Resource Recommendation Research Articles

Deep Knowledge Tracing Integrating Temporal Causal Inference and PINN

Knowledge tracing predicts students’ future performance based on their historical performance data, which is significant for students’ learning resource recommendation, learning path prediction, and other aspects. Students’ knowledge mastery, learning ability, and question difficulty all influence the performance metrics of knowledge tracing. This paper proposes a deep knowledge tracing model that integrates temporal causal inference and the PINN (Physics-Informed Neural Network) model. The model first uses the temporal causality model to explores the causal relationships between students’ knowledge points, which is then combined with the deep learning-based knowledge tracing model for prediction. Next, it treats the logical model as a ’physical model’, adds a loss term, considers the confounding factors caused by students’ answer preferences, and adjusts students’ learning ability through backdoors to obtain more accurate predictions. In the public education datasets ASSISTment2012 and ASSISTchall, the predictive performance of the TLPKT-PINN model is superior to some classical models and LPKT. From the experimental results, we can conclude that considering the degree of mastery of causal knowledge points and adjusting the loss term can improve the accuracy of predicting student grades.

Factors influencing success of virtual reality in Tunisia’s cultural heritage tourism sites: a UTAUT2 approach extension

The tourism industry is evolving rapidly with the emergence of virtual reality (VR), seen as a viable alternative to traditional travel. Our primary aim is to identify the factors influencing the success of VR and provide recommendations for resources, methods, and technology. Our study presents a multi-dimensional conceptual model for the pre-implementation success of VR in the tourism sector. We applied the UTAUT2 model with an additional contextual concept. Data from 352 participants support our model for assessing perceived VR success in Tunisian tourism. The study confirms the significant positive impact of performance expectancy, hedonic motivation, and facilitating conditions on behavioral intention. Personal innovativeness and trust expectancy positively influence performance expectancy. Conversely, effort expectancy negatively affects hedonic motivation. In summary, our research validates the model's suitability for assessing the intention to use VR in tourism activities. This framework is crucial as VR continues to revolutionize the tourism industry.

The application of artificial intelligence in biomechanical feedback and learning effectiveness enhancement in ideological and political education

The application of artificial intelligence (AI) in ideological and political education (IPE) optimizes the learning effect with the help of biomechanical feedback mechanism, and provides technical support for deepening the reform of IPE in colleges and universities. With the help of AI technology, especially its advantages in data analysis and deep learning (DL), accurate matching and personalized recommendation of learning resources can be realized. In this study, an AI-assisted IPE system with biomechanical feedback was constructed. By analyzing students’ physiological reactions and behavior patterns in the learning process, the system can evaluate the learning state and adjust teaching strategies. The key findings show that this learning method combined with biomechanical feedback can significantly improve students’ learning participation, understanding depth and satisfaction, and make IPE more in line with students’ needs.

Online mobile learning resource recommendation method based on deep reinforcement learning

Online mobile learning resource recommendation method based on deep reinforcement learning

Library academic resource recommendation and association analysis using GraphSAGE

Library academic resource recommendation and association analysis using GraphSAGE

Construction of College Students’ Music Interest Platform Based on Internet+

In the context of the gradual improvement of the degree of personalized development, College Students’ interest in music shows a diversified trend, which makes the construction of relevant music platforms fully consider the actual needs of individual users and recommend resources corresponding to their interests. This paper puts forward the research on the construction of College Students’ music interest platform based on Internet [Formula: see text]. On the premise of analyzing the value proposition of the cultural industry platform model and the emphasis on customer goals under the background of “Internet [Formula: see text]”, this paper designs a platform structure including resource management module, College Students’ user management module, resource recommendation module and resource startup module. The big data technology under the background of “Internet [Formula: see text]” is used to analyze the MIDI audio characteristics and user behavior characteristics of resources. The binary community is used to establish the matching relationship between the two, determine the recommended resources, and introduce the heat parameter to avoid the cold start problem. The test results show that the processing efficiency and response time of the design platform for user behavior and user requests can meet the needs of practical applications, and has high stability. The collection map, Download map and share map of the recommended results of the platform are the best outputs in the test stage.

