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The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice.

Despite the continuous emphasis on emotion in multimedia learning, it was still unclear how pedagogical agent emotional cues might affect learning. In the present study, a between-subjects experiment was performed to examine the effects of a pedagogical agent with dual-channel emotional cues on learners' emotions, cognitive load, and knowledge transfer performance. Participants from a central Chinese university (age mean = 21.26, N = 66) were randomly divided into three groups. These groups received instructions from an affective pedagogical agent, a neutral pedagogical agent, or a neutral voice narration without pedagogical agent embodiment. Results showed that learners assigned the affective pedagogical agent reported a significantly higher emotional level than learners assigned the neutral pedagogical agent. Learners’ perceived task difficulty was not significantly different among groups while instructional efficiency was significantly higher for learners with the affective pedagogical agent. Moreover, learners assigned to the affective pedagogical agent performed significantly better on the knowledge transfer test than those assigned the neutral pedagogical agent or the neutral voice.

Tripterygium wilfordii multiglucoside (TWM) is a fat-soluble extract from a Chinese herb T. wilfordii, that’s used in treating rheumatoid arthritis, nephrotic syndrome and other skin diseases. Triptolide (TP) is a major active component in TWM. However, clinical applications of TWM are limited by its various toxicities especially hepatotoxicity. In recent studies, it has been reported that drug-induced liver injury (DILI) could induce the disorder of lipid metabolism in animals. Hence, this study focuses on the metabolic profile of fatty acids in TWM- and TP-induced liver-injured rats. In serum and liver tissue, 16 free and 16 esterified fatty acids were measured by gas chromatography coupled with mass spectrometry. Metabolic profile of serum fatty acids in rats with liver injury was identified by multivariate statistical analysis. The fatty acid levels in the serum of TWM- and TP-treated rats significantly decreased, whereas those in the liver tissue of TWM- and TP-treated rats obviously increased when compared with the vehicle-treated rats. Four free fatty acids were identified as candidate biomarkers of TWM- and TP-induced liver injury. Therefore, the targeted metabolomic method may be used as a complementary approach for DILI diagnosis in clinic.

The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein–protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets., GRAPHICAL ABSTRACT

Soluplus® nanomicelles have been widely reported in biomedical field for their excellent drug loading capacity and solubility enhancement ability. However, when administrated in vivo, the protein corona will be formed on Soluplus® nanomicelles, significantly affecting their drug delivery performance. Up to now, few studies examined the protein corona formation process and its impact factors of Soluplus® nanomicelles. The multiple proteins in biofluids may form protein corona in different modes due to their diversified properties. In this study, Bovine serum albumin (BSA), Lysozyme (Lyso) and Bovine hemoglobin (BHb) were chosen as model proteins to investigate the protein corona formation process of Soluplus® nanomicelles. By analyzing the polarity of the protein amino acid residues distributing microenvironments, the results showed that there were two different protein corona formation modes, i.e., surface adsorption and insertion, which were determined by the hydrophilicity of proteins. The hydrophobic BHb followed the insertion mode while hydrophilic BSA and Lyso followed the surface adsorption mode. Ultimately, upon protein corona formation, the size and surface chemistry of nanomicelles was significantly affected. We believe this study will provide a new research paradigm to the design and application of Soluplus® nanomicelles.

For lack of effective and reliable assessment tools of nursing informatics competencies (NIC), we used literature research method, nominal group technology (NGT) and delphi technique to construct an evaluation index system of NIC for clinical nurses, which can provide reference for establishing a training system of talents of nursing informatics.

As the means to display abstract concepts and provide interactive experience, augmented reality (AR) has shown unparalleled superiority in educational domain. A sample of 213 Chinese K-12 teachers participated in our study. The results show that: 1. The external environment, self-efficacy, perceived usefulness, and perceived ease of use directly affect the teachers’ intention to use AR; 2. System characteristics indirectly affect the teachers’ intention to use AR through intermediary variables. 3. Perceived ease of use indirectly affects the teachers’ intention to use AR by affecting perceived usefulness and self-efficacy. The research has certain theoretical significance for AR application in teaching.

