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Çakir, A., Ince E. Yilmaz, and Y. Babanyyazova. "Adaptive Question Recommendation System Based on Student Achievement." International Journal of Trend in Scientific Research and Development 4, no. 1 (2019): 1034–38. https://doi.org/10.5281/zenodo.3609908.
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In this research, it is aimed to determine the success level of the students according to the answers given in the previous exams with TOPSIS method, to send the question to the related student by the authorized teacher according to the determined success level, to analyze the students periodical weekly monthly yearly performance with graphical data and to create a data bank and to determine the overall performance of the student. Developmental research technique was used, which is one of the design based research method derivatives including the design of educational tools. This study consists of question suggestion and question selection tool components. Adaptive question suggestion system perform generation, storage, classification, disclosure and publication of the content. The software has been successfully tested by 350 students and teachers by using 1000 questions prepared by the educator about the courses and subjects previously defined. As a result, it is seen that students check all the questions related to courses and subjects in the application database and submit the most appropriate question to the requestor in response to the question requests. Also it is observed that the TOPSIS algorithm quickly determined the desired number of questions from the question bank formed by the authorized teacher in accordance with the required criteria, and the process of creating a trial or subject test was performed successfully. ‡akir, A. | Yilmaz Ince, E. | Babanyyazova, Y. "Adaptive Question Recommendation System Based on Student Achievement" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29789.pdf
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Chen, Yunxiao, Xiaoou Li, Jingchen Liu, and Zhiliang Ying. "Recommendation System for Adaptive Learning." Applied Psychological Measurement 42, no. 1 (2017): 24–41. http://dx.doi.org/10.1177/0146621617697959.
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An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.
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Zhao, Yang, Yaqin Fan, Mingrui Yin, and Cheng Fang. "Research on the Construction of a Student Model of an Adaptive Learning System Based on Cognitive Diagnosis Theory." International Journal of Digital Crime and Forensics 12, no. 4 (2020): 20–31. http://dx.doi.org/10.4018/ijdcf.2020100102.
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With the promotion of online education, the adaptive learning system has attracted attention due to its good curriculum recommendation function. The student model is an important interface between the adaptive learning system and the user, reflecting the individual characteristics, knowledge status, and cognitive ability of the student. The accuracy of the information in the student model directly affects the quality of the system recommendation service. The traditional student model only judges students based on the basic information and simple test scores. This paper introduces the self-adaptive item bank and adaptive item selection strategy based on the cognitive diagnosis theory that dynamically detects the students' knowledge and analyzes the state according to the answering habits and knowledge mastering status of different students. This paper analyzes and contrasts a variety of traditional cognitive diagnosis theories and proposes a mixed cognitive diagnosis question bank and a selection strategy model to provide strong support for the construction of student models.
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Thompson, C. A., M. H. Goker, and P. Langley. "A Personalized System for Conversational Recommendations." Journal of Artificial Intelligence Research 21 (March 1, 2004): 393–428. http://dx.doi.org/10.1613/jair.1318.
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Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.
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Rogoza, Valeriy, and Yaroslav Zarichnyi. "USING RECOMMENDER SYSTEMS AS PART OF A BROKER OF OBJECT QUESTIONS IN THE ARCHITECTURE OF A SERVICE-ORIENTED SYSTEM." Інфокомунікаційні та комп’ютерні технології, no. 2(6) (2023): 78–83. http://dx.doi.org/10.36994/2788-5518-2023-02-06-09.
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The paper examines the issue of improving the efficiency of service implementation in a service-oriented architecture (SOA) by integrating recommendation systems into the object request broker. The authors analyze the key problems that arise during the operation of two components of SOA: the service discovery pattern on the server side and the service registry usage pattern.For the service discovery pattern, the identified problems include the complexity of configuring the load balancer, the need for individual traffic balancing due to service heterogeneity, difficulties in balancing the load on the broker, the dynamic nature of request processing, as well as the complexity of monitoring and analyzing requests for anomaly detection and load forecasting.For the service registry usage pattern, the authors identified problems with unstructured data about services, lack of metadata about their functional capabilities, interfaces, availability, and quality, as well as limitations in service filtering mechanisms, which complicates the search and selection of services.As a solution, it is proposed to include a recommendation system in the load balancer and service registry. For the load balancer, it will provide traffic distribution optimization, automatic anomaly detection, load forecasting, resource utilization optimization, and adaptive management of request processing processes.For the service registry, the recommendation subsystem will allow dynamic updating of recommendations based on demand, grouping users by interests, providing context-sensitive recommendations, enriching user profiles, using collaborative filtering, and evaluating recommendation quality based on user feedback.The integration of recommendation systems can significantly improve the efficiency, performance, and user experience of SOA through personalized recommendations of relevant services that meet their specific needs. The authors consider this an important step in the development of service-oriented architecture.
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Yang, Xi, Zhihan Zhou, and Yu Xiao. "Research on Students’ Adaptive Learning System Based on Deep Learning Model." Scientific Programming 2021 (December 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6593438.
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With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.
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Rishi Venkat, Darshana Suresh, Anu Rai, Shubham Metha, and Dyuti Dave. "Transforming learning through artificial intelligence: Evolution of guided learning systems." International Journal of Science and Research Archive 13, no. 2 (2024): 4334–40. https://doi.org/10.30574/ijsra.2024.13.2.2045.
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Artificial intelligence powered learning systems are transforming the learning landscape by using advanced algorithms and comprehensive data to deliver a highly personalized and adaptive learning experience. These systems analyze learner behavior, preferences, and outcomes at the macro and micro levels. This allows them to provide tailored content recommendations. adaptive assessment and tailored learning paths. Adaptive systems are those that are able to continuously learn from user interactions and feedback. An AI-powered recommendation system can dynamically adjust recommendations. This ensures that learners receive the most relevant and effective learning resources at all times. These advanced AI models can create personalized content, place interactive learning materials and even simulate a human-led teaching and learning experience. For example, chatbots and AI-powered virtual assistants can engage learners in understanding learner preferences, deciphering learning styles, answering questions and providing real-time feedback. effectively simulating a human teacher. In addition, generative AI can personalize, summarize and modify existing content to customize it based on individual learning capabilities, patterns, preferences, and achievement levels. ensuring that each learner receives a truly customized learning experience as AI continues to evolve.
