> </p>

Design and Implementation of a Learning Resource Recommendation System based on User Habits Based on GNN

This project aims to design and implement a learning resource recommendation system based on Graph Neural Networks (GNN). The system utilizes user learning habits as a foundation to provide personalized learning resource recommendations. By collecting and preprocessing user learning history data, and constructing a user-resource relationship graph, the GNN model is used to learn the representation vectors of users and resources. Combined with user habit features, appropriate recommendation algorithms are employed to recommend learning resources that align with their interests and habits.