With the rapid development of artificial intelligence (Al) technology, machine understanding math word problems (MWPs) has received growing attention. However, existing methods of automatic understanding MWPs are hardly integrated into cognitive intelligent systems used for individual learning. To address the integration problem, this paper firstly clarified the relationship between understanding MWPs and discourse comprehension. According to the trait of discourse comprehension models, the existing methods were divided into knowledge schema-based methods and mental processing-based methods. Then we shortly presented the construction-integration model and proposed a conceptual framework for machine understanding MWPs. The proposed conceptual framework was established from long and short-term memory, cognitive computing services, formal representation models, and human-computer interaction. Finally, we draw a conclusion that integrating cognitive models of human understanding discourse into the process of machine understanding MWPs is conducive to developing a humanized cognitive intelligence system for personalized learning.

Nano-systems (size range: 1 ~ 1000 nm) have been widely investigated as pulmonary drug delivery carriers, and the safety of inhaled nano-systems has aroused general interests. In this work, bibliometric analysis was performed to describe the current situation of related literature, figure out the revolutionary trends, and eventually forecast the possible future directions. The relevant articles and reviews from 2001 to 2020 were retrieved from the Web of Science Core Collection. The documents were processed by Clarivate Analytic associated with Web of Science database, Statistical Analysis Toolkit for Informetric, bibliometric online platform and VOSviewer, and the data were visualized. The bibliometric overview of the literature was described, citation analysis was performed, and research hotspots were showcased. The bibliometric analysis of 3362 documents of interest indicated that most of the relevant source titles were in the fields of toxicology, pharmacy, and materials science. The three research hotspots were the biological process of inhalable nano-systems in vivo, the manufacture of inhalable nano-systems, and the impact of nano-systems on human health in the environment. Toxicity and safety have always been the keywords. The USA was the major contributing country, and international collaboration and co-authorship were common phenomena. The general situation and development trend of literature of inhalable nano-systems were summarized. It was anticipated that bibliometrics analysis could provide new ideas for the future research of inhalable nano-systems. Supplementary Information The online version contains supplementary material available at 10.1007/s11051-021-05384-1.

The popularity of mobile devices and conversational agents in recent years has seen wide use of chatbots in different educational scenarios. In relation to the advances in mobile devices and conversational agents, there are few research works concerning the design and evaluation of domain-specific chatbots to fulfill the demand of mobile learning. To address this issue, we propose an agent-based conceptual architecture to develop a domain-specific chatbot for mobile learning. We extend the open-domain DeepQA agent to make it sensitive to restricted domain questions by building a domain-specific gate, and employ WeChat as user interface. To evaluate our chatbot, subjective and objective criteria are employed to assess its effectiveness. Additionally, its usability evaluation proceeds with system usability scale questionnaire and net promoter score simultaneously. In total, 18 domain experts participated in the evaluation of effectiveness, and 52 participants were involved in the evaluation of usability. Based on the evaluation results, we conclude that our chatbot can serve as an effective information retrieval tool in a specific domain. The perceived usability of our chatbot tends to be moderate and marginal and has positively affected the promotion of our chatbot for mobile learning. This paper contributes to the educative application of chatbots in specific subject fields.

The liver is an important organ for amino acid metabolism, and its damage can be reflected in the changes of amino acid level in the body. Triptolide (TP) has broad anti-inflammatory and anti-tumor activities, but its clinical application is limited due to hepatotoxicity. In this work, a simple, accurate and sensitive gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS/MS) method was developed and validated for evaluating the serum levels of amino acids from control and TP-induced liver injured rats, and chemometric analysis was employed for amino acid metabolic profiles analysis. It was found that 11 amino acids showed significant changes after TP administration, and they were mainly involved in 5 metabolic pathways that are phenylalanine, tyrosine and tryptophan biosynthesis, alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism, phenylalanine metabolism and arginine biosynthesis. Five amino acids including tyrosine, glutamine, glutamic acid, tryptophan and alanine were identified as biomarkers of TP hepatotoxicity by further analysis. These results indicated that the novel amino acid metabolic profiling study based on the GC-QqQ-MS/MS provided not only exact concentrations of serum amino acids, but also a prospective methodology for evaluation of chemically induced liver injury.