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Park, Youngsun. "Professor’s Perception on the Functional Indicators of Learning Management System(LMS) to Support Data-Based Adaptive Learning: Focusing on Local S University." Korean Association For Learner-Centered Curriculum And Instruction 24, no. 1 (2024): 231–46. http://dx.doi.org/10.22251/jlcci.2024.24.1.231.
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Objectives The purposes of this study were to find out the professor's perception of the functional indicators of the Learning Management System (LMS) that supports data-based adaptive learningspecify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them.
 Methods Based on literature analysis, the functions of LMS required for adaptive learning were divided into re-porting (dashboard), diagnosis (evaluation and classification), prediction (recommendation), and individualization (non-curricular) areas, and a 34-question questionnaire was created through an expert meeting to ask for func-tional indicators for each area. Next, a Delphi survey was conducted on nine professors at S University, a four-year university in Region B, and a content validity (CVI) analysis was conducted with the data collected through it.
 Results As a result of examining the professor's perception of LMS's functional indicators for each area that sup-port data-based adaptive learning, all of the proposed functional indicators except for some functions in each area satisfied content validity.
 Conclusions The LMS can be utilized to support data-based adaptive learning, and for this, the function of LMS needs to be advanced.
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Moorhouse, D., B. Sanders, M. von Spakovsky, and J. Butt. "Benefits and design challenges of adaptive structures for morphing aircraft." Aeronautical Journal 110, no. 1105 (2006): 157–62. http://dx.doi.org/10.1017/s0001924000001135.
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Abstract The purpose of this paper is to discuss the future of adaptive structures leading towards the concept of a fully morphing aircraft configuration. First, examples are shown to illustrate the potential system-level mission benefits of morphing wing geometry. The challenges of design integration are discussed along with the question of how to address the optimisation of such a system. This leads to a suggestion that non-traditional methods need to be developed. It is suggested that an integrated approach to defining the work to be done and the energy to be used is the solution. This approach is introduced and then some challenges are examined in more detail. First, concepts of mechanisation are discussed as ways to achieve optimum geometries. Then there are discussions of non-linearities that could be important. Finally, the flight control design challenge is considered in terms of the rate of change of the morphing geometry. The paper concludes with recommendations for future work.
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Li, Lianhuan, Zheng Zhang, and Shaoda Zhang. "Knowledge Graph Entity Similarity Calculation under Active Learning." Complexity 2021 (June 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/3522609.
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To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. Also, the target knowledge point is used as the starting point to traverse the knowledge map to generate the best learning path, and the corresponding learning resources and test questions are recommended for the learner in combination with the learner model; finally, this study adopts an architecture for the development of an adaptive learning platform in the environment to realize online tests, score analysis, resource recommendation, and other functions. A knowledge graph fusion system supporting interactive facilitation between entity alignment and attribute alignment is implemented. Under a unified conceptual layer, this system can combine entity alignment and attribute alignment to promote each other and truly achieve the final fusion of the two graphs. Our experimental results on real datasets show that the entity alignment algorithm proposed in this paper has a great improvement in accuracy compared with the previous mainstream alignment algorithms. Also, the attribute alignment algorithm proposed in this paper, which calculates the similarity based on associated entities, outperforms the traditional methods in terms of accuracy and recall.
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Tran, Huy, Tien Vu-Van, Tam Bang, Thanh-Van Le, Hoang-Anh Pham, and Nguyen Huynh-Tuong. "Data Mining of Formative and Summative Assessments for Improving Teaching Materials towards Adaptive Learning: A Case Study of Programming Courses at the University Level." Electronics 12, no. 14 (2023): 3135. http://dx.doi.org/10.3390/electronics12143135.
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It is crucial to review and update course materials regularly in higher education. However, in the course evaluation process, it is debatable what a difficult learning topic is. This paper proposes a data mining approach to detect learning topics requiring attention in the improvement process of teaching materials by analyzing the discrepancy between formative and summative assessments. In addition, we propose specific methods involving clustering and noise reduction using the OPTICS algorithm and discrepancy calculation steps. Intensive experiments have been conducted on a dataset collected from accurate assessment results of the data structures and algorithms (DSA) course for IT major students at our university. The experimental results have shown that noise reduction can assist in identifying underperforming and overperforming students. In addition, our proposed method can detect learning topics with a high discrepancy for continuously improving teaching materials, which is essential for question recommendation in adaptive learning systems.
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Pradit, Katekeaw, and Pallop Piriyasurawong. "Architecture of AI-driven business model on a digital ecosystem." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 3414–27. https://doi.org/10.53894/ijirss.v8i2.6016.
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This research presents an artificial intelligence architecture framework that drives business models in a digital ecosystem using synthetic methods. This architecture focuses on integrating the potential of artificial intelligence to revolutionize the learning process and create new businesses. The system consists of four key components: 1) Recommendation System – analyzes behavior and learning progress to tailor content to individual understanding; 2) Adaptive Test System – adjusts the difficulty level of questions to suit individual learners; 3) Collaboration Tools – allow learners to exchange ideas and develop business models together; 4) Business Intelligence Tools – make practical learning easier and apply it to real-world situations. The system supports data analysis for business decision-making. The evaluation of the system indicates that it is very good (mean = 4.83, S.D. = 0.15). The proposed architecture is developed in a digital ecosystem with the function of facilitating learning and creating business plans for student entrepreneurs. This approach promotes strong governance within higher education institutions, optimizing entrepreneurial development within the various stages of education, testing, practice, and entrepreneurship assessment. This will lead to best practices in the effective use of artificial intelligence tools in such a way as to create an innovative and sustainable educational environment.
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Cokely, Edward T., Mirta Galesic, Eric Schulz, Saima Ghazal, and Rocio Garcia-Retamero. "Measuring Risk Literacy: The Berlin Numeracy Test." Judgment and Decision Making 7, no. 1 (2012): 25–47. http://dx.doi.org/10.1017/s1930297500001819.
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AbstractWe introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies (n=5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers (http://www.riskliteracy.org). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.