Human Resource Intelligent Recommendation Method based on Improved Decision Tree Algorithm

Due to the current complexity of human resource recommendation methods and the large number of recommended errors, it is difficult to meet the actual requirements of human resource recommendation. Therefore, this paper proposes an intelligent human resource recommendation method based on an improved decision tree algorithm. Firstly, based on the improved decision tree algorithm, classify resources to accelerate learning speed and avoid errors during learning. Secondly, collect human resources data and store the preprocessed human resources data in the human resources data warehouse. Finally, successful implementation of intelligent human resource recommendations. The experimental results are as follows: After using the human resource intelligent recommendation method based on the improved decision tree algorithm, the number of successful hires was higher than the other two methods. Compared with the method proposed in this paper, method 1 reduced 1098 positions, and method 2 reduced 1008 positions. The effectiveness of the human resource intelligent recommendation method based on the improved decision tree algorithm has been demonstrated.

Using UX Testing to Optimize Discoverability of Non-traditional Resources

Objective – The accessibility of non-traditional resources presents ongoing challenges for users and librarians. This study investigates methods for optimizing metadata and the placement of search results to enhance the discoverability of these resources within library systems. Researchers conducted A/B testing to compare two features of Ex Libris Primo: the Resource Recommender and Discovery Import Profiles. The objective was to enhance user access to a broader range of informational assets beyond conventional collections. This study posed the research question: Is inclusion in the results list (Discovery Import Profiles) or are visually appealing advertisement-style cards above results (Resource Recommender) a more effective method for discovery of non-traditional library resources? Methods – Researchers identified four key resource types for testing: librarians, frequently asked questions (FAQs), databases, and research guides. An A/B test was conducted with each resource presented in the Discovery Import Profiles and Resource Recommender formats. Following the A/B test, a combined C test was conducted to validate findings. Results – The ad-style cards achieved higher engagement rates, particularly for databases and FAQs, while research guides performed better when embedded directly in search results. This study highlights the strengths and limitations of each method. Databases and FAQs benefited from the visual prominence of the ad-style cards, while research guides were more discoverable within search results. However, minimal engagement with librarians as a resource type across both methods suggests the need for improved tagging and metadata strategies. Conclusion – Findings underscore the importance of institution-specific research and localized assessments to ensure effective implementation of discovery strategies. This study provides a useful method for libraries aiming to enhance the discoverability of their non-traditional resources, ultimately improving user access and satisfaction.

Recommendation Method of Teaching Resources for Professional Music Courses Based on Knowledge Graph

There is an abundance of materials for use in professional music courses, but it can be 1 difficult for consumers to quickly and efficiently obtain the specific knowledge they require. Additionally, there is a general lack of data collected on online learning, which renders the recommendation effect of music course resources insufficient. In this study, we use technologies connected to the knowledge graph in the field of online education in order to create a system capable of recommending acceptable educational materials for use in professional music classes. In order to construct a recommendation model using multi-task feature learning, knowledge graphs are embedded within tasks, and high-order connections between latent features and entities are constructed across tasks using cross-compression units. It is possible to achieve success by recommending relevant course materials for individual students based on their requirements, interests and present skill levels. In terms of its ability to generate suggestions, the proposed knowledge spectrogram-based teaching resource recommendation system for professional music courses outperforms four baseline models on a number of publicly available datasets. This method has some practical utility in the domain of course resource suggestion, and its training time is less than that of the comparison model.

Design and Implementation of an Electronic Journal Resource Recommendation System Integrating User Profile and Knowledge Graph

Design and Implementation of an Electronic Journal Resource Recommendation System Integrating User Profile and Knowledge Graph

Application of collaborative filtering algorithm based on time decay function in music teaching recommendation model.

To address the issues of data sparsity, scalability, and cold start in the traditional teaching resource recommendation process, this paper presents an enhanced collaborative filtering (CF) recommendation algorithm incorporating a time decay (TD) function. By aligning with the human memory forgetting curve, the TD function is employed as a weighting factor, enabling the calculation of similarity and user preferences constrained by the TD, thus amplifying the weight of user interest over a short period and achieving the integration of short-term and long-term interests. The results indicate that the RMSE of the proposed combined recommendation algorithm (TD-CF) is only 8.95 when the number of recommendations reaches 100, which is significantly lower than the comparison model, which exhibits higher accuracy across different recommended items, effectively utilizing music teaching resources and user characteristics to deliver more precise recommendations.