Mental models have been deeply concerned by researchers and practitioners since it plays a crucial role in problem-solving. In this study, we proposed mental models for random sampling word problems. On basis of the proposed mental models, we further performed a mental simulation with a rule-based mental reasoning method and a template-based approach to generate human-style explanations. And then we established an experiment on a prototype intelligent tutoring system. Experimental results showed that: the proposed mental models were available for most random sampling word problems; and a solution with human-style explanations to the problem encoded in mental models was produced effectively by means of mental reasoning. Based on these results, we concluded that our mental models can be used to encode essential information for solving random sampling word problems effectively, and it is profitable to take mental models into account when developing intelligent tutoring systems.

Tripterygium wilfordii multiglucoside (TWM) is a fat-soluble extract from a Chinese herb T. wilfordii, that’s used in treating rheumatoid arthritis, nephrotic syndrome and other skin diseases. Triptolide (TP) is a major active component in TWM. However, clinical applications of TWM are limited by its various toxicities especially hepatotoxicity. In recent studies, it has been reported that drug-induced liver injury (DILI) could induce the disorder of lipid metabolism in animals. Hence, this study focuses on the metabolic profile of fatty acids in TWM- and TP-induced liver-injured rats. In serum and liver tissue, 16 free and 16 esterified fatty acids were measured by gas chromatography coupled with mass spectrometry. Metabolic profile of serum fatty acids in rats with liver injury was identified by multivariate statistical analysis. The fatty acid levels in the serum of TWM- and TP-treated rats significantly decreased, whereas those in the liver tissue of TWM- and TP-treated rats obviously increased when compared with the vehicle-treated rats. Four free fatty acids were identified as candidate biomarkers of TWM- and TP-induced liver injury. Therefore, the targeted metabolomic method may be used as a complementary approach for DILI diagnosis in clinic.

© 2018, Springer Nature B.V. A big challenge in educational resources construction is the intelligent and personalized resource recommendation for learners. This paper proposes a semantic recommendation framework of educational resources based on semantic web and pedagogics. In this framework, a domain ontology is constructed to describe the knowledge structure of the domain. All the resources and user portfolio are described with ontology technology and resource description framework to support semantic inference. Based on the semantic resource organization, we made a set of reasoning rules based on pedagogics. These rules are made from the synthesis of the type of the knowledge, the internal structure of knowledge and learner’s learning performance. A case study was implemented on the course “theory and practice of database”. In this case, learners are recommended different learning materials according to the different knowledge structure and different learning performance. Three typical learning modes are proposed to describe the personalized learning experience. This framework can be used as a guide for teachers and resource designers.

Background/Aims: Alpinia zerumbet (Pers.) Burtt. et Smith has been used as a flavor additive in food and a traditional medicine for centuries, especially in Guizhou Province, China, and it prolongs people’s lives with multiple beneficial effects. Thus, one of the aims of this review was to expound the chemical constituents of this plant, especially its fruits. Since cardiovascular diseases, including atherosclerosis, pose a health threat to humans, another aim was to expound the possible mechanisms of its potential use as an herbal medication for atherosclerosis. Methods: In this study, 10 reports are cited to expound the potential bioactive compounds. Moreover, 33 reports explain the antihypertensive and antiatherosclerotic effects of the plant by ameliorating inflammation and endothelial dysfunction, increasing vasodilation, improving hyperlipidemia, downgrading the glucose status, and working as an antioxidant. Results: A. zerumbetis rich in terpenes, essential oils, flavonoids, polyphenolics, and sterols. Pharmacological experiments showed that A. zerumbet has antioxidative and anti-inflammatory effects on the NF-κB signaling pathway and can ameliorate oxidative stress in the NOS-NO signaling pathway. Moreover, A. zerumbet demonstrates antihypertensive effects by accelerating vasorelaxant response and increasing 3T3-L1 intracellular cAMP, which has promising antiobesity properties, as well as hypolipidemic and anti-diabetic complication effects. Conclusions: A. zerumbet has potential functions and applications in the prevention of atherosclerosis, but further studies are required before clinical trials.