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TETIANA, KALASHNIKOVA. "Study of the Evolution of the Theory of Resilience in Application to Territorial Systems." Demography and social economy, no. 4 (January 7, 2025): 65–78. https://doi.org/10.15407/dse2024.04.065.
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In the modern context, the problem of ensuring the resilience of territorial economic systems and the economy of Ukraine as a whole is extremely relevant. The purpose of the article is to study the evolution of the theory of resilience in application to territorial systems, which was proposed by foreign economic theory in response to the challenges of the global financial and economic crisis of 2008 and the period of recovery after it. To form the main postulates of the study, general scientific methods of analysis and synthesis, generalization, classification, system analysis were used. The work analyzes the basic theories of resilience. The main contradictions that were faced by researchers during their development were investigated as well. The views on the question of the ultimate goal of resilience have been investigated. In particular, the evolution from the point of view that this goal is to maintain the state of equilibrium of the regional (urban) economic system, to the abandonment of this thesis, when the ultimate goal is blurred and either is considered as a continuous process of balancing the economic system, rather than returning to a previously existing or a new stable equilibrium condition, or there is a thesis of interrupted equilibrium, which is a sequence of stable forms or pathways of sustainable growth that is interrupted by periodic shocks. Engineering, ecological and evolutionary approaches to regional (urban) resilience have been studied by scientists on the way to finding an analogy from other fields of science, where the phenomenon of “sustainability — resilience “ has been developed quite carefully. The analysis gives the grounds for highlighting the adaptive approach as the most promising and adequate for the purpose of studying the economic content of the territorial aspect of the economy and the formation of relevant practical recommendations. According to the adaptive approach, economic resilience is understood as a multidimensional property, which includes not only the ability of the territorial system to recover from shock, but also the adaptive properties of its economic structure and the ability to restore positive economic dynamics.
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Cha, Seungeon, Martin Loeser, and Kyoungwon Seo. "The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process." Applied Sciences 14, no. 9 (2024): 3672. http://dx.doi.org/10.3390/app14093672.
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The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI.
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Dyulicheva, Y. Yu. "The swarm intelligence algorithms and their application for the educational data analysis." Open Education 23, no. 5 (2019): 33–43. http://dx.doi.org/10.21686/1818-4243-2019-5-33-43.
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The purpose of the paper is the investigation of the modern approaches and prospects for the application of swarm intelligence algorithms for educational data analysis, as well as the possibility of using of ant algorithm modifications for organizing educational content in adaptive systems for conducting project seminars.Materials and methods. The review of the modern articles on the educational data analysis based on swarm intelligence algorithms is provided; the approaches to solving problem of the optimal learning path construction (optimal organization of the learning objects) based on the algorithm and its modifications taking into account the students’ performance in the process of the optimal learning path construction are investigated; the application of particle swarm optimization and its modification based on Roccio algorithm for the reduction of curse dimension in the problem of the auto classifying questions; the application of ant algorithm, bee colony algorithm and bat algorithm for recommender system construction are studied; the prediction of students’ performance based on particle swarm optimization is researched in the article. The modification of ant algorithm for optimal organization of learning objects at projects seminars is proposed.Results. The modern approaches based on swarm intelligence algorithms to problem solving in educational data analysis are investigated. The various approaches to pheromones updating (their evaporation) when building the optimal learning path based on students’ performance data and search of group with “similar" students are studied; the abilities of the hybrid swarm intelligence algorithms for recommendation construction are investigated.Based on the modification of ant algorithm, the approach to the learning content organization at project seminars with individual preferences and students’ level of basic knowledge is proposed. The python classes are developed: the class for statistical data processing; the classfor modifica -tion of ant algorithm, taking into account the current level of knowledge and interest of student in studying a specific topic at the project seminar; the class for optimal sequence of the project seminars ’ topics for students. The developed classes allow creating the adaptive system that helps first year students with a choice of topics of project seminars.Conclusion. According to the results of the study, we can conclude about the effectiveness of swarm intelligence algorithms usage to solve a wide range of tasks connected with learning content and students’ data analysis in the e-learning systems and perspectives to hybrid approaches development based on swarm intelligence algorithms for realizing the adaptive learning systems on the paradigm of “demand learning".The results can be used to automate the organization of learning content during project seminars for the first-year students, when it is important to understand the basic level of knowledge and students’ interest in learning new technologies.
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GRINDE, JOHN, and ANSHUMAN KHARE. "THE ANT, THE GRASSHOPPER OR SCHRÖDINGER'S CAT: AN EXPLORATION OF CONCEPTS OF SUSTAINABILITY." Journal of Environmental Assessment Policy and Management 10, no. 02 (2008): 115–41. http://dx.doi.org/10.1142/s1464333208003007.
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Living within the limits imposed by a finite earth may be the predominant challenge of the 21st century; however there is no agreement on what this means economically or politically. Continually increasing population and per capita consumption within a finite environment is a biologic impossibility. Today many of nature's resources are being harvested at rates where growth is uneconomic and damaging to future production. This paper explores linkages between economics, society and nature as complex adaptive systems in a world of uncertainty. To understand that these self organising systems exist in equilibrium, dependent on feedback loops, is to understand that as humanity destroys system equilibrium we push the entire system to the edge of criticality and perhaps chaos. Consequences of criticality and chaos theory are now thought to follow power laws; wherein the size of any single system disruption is impossible to predict, especially when inter-relationships with other systems are not understood. Understanding the biosphere from a holistic perspective is critical when considering priorities and making wise choices. Thoughts of some environmental thinkers are reviewed for a perspective on the meaning of true sustainability, where it may lead and how we must redesign human systems to adapt to a sustainable world. The authors believe holism exists; that similarities with other fields of study exist in the environmental issues of today; and, that ideas and solutions may be also found in unexpected places. Although no recommendations are made it is hoped that interest is stimulated in answering the question when are we going to act collectively to address some of the present day myriad issues.
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Li, Zhe, Lei Shi, Mingyu Pei, et al. "DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization." PLOS One 20, no. 6 (2025): e0321352. https://doi.org/10.1371/journal.pone.0321352.