Health professions students’ acceptance and readiness for learning analytics: lessons for educators

BackgroundLearning analytics (LA) is a tool for gathering, analysing, and interpreting data related to student’s learning behaviour, engagement, and performance. As the student data collected by the institutions are used to construct LA, it is essential to understand the acceptance and readiness of the students regarding the implementation of LA.MethodsA sequential mixed method research design was used in this study. A validated questionnaire was distributed to undergraduate students of Medicine, Dentistry and Pharmacy programmes to collect their general views on LA. Focus group interviews with a total of 18 students were conducted to explore their perceptions in depth, followed by thematic analysis of the transcribed data.ResultsGenerally, the students were aware of their demographic data, utilisation of learning management system and academic performance data being collected by the university. They were agreeable for collection of those data which had direct association with their learning to be used for LA. However, they expressed concerns about the privacy, confidentiality, and security of the collected data. Three themes emerged from the interviews, i.e., self-regulated learning, evidence-based decision making and data management. The students perceived that LA could help them to monitor achievement of learning outcomes and provide support for individualised learning paths through recommendations of learning resources and learning motivation. They also opined that LA could help educators and institutions by providing feedback on teaching and learning methods, resource allocation and interventions to create conducive learning environment.ConclusionsLA is a useful tool to support self-regulated learning, however, precautions should be exercised during implementation to ensure data privacy and security.

Call to Action: Responding to COVID-19, Racism, and Student Mental Health on University Campuses

ABSTRACT The onset of the COVID-19 pandemic initiated a cascade of stressors that occurred concurrently with increasing issues of racism and racial injustice events. These compounded healthcare and societal stressors significantly impacted the mental health of individuals worldwide, including university students. To address the needs of students, a survey was administered to undergraduate, graduate, and professional students at a large public university in the southeastern United States. Qualitative data were collected regarding student mental health, recommendations for institutional resources, and support needs therein. Thematic analysis of qualitative responses from 1050 university students indicated six areas students stated they wanted the university to address to support their mental health needs: (1) Communication, (2) Academic Concerns, (3) Mental Health Resources and Connections, (4) COVID-19 Safety, (5) Social Injustice, and (6) Financial Concerns. To address these areas, we propose a set of evidence-based recommendations within the Social-Ecological Framework for colleges and universities to adopt to support student mental health considering all races, backgrounds, and experiences. Additionally, we propose strategies for university and college counseling centers to provide an essential leadership role on campuses to move these recommendations forward and positively impact the mental health of students.

Adoption of Artificial Intelligence in Public and Private Libraries of China: Determinants, Challenges, and Perceived Benefits

Artificial intelligence (AI) has much significance in different industries, including education. Due to increasing technological advancement, universities show their growing concerns about adopting several facilities linked with AI, specifically in their library management systems. Motivated by such an emerging trend, this research was conducted to examine AI adoption in both public and private libraries in China. The study explored the existing landscape of AI powered services in libraries, the perception of AI benefits, challenges related to AI adoption, and key determinants of such adoption for the targeted libraries. Using a questionnaire technique, the study collected data from different public and private libraries for which a sample of 154 respondents was finalized over the time span of 4.5 weeks. The analysis of the collected data shows that both the situations of launching and planning to launch different AI-related services were observed in Chinese libraries. Moreover, participants also confirmed several benefits of AI, including the enhancement of library operations by improving search accuracy, automating repetitive tasks, offering personalized resource recommendations, revealing usage trends through analytics, curating digital collections, improving cataloging, digitizing rare materials, and providing quick responses via chatbots, thereby allowing librarians to conduct their other more complex tasks. Additionally, the study confirmed several challenges associated with AI adoption. The results also revealed that along with other factors, the support from university administration, maturity and reliability of AI applications, and training and human resource management are positive and significant determinants of AI adoption. On the contrary, funding/cost associated with implementation and innovative services/alignment with technological trends reflected negative coefficients for AI adoption in Chinese libraries.