Magnetic field is an important knowledge point in junior high school physics discipline. Due to its invisible feature, students require to concretely perceive the magnetic field through experiments. However, the traditional experiment has some defects such as inconvenient operation and insignificant effect. Computer simulation technologies can solve these problems by virtualizing them, but it currently has some shortcoming in terms of its experience and credibility. Based on augmented reality technology and the idea of inquiry learning, this paper designs and implements an AR courseware for magnetic field teaching. The experimental experience is enhanced by the fusion of virtual and real while visualizing the magnetic line. Finally, a preliminary application is carried out.

A method is reported for recognizing MCF-7 human breast carcinoma cells based on silica-encapsulated nanoparticles modified with aminophenylboronic acid which can recognize sialic acid on cell surfaces. Gold@rhodamine B nanoparticles were coated with aminophenylboronic acid and used to capture MCF-7 cells. It is found that the presence of gold NPs was favorable to prepare nanoparticles easily and that they were extraordinarily biocompatible with MCF-7 cells. The experimental results confirmed that the nanoparticles can be used to target breast carcinoma cells using HS 578Bst normal breast cells as the negative control. The MCF-7 cells were imaged by laser scanning microscopy and showed strong red fluorescence in dark field. An MTT test revealed an 82 % viability of cells when 50 mg · mL−1 fluorescent probe was used in the incubation experiments. The results exhibited that the NPs are innocuous and stable. In our perception, the method has a larege potential for early diagnosis of breast cancer due to high affinity between nanoparticles and the breast carcinoma cells.

Metacognition is important in self-regulated learning and understanding its epistemic network can improve teaching and learning. We collected self-reported metacognition reflections on collaborative learning activities from 87 college students to analyze how students' metacognitive patterns differ by performance level and discipline type. We used an epistemic network analysis to identify these differences, and the results indicated that description of goals appeared most in self-reported reflections. There are variations in metacognitive patterns between different groups. High-score students had stronger connections around actions, while low-score students had stronger connections between metacognitive knowledge and context. The natural science students focused more on metacognitive knowledge and actions, while the humanities science students focused more on metacognitive experience and context. This implies that teachers should provide clear explanations about the collaborative learning goal, and a group strategy that takes both performance and discipline types into consideration could address the variation in metacognitive patterns.

In recent years, with the rapid progress of game development technology as well as human-computer interaction technology, game-based learning (GBL) has not only drew the attention of increasing number of researchers but this method has also been implemented into classrooms by teachers around the world. And the improvement for knowledge acquisition is being reported as the most frequently occurring outcome of GBL across a wide range of subjects like mathematics, physics, health etc. However, there are very limited cases where GBL being employed into music class especially for Chinese traditional musical instruments which are facing the danger of being lost since children today are far less likely to have a chance to play or listen to those beautiful instruments because schools usually focus more on the education of mainstream instruments like pianos and violins. Therefore, the present paper designs and develops an interactive virtual dulcimer auxiliary teaching system based on musical instrument digital interface, which in the future can be utilized as a tool in class to assist teaching or can be promoted as a self-teaching instrument.

In recent years, immersive interactive technologies are becoming more and more mature. A large number of schools actively carry out virtual experimental teaching and establish virtual laboratories. As a kind of immersive interactive technology, virtual reality technology is attracting increasing attention in education. This paper describes the characteristics of immersive VR, hardware and software types, summarizes the different forms of VR used in teaching and discusses the principles of biological virtual experiment construction in other research. Taking the teaching content of ‘structure of eyeball’ in middle school biology textbook as a case, this research designs and develops a virtual experiment courseware - Miraculous eyeball. The research suggests that to construct outstanding teaching experiments of VR not only needs mature equipment and technologies but also requires a combination of decent teaching theories and tactics.