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Objective This study aimed to evaluate the effectiveness of a virtual reality (VR) training system for mass casualty management, integrating artificial intelligence (AI) and machine learning (ML) to analyze trainee performance and error patterns. The goal was to identify key predictors of performance, generate personalized feedback, and provide actionable recommendations for optimizing VR-based medical training. Materials and methods A total of 196 medical professionals participated in a 1-hour VR training session, followed by a 20-question assessment and a post-training evaluation survey. The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. Descriptive statistics, error rates, and correlation analyses were performed using R software (version 4.1.2). Machine learning models were trained to predict performance outcomes, and feature importance was assessed using the Gini index. Personalized feedback reports were generated based on clustering and error analysis results. Results The study identified three distinct trainee clusters, with the highest-performing group excelling in Trauma Assessment and Clinical Case Analysis. However, high error rates were observed in Clinical Case Analysis (69.4%) and Trauma Assessment (67.3%), indicating areas for targeted improvement. Machine learning models highlighted replacing traditional teaching methods (IncNodePurity = 25.76) and stimulating learning interest (IncNodePurity = 13.08) as the most critical factors influencing learning outcomes. AI-driven feedback provided actionable recommendations, such as redesigning complex scenarios and enhancing system usability. Conclusions This study demonstrates the potential of integrating AI with VR training to create a more personalized and effective learning experience for medical professionals. The findings underscore the importance of adaptive, data-driven approaches in medical education, particularly in high-stakes environments such as emergency medicine. Future research should explore hybrid training models and incorporate physiological data to further enhance the efficacy of VR-based training systems.
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Lakshmi, Prasanna N., A. Abhinav, Dinesh B. Soma, Bharathi D. Srikari, and D. Iswarya. "Adaptive question answering system." i-manager's Journal on Information Technology 14, no. 1 (2025): 26. https://doi.org/10.26634/jit.14.1.21933.
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This paper presents an Adaptive Question Answering System designed to enhance information retrieval from text-heavy documents. The system integrates Natural Language Processing (NLP) techniques such as tokenization, stop word removal, and lemmatization to preprocess extracted text. By leveraging BM25 for document ranking and transformer-based models like BERT and T5 for answer generation, the system ensures accurate and contextually relevant responses. The backend is implemented using Python (Flask or FastAPI); while the frontend utilizes JavaScript, frameworks like React or Vue.js. This architecture facilitates an efficient and user-friendly interface for document uploads and querying, making complex information more accessible.
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S. Bayne, Clarence, and Raafat G. Saade. "The Use of Complex Adaptive Theory and Information Technologies to Inform Development Strategies in English Speaking Black Community, Montreal." International Journal of Community Development and Management Studies 2 (2018): 061–86. http://dx.doi.org/10.31355/23.
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NOTE: THIS ARTICLE WAS PUBLISHED WITH THE INFORMING SCIENCE INSTITUTE. Aim/Purpose................................................................................................................................................................................ The purpose of this paper is to conduct a multi-case/agent analysis using complexity theory to develop propositions that guide and inform our research for solutions to the problems of integration and full participation of the English-speaking Black community in the societies of Montreal and Quebec. Background................................................................................................................................................................................ This study was motivated by our interest in community organizational leader-ship, and concerns expressed by Black social entrepreneurs and organizations in the English-speaking Black communities of Montreal. The results of an unpublished survey conducted by the Institute for Community Entrepreneurship and Development (ICED) revealed a strong perception among Black leaders that in spite of their efforts to advance their communities there was too little progress. They attributed this to systemic exclusion and competitive strategies of mainstream non-Black agencies and leaders. Our further investigation of these claims suggested that beside discrimination based on color and race, factors more complex than skin color, being a person of African descent or White hate, were at work. Preliminary patterns in our observations suggest that the problems of exclusion and discrimination needed to be addressed in a broader psycho-social sense and in the context of Canada as a complex political, economic, and social adaptive system emerging continuously from generation to generation Methodology................................................................................................................................................................................ We used historical analysis and dynamic systems constructs to understand the causality structures of human social systems and to design strategies that have the highest possibilities for improving and optimizing the objective and subjective well-being of members of targeted minority sub-groups in the system. The general research approach is deductive and exploratory. It conforms mostly to critical realist thinking as opposed to traditional scientific methodologies. Contribution................................................................................................................................................................................ It is our opinion that communication network centers can be designed as part of a strategic planning process to increase the capacity of minority communities for creating, in a timely manner, the ingenuity required for solving problems of social, political and economic exclusion; for promoting sustainable development and improving objective and subjective well-being. The use of the MAS (multiple-agents system) analytical framework allows us to address and assess problems of decision making under varying degrees of uncertainty and in the social and historical context of the study. Findings..................................................................................................................................................................................... Our review of the development and progress of the Black community of Montreal shows that “under the radar” community based organizations and Black Social entrepreneurs have developed governance mechanisms and generated strategies and approaches to decision making that are consistent with the optimal patterns observed in simulations of multi-agent systems (MAS) . In particular, social entrepreneurs seem to support the formal creation of community based communication networks and information sharing as essential for community development. Several of these organizations consider these useful tools for facilitating the sharing of innovative ideas and best practices. Recommendations for Practitioners.......................................................................................................................................... The usefulness of the network community systems need to be monitored. Its usefulness will depend on how its outputs are perceived to have contributed to improving the level of fitness (the vitality and well-being/utility) of the community and its members. It will require a holistic approach to community development supported by network centers that provide communication and information services at levels that improve and sustain the capacity of the organizations and the community to adapt and evolve from generation to generation. The mechanisms in place must increase and sustain the capacity of the systems to achieve and maintain the desired level of outcomes consistent with attaining the highest fitness levels for the English speaking Black Communities. This must be tested with the help of information provided by a built in feedback subsystem of the network. Recommendation for Researchers.............................................................................................................................................. A central database has to be built into the system where social and economic data and measures of subsystem specific attributes and characteristics are gathered and stored for use by the network organizations and social entrepreneurs. There is no comprehensive measure of a fitness index for the Black community in Montreal. Theoretically speaking, there are too many possibilities to find a precise solution. However, an approximation of fitness can be obtained by constructing a human development index (HDI) in combination with measures of inequality such as comparative data on income, employment and unemployment, poverty, and etc. Impact on Society......................................................................................................................................................................... The paper raises some questions about the success of the experiment of multiculturalism in terms of greater recognition of the contributions of Canada’s diverse and multiple sub-cultures. It proposes ways to address complaints of failed expectations expressed by Black and immigrant minority groups. The paper offers policy makers and social entrepreneurs a dynamic analytical framework to explore the use of information and communication network theory, and information from simulations of multi-agent adaptive systems theory to develop more informed strategies and actions. Future Research............................................................................................................................................................................ More research needs to be done to improve the quality and expand the demographic and other data relating to the black communities in Montreal and Quebec. In addition, more research needs to be done on the development of an archival documentation system for the management and distribution of information between the different communities that make up the Black cultural community of Quebec and Canada.
