Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing le...Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.展开更多
BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant...BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant challenges to glycemic management,particularly during chemotherapy.AIM To assess the effects of individualized exercise training(IET)on glycemic control and nutritional status in patients with T2DM undergoing chemotherapy for CRC.METHODS In this retrospective study,clinical data from 245 patients with T2DM and on chemotherapy for CRC between November 2023 and December 2024 were analyzed.Patients were stratified into two groups according to their treatment regimens:The standard care(SC)group(n=111),which received conventional chemotherapy and diabetes management,and an IET group(n=134),which received additional personalized exercise interventions alongside SC.Parameters assessed included fasting plasma glucose,glycosylated hemoglobin,glycemic variability indices,nutritional biomarkers,markers of intestinal permeability,and adverse events.RESULTS Patients in the IET group demonstrated significant improvements in glycemic control,nutritional biomarkers,and glycemic variability(all P<0.05),compared with the SC group.The markers of intestinal permeability also improved significantly in the IET group(P<0.05).Meanwhile,no statistically significant difference in the incidence of adverse events was found between the two groups(P>0.05).These findings suggest that individualized exercise interventions can enhance metabolic,nutritional,and gastrointestinal outcomes without increasing treatment-related risks.CONCLUSION Personalized exercise training may offer clinically meaningful benefits in glycemic regulation and nutritional status for patients with T2DM and on chemotherapy for CRC.展开更多
文摘Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.
文摘BACKGROUND Colorectal cancer(CRC)is the third most diagnosed malignancy worldwide and a frequent comorbidity among these patients is type 2 diabetes mellitus(T2DM).The coexistence of these conditions poses significant challenges to glycemic management,particularly during chemotherapy.AIM To assess the effects of individualized exercise training(IET)on glycemic control and nutritional status in patients with T2DM undergoing chemotherapy for CRC.METHODS In this retrospective study,clinical data from 245 patients with T2DM and on chemotherapy for CRC between November 2023 and December 2024 were analyzed.Patients were stratified into two groups according to their treatment regimens:The standard care(SC)group(n=111),which received conventional chemotherapy and diabetes management,and an IET group(n=134),which received additional personalized exercise interventions alongside SC.Parameters assessed included fasting plasma glucose,glycosylated hemoglobin,glycemic variability indices,nutritional biomarkers,markers of intestinal permeability,and adverse events.RESULTS Patients in the IET group demonstrated significant improvements in glycemic control,nutritional biomarkers,and glycemic variability(all P<0.05),compared with the SC group.The markers of intestinal permeability also improved significantly in the IET group(P<0.05).Meanwhile,no statistically significant difference in the incidence of adverse events was found between the two groups(P>0.05).These findings suggest that individualized exercise interventions can enhance metabolic,nutritional,and gastrointestinal outcomes without increasing treatment-related risks.CONCLUSION Personalized exercise training may offer clinically meaningful benefits in glycemic regulation and nutritional status for patients with T2DM and on chemotherapy for CRC.