The automatic solution of mathematical problems is one of the difficulties in the research of mathematics intelligent tutoring system. In recent years, the construction of corpus wins more and more attention, and is applied in various kinds of domains, especially in natural language processing. This paper is aimed at elementary mathematics statistics applications. We collected the university entrance exam questions, review questions and the relevant knowledge of 2008–2014, and then formed a corpus consists of the formalization of corpora in XML. The construction of the corpus is to make computer understand and then deal with the applications of elementary mathematical statistics, it’s the foundation for automatic solution of the applications.

The thesaurus dataset of educational technology is a knowledge description of educational technology in Chinese. The aims of this thesaurus were to collect the subject terms in the domain of educational technology, facilitate the standardization of terminology and promote the communication between Chinese researchers and scholars from various countries. This thesaurus contains 2367 subject terms and 752 relation pairs between subject terms. This thesaurus can be used in bilingual control, index and retrieval of information resources. [ABSTRACT FROM AUTHOR]

As educational informatization develops continuously, blended learning has become the focus of increasing amount of research works. Among these studies, researchers indicate that collaborative learning, as an important teaching strategy, is of great effectiveness in promoting students’ learning performance either in E-learning or classroom teaching. However, due to the fact that different students may have different learning styles, it is crucial for teachers to take this factor into consideration. Therefore, to enhance the validity of grouping in collaborative learning, this paper proposes a grouping strategy based on the analysis results of students’ learning styles using K-Means and hierarchical clustering. Results of clustering analysis can provide a valuable reference no matter for homogeneous grouping or heterogeneous grouping.

Chinese word segmentation is a difficult and challenging job because Chinese has no white space to mark word boundaries. Its result largely depends on the quality of the segmentation dictionary. Many domain phrases are cut into single words for they are not contained in the general dictionary. This paper demonstrates a Chinese domain phrase identification algorithm based on atomic word formation. First, atomic word formation algorithm is used to extract candidate strings from corpus after pretreatment. These extracted strings are stored as the candidate domain phrase set. Second, a lot of strategies such as repeated substring screening, part of speech (POS) combination filtering, and prefix and suffix filtering and so on are used to filter the candidate domain phrases. Third, a domain phrase refining method is used to determine whether a string is a domain phrase or not by calculating the domain relevance of this string. Finally, sort all the identified strings and then export them to users. With the help of morphological rules, this method uses the combination of statistical information and rules instead of corpus machine learning. Experiments proved that this method can obtain better results than traditional n-gram methods.

Adaptive learning requires the learning contents to be presented to students dynamically in a personalized fashion. Multiple displays on cross-platform devices can meet the diverse needs for content presentation. Current practices approach web page design for multi-terminal devices on a trial-and-error basis. This paper discusses a web page design method that uses the client resolution adaptive table for multi-terminal devices. To obtain the table, we calculate the character size and other elements for the best visual effect by adapting to the client's device resolution.

A novel electrochemical sensor for catechol was developed by electrodepositing HAuCl4 and cobalt hexacyanoferrate (CoHCF) on an ordered mesoporous SBA-15 decorated glassy carbon electrode (GCE). The CoHCF/Au/SBA-15 film was characterized by scanning electron microscopy (SEM) and impedance spectra. A mesoporous SBA-15 was used as a platform that enlarged the surface area of the working electrode. The CoHCF/ Au/SBA-15 modified electrode showed good electrocatalytic activity to catechol and the electrocatalytic response was measured using cyclic voltammetry and Amperometric i-t curve. The electrochemical performance of the sensor for catechol was further enhanced due to the deposition of Au on the electrode surface. Under the optimal conditions, the sensor showed a linear range from 3.0 × 10-7M to 5.1 × 10-5M of catechol, with a detection limit of 50 nM (S/N=3). Good reproducibility, stability and good selectivity in the presence of numerous organic phenolics made the CoHCF/ Au/SBA-15 modified electrodes applicable to the determination of catechol in the various water samples.