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M., Narendra. "An Adaptive Query based Product Recommendation System." International Journal of Computer Applications 178, no. 46 (2019): 13–17. http://dx.doi.org/10.5120/ijca2019919350.
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Maged, Elazony, Khalifa Ahmed, Nouh Sayed, and Hussein Mohamed. "Design and Implementation of Adaptive Recommendation System." International Journal of Management, Technology, and Social Sciences (IJMTS) 3, no. 1 (2018): 101–17. https://doi.org/10.5281/zenodo.1254142.
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E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study. In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.
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Hsiao, I.-Han, and Peter Brusilovsky. "Guiding and Motivating Students through Open Social Student Modeling: Lessons Learned." Teachers College Record: The Voice of Scholarship in Education 119, no. 3 (2017): 1–42. http://dx.doi.org/10.1177/016146811711900302.
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Background/Context A large number of educational resources are now made available on the web to support both regular classroom learning and online learning. The abundance of available content has produced at least two problems: how to help students find the most appropriate resources and how to engage them in using and benefiting from these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work attempts to integrate these directions of research by combining the ideas of adaptive navigation support and open student modeling with the ideas of social comparison and social visualization. We call our approach Open Social Student Modeling (OSSM). Objective/Research Questions First, we review a sequence of our earlier projects focused on Open Social Student Modeling for one kind of learning content and formulate several key design principles that contribute to the success of OSSM. Second, we present our exploration of OSSM in a more challenging context of modeling student progress for two kinds of learning content in parallel. We aim to answer the following research questions: How do we design OSSM interfaces to support many kinds of learning content in parallel? Will current identified design principles (key features) confirm the power of the learning community through OSSM with multiple learning-resource collections? Will the OSSM visualization provide successful personalized guidance within a richer collection of educational resources? Research Design We designed four classroom studies to assess the value of different options for OSSM visualization of one and multiple kinds of learning content in the context of programming-language learning. We examined the comparative success of different design options to distill successful design patterns and other important lessons for the future developers of OSSM for personalized and social e-learning. Findings/Results The results confirmed the motivational impact of personalized social guidance provided by the OSSM system in the target context. The interface encouraged students to explore more topics and motivated them to work ahead of the course schedule. Both strong and weak students worked with the appropriate levels of questions for their readiness, which yielded consistent performance across different levels of complex problems. Additionally, providing more realistic content collection on the navigation-supported OSSM visualizations resulted in uniform performance for the group. Conclusions/Recommendation A sequence of studies of several OSSM interfaces confirmed that a combination of adaptive navigational support, open student modeling, and social visualization in the form of the OSSM interface can reinforce the navigational and motivational values of these approaches. In several contexts, the OSSM interface demonstrated its ability to offer effective guidance in helping students to locate the most relevant content at the right time while increasing student motivation to work with diverse learning content.
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Siddiquee, Mahfuzur Rahman, Naimul Haider, and Rashedur M. Rahman. "Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System." International Journal of Fuzzy System Applications 4, no. 4 (2015): 31–69. http://dx.doi.org/10.4018/ijfsa.2015100103.
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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.
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Torres, Nicolás. "A Multimodal User-Adaptive Recommender System." Electronics 12, no. 17 (2023): 3709. http://dx.doi.org/10.3390/electronics12173709.
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Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process warrants investigation. This paper introduces an original convolutional neural network (CNN) architecture that leverages multimodal information, connecting user ratings with product images to enhance item recommendations. A central innovation of the proposed model is the User-Adaptive Filtering Module, a dynamic component that utilizes user profiles to generate personalized filters. Through meticulous visual influence analysis, the effectiveness of these filters is demonstrated. Furthermore, experimental results underscore the competitive performance of the approach compared to traditional collaborative filtering methods, thereby offering a promising avenue for personalized recommendations. This approach capitalizes on user adaptation patterns, enhancing the understanding of user preferences and visual attributes.
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Lin, Hanhui, Shaoqun Xie, Zhiguo Xiao, Xinxin Deng, Hongwei Yue, and Ken Cai. "Adaptive Recommender System for an Intelligent Classroom Teaching Model." International Journal of Emerging Technologies in Learning (iJET) 14, no. 05 (2019): 51. http://dx.doi.org/10.3991/ijet.v14i05.10251.
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The development of information technology has facilitated the use of the intelligent classroom model supported by information technology to improve the college students’ comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation was proposed. First, the entire teaching process was divided into three stages which were used to combine teachers’ teaching and students’ learning. Then the key problems of the learning resources recommendation system was studied and a learning resource recommendation based on TR-LDA (Teaching Resources-Latent Dirichlet Allocation) was proposed and how to be achieved. Finally, the proposed intelligent classroom model was verified in practical teaching. Results show that the intelligent classroom model with adaptive learning resources recommendation can help to improve students’ learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.
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Etaati, Leila, and David Sundaram. "Adaptive tourist recommendation system: conceptual frameworks and implementations." Vietnam Journal of Computer Science 2, no. 2 (2014): 95–107. http://dx.doi.org/10.1007/s40595-014-0034-5.
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Yan, Xiaofang. "Effects of Personalized Recommendation System Based on User Learning Behavior in English-Chinese Translation." Journal of Combinatorial Mathematics and Combinatorial Computing 119, no. 1 (2024): 185–94. http://dx.doi.org/10.61091/jcmcc119-19.