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Fuzzy clustering algorithm-based teaching resource recommendation method for automotive engine overhaul

Fuzzy clustering algorithm-based teaching resource recommendation method for automotive engine overhaul

Enhancing University English Teaching through Mobile Interactive Applications from an Educational Technology Perspective

In the context of globalization and rapid advancements in information technology (IT), the application of educational technology in higher education has become increasingly prevalent. Particularly in university English teaching, mobile interactive applications have emerged as a significant teaching model. These applications transcend the constraints of time and space, providing students with a flexible learning environment that promotes interaction between teachers and students, thereby enhancing learning outcomes. However, existing research has primarily focused on the technological development and effectiveness evaluation of mobile interactive applications, neglecting the impact of student interaction and trust on learning outcomes. Additionally, there is a lack of dynamism and personalization in the recommendation of educational resources and study partners. This study constructs global and local dynamic trust models for mobile interactions, calculates trustworthiness based on mobile interactive behaviors, and integrates mobile interaction trust relationships and behaviors with a dynamic English recommendation algorithm, thereby providing a new theoretical framework and a practical approach for university English teaching, aiming to enhance the teaching effectiveness and improve students’ learning experiences.

Review Paper on “Jarvie: AI-Driven Mental Health Companion”

With the growing prevalence of mental health issues, many individuals still face barriers to accessing timely and effective support. This paper presents an innovative AI-powered Mental Health Companion aimed at providing personalized, real-time mental health assistance. By utilizing advanced natural language processing (NLP) techniques and machine learning models, the system interacts with users through meaningful conversations, offering customized coping strategies, mood tracking, and resource recommendations. The solution incorporates techniques such as sentiment analysis and adaptive learning to continuously refine responses based on user behavior and emotional states. The companion's effectiveness will be measured through metrics including user engagement, response accuracy, and its impact on emotional well-being. This scalable, on- demand tool offers the potential to significantly expand access to mental health support, reducing the strain on traditional services. Keywords - Artificial Intelligence, Mental Health, Natural Language Processing (NLP), Machine Learning, Chatbot, Mental Health Companion, Sentiment Analysis, Conversational AI, Generative AI.

Research on Optimization Path of Higher Education Management Based on Collaborative Filtering Algorithm

In order to better realize the optimization of university education management, this paper puts forward the research on the optimization path of university education management under collaborative filtering algorithm. The optimization of higher education management is divided into several management directions. First, the teaching resource recommendation management, using collaborative filtering algorithm based on emotional tendency, analyzes the comments of users of teaching resources, and recommends the teaching resources to users next time according to the emotional tendency obtained from the analysis. Second, the library book recommendation management, constructing the characteristic model of library book borrowing users, uses collaborative filtering algorithm to generate nearest neighbors, calculating the similarity of users, and recommending books that meet their characteristics for users according to the similarity. Finally, the management of personalized course selection recommendation in colleges and universities, which uses the historical information of students to establish the evaluation matrix of course selection, searches the nearest neighbors according to the evaluation matrix, and produces the personalized course selection recommendation results of the students according to the nearest neighbors. According to the experiment, the recommended users can be satisfied with the recommended results in different directions of education management. After applying this method, the overall quality of college students has been significantly improved, the expenditure of education management in colleges and universities has been significantly reduced, and the optimization effect of education management in colleges and universities has been remarkable.

Preparing for Virtual Interviews: A Pilot Study to Understand the Needs of Interviewees.

Virtual interviews play a crucial role in the ranking process. National Resident Matching Program (NRMP) data shows a median number of 23.5 interviews per applicant. Considering this volume of interviews, graduate medical education programs must leverage the virtual pre-interview materials to impact applicants' attitudes and perceptions of the interview beforehand. This study is a secondary data analysis of an anonymous cross-sectional survey collected during two orientation sessions held in June and July 2022. Responses from 123 residents and fellows rated the usefulness of available print and virtual pre-interview resources. Using a 5-point Likert scale, social media sites scored an average usefulness score of 4.0, while the information packet and program website scored 3.88 and 3.90, respectively. This article provides recommendations for restructuring virtual resources to aid residents and fellows, specifically under-represented minorities in medicine (UiM) applicants, in having a quality interview experience.