The adaptive learning system is a hotspot in the field of e-Learning and intelligent education researches. Learner model is the core of adaptive learning system. However, in the light of rapidly growing “big data”, adaptive learning system is facing the challenge of dealing with the realistic data. So in order to respond to this situation, we take a summary of oversea and domestic learner model norm of e-Learning, and then explore the ant colony algorithm and propose a construction method of learner model with consideration on the learners’ knowledge, interests and individual traits in the adaptive learning system. Especially, we regard the learners’ cognitive ability as a vital aspect of the learner model, and creatively employ the forgetting curve to the quantitative learner model built on ant colony algorithm.

With the scale of educational resources construction continues to expand, how to achieve the semantic description and sharing of educational resources, and provide users with semantic-level resource support services, is a key problem which we should pay attention to in the field of knowledge services. This paper proposes a solution on description and retrieval of educational resources metadata based on domain ontology. It can achieve the semantic sharing and service of educational resources. We give the relevant implementation using the major of “Educational Technology” as an example which has shown good effect.

As the main approach of distance education, the key point of Network Learning research has turned to getting the learners' personality characteristics and constructing the virtual learning environment based on theories of modern education and psychics. Currently, the characteristics of learners are stored in database statically. We can't timely acquire the latest interests of learners. The paper proposes an approach to get learners' characteristics dynamically. The approach uses data mining technology to analyse learners' network learning behaviors. The experiment result sugguests it is useful for acquisition of learners' characteristics.

With the sudden growth of information technology, network resources and resource search technology have been developed rapidly. However, general search engines such as Google and Baidu are difficult to satisfy our retrieval requirements in professional field because the lack of professionalism. This paper constructs a vertical educational resource oriented search engine based on the circumstances of educational field. This search engine adds the professional vocabulary on the basis of general search engine, and puts forward an improved sequencing algorithm based on specialized vocabulary. These measures improved the recall and precision of resource search and achieved good effect in practical application.

When the traditional text classification technologies classify academic dissertations, the dimension of extracted feature terms is high, and they can't represent the theme of thesis. it makes the efficiency is very low and the accuracy rate is not high. The topic words are small in quantity and can reflect the theme of thesis well. Accordingly, the paper proposes to extract the topic words with topic word list and uses topic words as feature terms. Then using the Bayesian Classification method classifies vast texts. The experiments show that the Bayesian Classification method using topic words as feature terms can greatly reduce the dimension and improve the efficiency of classification, when the dimension of feature terms is equivalent, the accuracy of Bayesian Classification method using topic words as feature terms is also higher than the traditional Bayesian text classification methods.

Because of its important application value in almost every region, early-warning has received extensive concern. This paper puts forward a study early-warning mechanism based on association rules. It uses an Apriori mining algorithm with some corresponding restrictions to dig out the latent school record association rules from former students' scores which are viewed as a history resource. Then these rules will be used to match up the data sets that need to monitor. Once a record is matched to one of these rules, the student of this record will receive an early-warning. This kind of early-warning mechanism changes the situation that problems with study can only be detected after knowing the scores. It has a predictable and forward-looking capability and has been proved to obtain a good early-warning effect during actual verification.

XML is widely used in development of educational resources database system (ERDS) because of its natural traits: easy-to-interpreted, criterion-opened, extensive, cross-platform compatible. But there are some limitations naturally when we use the relational database to store and search XML data. SQL SERVER 2005 and ASP.NET 2.0 provide stronger function to support XML. Through analyzing the criterion, functions and structure about ERDS, this paper gives us a solution about ERDS based on SQL SERVER 2005 and ASP.NET 2.0. Also this paper discusses the technology and realization about how to construct, store, access and present the educational resources.