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Users may receive personalised information services and decision support from personalised recommendations. In this paper, a hybrid algorithm-based personalised recommendation approach for learning English is proposed. The user model is created by merging user interest tags, and the Person Rank algorithm is then recommended based on user information. Second, the question-and-answer model is created once the question-and-answer data has been labelled, and the Problem Rank algorithm is suggested in accordance with the question-and-answer data. Then, the approach of tag-based recommendation, comparable user recommendation, and multi-dimensional sliding window are used to construct the recommendation algorithm model. The experimental findings demonstrate that, following the model’s training with the gradient descent technique, the recommendation accuracy is steady at around 0.78, the suggested information can accommodate users who are learning English, and the personalised recommendation effect is enhanced.
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Sagedur Rahman. "Extended Collaborative Filtering Recommendation System with Adaptive KNN and SVD." International Journal of Engineering and Management Research 13, no. 4 (2023): 105–12. http://dx.doi.org/10.31033/ijemr.13.4.14.
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In recent years, recommendation systems have gained significant importance due to the vast amount of digital content available on various online platforms. Collaborative filtering is a widely adopted approach in recommendation systems, leveraging user-item interactions to make personalized predictions. However, traditional collaborative filtering methods face challenges such as the cold-start problem and data sparsity. To address these issues, researchers have proposed advanced techniques, including Adaptive KNN-Based and SVD-Based Extended Collaborative Filtering. This paper provides a comprehensive review of these two recommendation systems, discussing their underlying principles, advantages, and limitations. Furthermore, we explore recent research advancements and real-world applications, providing insights into the potential future developments in this field.
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Rani, Geeta, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, and Pradeep Kumar Tiwari. "Intelligent and Adaptive Web Page Recommender System." International Journal of Web Services Research 18, no. 4 (2021): 27–50. http://dx.doi.org/10.4018/ijwsr.2021100102.
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In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.
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Indar, Alyssa, Michelle Nelson, Whitney Berta, and Maria Mylopoulos. "Learning from Canadian Stroke Rehabilitation Care Clinicians: Implications for the Management of Patient Complexity." International Journal of Integrated Care 25 (April 9, 2025): 627. https://doi.org/10.5334/ijic.9473.
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Introduction: Clinicians working in stroke rehabilitation settings care for a high proportion of patients with complex care needs. The average patient recovering from a stroke has a minimum of two co-morbidities and a range of psychological and socio-economic needs (Gallacher et al., 2019; Schaink et al., 2014). The care trajectories of these “complex” patients usually deviate from standard practices and they require customized care. There is limited evidence to guide clinician decision-making in relation to complex patients, as their needs are not fully reflected in clinical practice guidelines (Boyd et al., 2005). In lieu of evidence, clinicians may engage in collaborative problem solving to generate innovative solutions (Nelson et al., 2016). However, implementing customized approaches may be difficult in environments that prioritize adherence to specific clinical pathways. For example, Stroke Distinction sites across Canada are recognized for delivering care in accordance with the Canadian Stroke Best Practice Recommendations (CSBPR) (Accreditation Canada, 2021). We sought to explore how expert stroke rehabilitation clinicians provide customized care to a large subset of complex patients, while meeting organizational performance requirements at Stroke Distinction sites. Methods: We used an interpretive descriptive research design (Thorne, 2016) to explore the research question: How do expert clinicians at Stroke Distinction sites recognize and manage the care of patients with complex care needs? We interviewed 16 clinicians (including medicine, allied health, nursing), four organizational leaders and two health system experts. We collected data via 45–60-minute virtual interviews and engaged in a hybrid inductive- deductive approach to analysis. Results: We reported three themes: (1) recognizing complexity is routine work for clinicians, (2) clinicians use workarounds to manage complexity, and (3) clinicians perceived and worked to bridge a difference between organizational processes and the realities of patient care. We noted differences regarding perceptions of patient complexity across participant types. For example, clinicians reported most of their patients to have complex care needs. They described care for patients with a high degree social complexity (e.g., limited family or financial support) as particularly difficult to manage. When unable to secure outcomes that patients “deserve”, clinicians reported experiencing moral distress. In contrast, the organizational and system experts described that stroke programs are designed for approximately 20% of patients to have complex care needs; this represents a large mismatch in perceptions of patient complexity in comparison with the clinician group. Conclusions and Implications: Expert clinicians use adaptive strategies to continually manage care for a high proportion of patients with complex care needs. However, they often report moral distress when these strategies are unable to compensate for health system limitations. Given the significant mismatch in perceptions of patient complexity between clinicians and leaders who shape systems of care, decision-makers could consider macro- and meso-level strategies to support the adaptive practices of clinicians in alignment with workforce strategies to prioritize the clinician retention. Next Steps: This research was a part of a doctoral dissertation. We continue to share this work and seek collaboration with others in supporting clinician management of complexity.
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González-Castro, Nuria, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos, and Carlos Delgado Kloos. "Adaptive learning module for a conversational agent to support MOOC learners." Australasian Journal of Educational Technology 37, no. 2 (2021): 24–44. http://dx.doi.org/10.14742/ajet.6646.
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Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions provided to learners considering their level of knowledge using item response theory (IRT) and also provides recommendations of video fragments extracted from the MOOC for when learners fail questions. The adaptive learning module for JavaPAL has been evaluated showing good usability and learnability through the system usability scale (SUS), reasonably suitable video fragments recommendations for learners, and useful visualisations generated as part of the IRT-based adaptation of questions for instructors to better understand what is happening in the course, to design exams, and to redesign the course content.
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Liu, Yan. "Survey of Intelligent Recommendation of Academic Information in University Libraries Based on Situational Perception Method." Journal of Education and Learning 9, no. 2 (2020): 197. http://dx.doi.org/10.5539/jel.v9n2p197.
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Based on the context-aware environment, in this paper, the adaptive interest models are reviewed. And the academic information intelligent recommendation systems of university libraries are presented based on the situational awareness-related theory method, where the current situation and trend of situational awareness-related theory and service of intelligent recommendation systems are investigated. Meanwhile, the potential research directions, basic ideas and methods are also presented. The adaptive model and architecture of the academic information recommendation system are built based on situational awareness, and the collaborative filtering information recommendation algorithm is studied based on spatial-temporal similarity relationship to obtain the interest of scientific and technological scholars. Combining with the adaptive interest model, an academic information demand model is established. On this basis, the prototype system of academic information recommendation is further studied to realize personalized recommendation of academic information based on the situational perception. This paper will provide effective solutions to the digital resources service of university libraries and the academic information recommendation needs of scientific and technological scholars.
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Osman, Tousif, Maisha Mahjabeen, Shahreen Shahjahan Psyche, Afsana Imam Urmi, J. M. Shafi Ferdous, and Rashedur M. Rahman. "Application of Fuzzy Logic for Adaptive Food Recommendation." International Journal of Fuzzy System Applications 6, no. 2 (2017): 110–33. http://dx.doi.org/10.4018/ijfsa.2017040106.
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The research introduces an adaptive food searching and recommending engine by taste and user preference using fuzzy logic. In contrast with existing system where food is searched by predefined keywords, this system searches food by its taste and users' preference which allows the system to provide better results. As food taste cannot be measured and user's preference is relative to each user, the authors have used concepts of artificial intelligence (AI) and fuzzy logic to better understand and deal the abstractness of these parameters. Along with food taste the authors have considered restaurant's environment, location, review and user's budget as searching parameters. The system includes a fuzzy database where food items of different restaurants with the specific parameters have been stored and gets updated by user feedback. System also maintains a user profile for individual user to adapt with individual user's choice of preference.
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SenthilKumaran, V., and A. Sankar. "Recommendation System for Adaptive E-learning using Semantic Net." International Journal of Computer Applications 63, no. 7 (2013): 19–24. http://dx.doi.org/10.5120/10478-5210.
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Jayaprakash, S., S. Vishnupriya, and R. Kumar. "Adaptive Predictive and Recommendation System Based on Learners Style." International Journal of Innovative Research in Applied Sciences and Engineering 4, no. 5 (2020): 743–47. http://dx.doi.org/10.29027/ijirase.v4.i5.2020.743-747.
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Da’u, Aminu, Naomie Salim, and Rabiu Idris. "An adaptive deep learning method for item recommendation system." Knowledge-Based Systems 213 (February 2021): 106681. http://dx.doi.org/10.1016/j.knosys.2020.106681.
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Walek, Bogdan. "Creating adaptive web recommendation system based on user behavior." Journal of Physics: Conference Series 933 (January 3, 2018): 012014. http://dx.doi.org/10.1088/1742-6596/933/1/012014.
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Zhang, Chao. "Discussion on Digital Reference System Development Technology." Advanced Materials Research 271-273 (July 2011): 883–86. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.883.
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This paper analyzes the Reference System software development environment, the system architecture of the browser / server mode is analyzed in detail, describes the object-oriented analysis method, and presented different development framework according to different needs. This paper is from the resource classification, information retrieval, question recommendation and other technical to give a certain amount of evaluation and analysis .This paper mainly analysis a category navigation system, it help users find ways by providing users with search suggestions help them find answers from local resources or the Internet;introduced Chinese segmentation, quizzes indexing and search technology based on lucene; and discussed methods and strategies of the question recommendation from the consultants, the questioner and the question.
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Banerjee, Ayan, and Anirban Kundu. "Software for Feedback System Using Adaptive Categorization and Authenticated Recommendation." International Journal of Open Source Software and Processes 10, no. 2 (2019): 37–69. http://dx.doi.org/10.4018/ijossp.2019040103.
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The authors propose a web-based adaptive categorization and authenticated recommendation system, based on teacher performance. Distinct layers of the proposed framework have been operated from many geographically distributed locations. The system contains multiple entry points such as a student attendance module, a teacher categorization module, and a teacher recommendation module, strictly accessed by the administrative authority of an academic organization. The student attendance module is required for achieving better results on the teacher categorization module, and the teacher recommendation module. The reliability factor has been incorporated for realizing the accuracy of the proposed system. The administration authorities communicate with the server to categorize and recommend teachers by using teachers' performance. The replication and re-allocation transparencies have been maintained throughout the servers. Lightweight system performance has been enhanced due to the incorporation of a paperless approach and has provided less data loss. A linear time complexity is achieved due to usage of cellular automata as a tool.
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Cheng, Jinyu, and Hong Wang. "Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading." Complexity 2021 (March 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/5592534.
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This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills.
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Prof.Dr.TorkeyI.Sultan, Ayman E. Khedr Dr., and Kamal Alsheref Fahad. "ADAPTIVE MODEL FOR WEB SERVICE RECOMMENDATION." International Journal on Web Service Computing (IJWSC) 4, no. 4 (2013): 21–33. https://doi.org/10.5281/zenodo.3626430.
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The Competition between different Web Service Providers to enhance their services and to increase the users' usage of their provided services raises the idea of our research. Our research is focusing on increasing the number of services that User or Developer will use. We proposed a web service’s recommendation model by applying the data mining techniques like Apriori algorithm to suggest another web service beside the one he got from the discovery process based on the user’s History. For implementing our model, we used a curated source for web services and users, which also contains a complete information about users and their web services usage. We found a BioCatalogue: our proposed model was tested on a Curated Web Service Registry (BioCatalogue).and 70 % of users chose services from services that recommended by our model besides the discovered ones by BioCatalogue.
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Rey Muhamad Rifqi, Djupriadi, Widodo Tri Haryanto, Ema Utami, and Alva Hendi Muhamad. "Hybrid Recommendation System in E-Commerce." International Journal of Integrated Science and Technology 3, no. 5 (2025): 1983–92. https://doi.org/10.59890/ijist.v3i5.29.
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This study explores hybrid recommendation systems in e-commerce, which combine content-based and collaborative filtering to overcome limitations such as cold-start and data sparsity. Through a Systematic Literature Review of 15 selected papers, it identifies key hybrid types—Weighted, Switching, and Cascade Hybridization—and analyzes trends in their adoption. Weighted Hybridization is found to be the most frequently used due to its effectiveness in improving recommendation accuracy. The study also discusses the strengths of hybrid systems in providing personalized and adaptive suggestions, along with challenges like system complexity and computational cost. Overall, hybrid approaches offer promising improvements for user experience in e-commerce recommendation systems.
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Song, Meng. "IoT Speech Recognition Application in Mass Sports Data Monitoring Based on Dynamic Adaptive Recommendation Algorithm." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/8032571.
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In this paper, the existing dynamic adaptive recommendation methods are studied, which combine the practical application scheme of transforming the actual dynamic adaptive recommendation problem into user microblog information. After that, a dynamic adaptive weight fusion method is proposed and based on experimental verification, a real-time dynamic adaptive recommendation system is finally designed. The speech recognition of the Internet of Things takes natural language problems as the research object for a long time and takes the sound signal as the research topic. This paper analyzes the application of dynamic adaptive recommendation and Internet of Things speech recognition in mass sports data monitoring. The simulation results show that the system in this paper is convenient for users to monitor the exercise indicators in real time through the mobile client, and at the same time query the exercise historical data and compare the exercise data through the network terminal, thereby improving the exercise method and exercise load. Users can access the motion monitoring module and see the past floating state of motion parameters more intuitively than graphs, contains queries for metrics such as heart rate, body temperature, kinetic energy, pulse, and weight. Due to the diversity and complexity of people’s differences, personal characteristics and business environments, sports data monitoring systems also need to be designed according to the scope of use. This paper analyzes the requirements for a motion data monitoring system and provides the system architecture design and basic data for producing detailed information for the system.
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Szkaliczki, Tibor, Balázs Goldschmidt, and Laszlo Böszörmenyi. "Algorithmic Background of the Host Recommendation in the Adaptive Distributed Multimedia Server." Serdica Journal of Computing 1, no. 3 (2007): 365–86. http://dx.doi.org/10.55630/sjc.2007.1.365-386.
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In a distributed server architecture an obvious question is where to deploy the components. Host recommendation, which gives the answer, faces problems such as server selection, host deployment and, in case of multimedia servers, video replication. It is especially relevant for the Adaptive Distributed Multimedia Server (ADMS) which is dynamically able to add and remove its components to different nodes of the network. The present survey paper introduces the different variants of host recommendation and gives an overview of its possible mathematical approaches. Emphasis is put on the facility location problem and the related approximation algorithms. Finally some algorithms selected for implementation are presented.
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Malinowski, Michał. "Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System." Management and Business Research Quarterly 19 (November 2021): 14–32. http://dx.doi.org/10.32038/mbrq.2021.19.02.
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The aim of the paper is to present a study as the implementation of the author’s Algorithm of the Recommendation Sessions ARS in an operating e‐commerce information system and to analyse basic parameters of the recommendation system created as a result of the implementation. The first part of the study contains a synthetic description of the area of recommendation systems. The next section presents the proprietary ARS recommendation algorithm based on recommendation sessions. The third part of the paper describes the mathematical model of the recommendation session built on the basis of the theory of graphs and networks, which such model makes the input data for the algorithm in question. The next part of the publication describes the possibilities of representing graph structures and the method of implementing a G graph (constituting a set of the recommendation session) in a relational database. The implementation of the ARS algorithm, based on the SQL standard, was also presented. The implementations in question have been developed on the basis of a working information system of the e‐commerce class. As a result of the implementation of the algorithm, a fully functional recommendation system was created, which can be adapted to various e‐commerce IT systems. The positive result of the work was confirmed by the research on the parameters of the recommendation system, included in the last part of the study.
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Malinowski, Michał. "Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System." European Journal of Studies in Management and Business 19 (September 11, 2021): 14–32. https://doi.org/10.32038/mbrq.2021.19.02.
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The aim of the paper is to present a study as the implementation of the author’s Algorithm of the Recommendation Sessions ARS in an operating e‐commerce information system and to analyse basic parameters of the recommendation system created as a result of the implementation. The first part of the study contains a synthetic description of the area of recommendation systems. The next section presents the proprietary ARS recommendation algorithm based on recommendation sessions. The third part of the paper describes the mathematical model of the recommendation session built on the basis of the theory of graphs and networks, which such model makes the input data for the algorithm in question. The next part of the publication describes the possibilities of representing graph structures and the method of implementing a G graph (constituting a set of the recommendation session) in a relational database. The implementation of the ARS algorithm, based on the SQL standard, was also presented. The implementations in question have been developed on the basis of a working information system of the e‐commerce class. As a result of the implementation of the algorithm, a fully functional recommendation system was created, which can be adapted to various e‐commerce IT systems. The positive result of the work was confirmed by the research on the parameters of the recommendation system, included in the last part of the study.
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Filho, Aluizio Haendchen, Silva Esteves Adson Marques da, Prado Hércules Antonio do, Edilson Ferneda, and André Luis Alice Raabe. "Using Adaptive Content Recommendations to Improve Logic and Programming Teaching and Learning." JUCS - Journal of Universal Computer Science 30, no. (12) (2024): 1645–61. https://doi.org/10.3897/jucs.115016.
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The high dropout rate in Information Technologies courses is a relevant problem in many countries, mainly because of the increasing demand for professionals in this sector. Usually, high dropout rates in these courses are related to difficulties in algorithms and programming subjects. Content recommendation systems are proposed to mitigate this problem, employing adaptive learning environments that facilitate the learning process. This study presents a content recommendation system that uses learning paths to group students and provide personalized recommendations based on peers' progress. The work follows the many efforts of group-based recommendation systems reported in the literature. The system uses intelligent agents and clustering algorithms to implement the recommendation system and was evaluated by submitting the simulation results to the judgment of human experts who significantly agreed with them. This initiative could make programming teaching more adaptive, using the groups' knowledge. Facilitating learning is one of the key issues to reduce dropout rates and resolve the shortage of labor in the technological area in Portuguese-speaking countries.
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Zhong, Shan, Wenhao Ying, Xuemei Chen, and Qiming Fu. "An Adaptive Similarity-Measuring-Based CMAB Model for Recommendation System." IEEE Access 8 (2020): 42550–61. http://dx.doi.org/10.1109/access.2020.2977463.
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Almohammadi, Khalid, Hani Hagras, Bo Yao, Abdulkareem Alzahrani, Daniyal Alghazzawi, and Ghadah Aldabbagh. "A type-2 fuzzy logic recommendation system for adaptive teaching." Soft Computing 21, no. 4 (2015): 965–79. http://dx.doi.org/10.1007/s00500-015-1826-y.
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