Identification Phase Research Articles

Two Phase - Intrusion Detection System (TP-IDS) model using Machine Learning Techniques

Network security is one of the important issues that should be handled carefully and security to our data and important information is necessary when communicating with outside world over the internet. We have so many examples of the attacks that have been happened in the past and threatened a huge loss to the world. Over the years, a lot of work is done in this area and many security systems are developed, many security algorithms are implemented which have strong security fundamentals. Also, these systems have proven strong at the time of highly insecurity incidences or attacks. But, the problem is still there in identifying the malicious node entry in the network correctly without any false positives or false negatives, also within quick time before getting any type of access to the network by such malicious nodes which are also called as intruders. We need a system which should be operating faster even in heavy incoming network traffic scenarios, and generating correct identification of the intruders as well as giving easy, faster network access to non intruders or normal connecting nodes by correctly identifying them as normal nodes. In this research work, we are carrying out the implementation of the Intrusion detection System (IDS) model which performs faster as well as classifies the communicating nodes correctly as intruder or normal user. This IDS model is being implemented in two phases, hence a Two Phase Intrusion Detection System, abbreviated and named as TP-IDS model. We are using the machine learning techniques to develop this model, which makes this model a strongly secure IDS model and helping to better identify the known as well as unkown attack. We are using first phase for the identification and second phase for the validation of the first phase results of our model, which provides high accuracy. In first phase of the IDS we are using Support Vector Machine (SVM), k Nearest Neighbor (kNN) and in second phase we are using Decision Tree (DT), Naive Bayes (NB) for first phase validation. Also, the issue of efficiency is handled by underlying data processing infrastructure, which is HADOOP that increases efficiency or speed of the TP-IDS.

The implementation of a national paediatric oncology protocol for neuroblastoma in South Africa.

PurposeThe aim of the World Health Organization-International Paediatric Oncology Society is to improve childhood cancer survival in low- and middle-income countries to 60% by 2030. This can be achieved using standardised evidence-based national treatment protocols for common childhood cancers. The aim of the study was to describe the development and implementation of the SACCSG NB-2017 neuroblastoma (NB) treatment protocol as part of the treatment harmonisation process of the South African Children’s Cancer Study Group.MethodsThe Consolidated Framework for Implementation Research was used to identify factors that could influence the implementation of the national NB protocol as a health care intervention. The evaluation was done according to five interactive domains for implementation: intervention characteristics, inner setting, outer setting, individual or team characteristics and the implementation process.ResultsThe protocol was developed over 26 months by 26 physicians involved in childhood cancer management. The process included an organisational phase, a resource identification phase, a development phase and a research ethics approval phase. Challenges included nationalised inertia, variable research ethical approval procedures with delays and uncoordinated clinical trial implementation.ConclusionThe implementation of the national NB protocol demonstrated the complexity of the implementation of a national childhood cancer treatment protocol. However, standardised paediatric cancer treatment protocols based on local expertise and resources in limited settings are feasible.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10552-021-01424-2.

The neurodevelopment of delay discounting for monetary rewards in pre-adolescent children

Children are found to exhibit high degrees of delay discounting compared with adults in many delay discounting studies, which might be due to the asynchronous development of “bottom-up” and “top-down” neural systems. However, the temporal dynamics associated with the two systems in the development of delay discounting processes are not well known. In this study, we chose two age groups of participants and adopted event-related potential (ERP) techniques to investigate the neural dynamic differences between children and adults during delay discounting processes. Behavioral findings showed that children discounted more than adults and chose more immediate choices. Electrophysiological findings revealed that children exhibited longer neural processing (longer P2 latency) than adults during the early detection and identification phase. Children showed less cognitive control (smaller N2 amplitude) than adults over the middle frontal areas, and they devoted more neural effort (larger P3 amplitudes) to making final choices than adults. The factors of reward amount and time delay could influence the development of delay discounting in children.

NOVEL MULTI-LEVEL ASPECT BASED SENTIMENT ANALYSIS FOR IMPROVED ROOT-CAUSE ANALYSIS

Aspect extraction and sentiment identification are the two important tasks to provide effective root cause analysis. This work presents a Multi-Level Aspect based Sentiment Analysis (MLASA) model that integrates the aspect extraction and sentiment identification modules to provide effective root cause analysis. The aspect extraction module performs token filtration, followed by rule based aspect identification. The heterogeneous multi-level sentiment identification phase performs aspect based sentiment identification. First level performs magnitude along and polarity identification of text, while the second level performs polarity identification using multiple machine learning models. The results are aggregated and ranked based on aspect significance and sentiment magnitude. Experiments and comparisons show effective performance of the MLASA model.

A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset.

Identification of the Gray–Scott Model via Deterministic Learning

Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.

Two-Stage Biometric Identification Systems Without Privacy Leakage

We study two-stage biometric identification systems that allow authentication without privacy leakage. In the enrollment phase, secret keys and two layers of the helper data for each user are generated. Additional to the helper data and secret keys, we also introduce private keys in the systems. In the identification phase, an unknown but previously enrolled user is observed, and the user's private key is also presented to the system. The system firstly compares the user with the first layer helper database, outputs a list, and obtains a set of user indices. Then the system compares the observed user with the users in the set. Therefore, the identification procedure avoids an exhaustive search and only has to do a comparison with some part of the users in the system, which leads to a systematic reduction of the search complexity. Fundamental trade-offs among the identification rate, the secret key rate, the private key rate, the helper data rate, and the list size rate are derived in the imposed two-stage biometric identification systems without privacy leakage. Moreover, the obtained results show that a private key can boost the identification rate and the secret key rate, as well as preserve privacy.

Exploring Human Trafficking Victimization Experiences in Portugal

ABSTRACT This study aims to access the experience of the victims of human trafficking, during recruitment, exploitation, and identification phases. The data were collected through a semi-structured interview, resulting in a clinical-forensic sample of nine adult victims of both sexes – two females and seven males, aged between 42 and 67 years (M = 56.67; SD = 8.411). Data analysis was done using thematic analysis and allowed to reinforce the dynamics of trafficking in human beings, concerning victims and traffickers (e.g., the deception in the recruitment phase) and dynamics of exploitation (e.g., entrapment strategies). This study was able to provide novelty concerning the hetero identification of the victims by community members. Despite the control strategies employed by the traffickers, some victims were identified and reported by neighbors to competent authorities. This should be considered as it may be indicative that educational campaigns are having an effect by enhancing the victims’ identification.

Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

Prescription of Antibiotic Prophylaxis for Dental Implant Surgery in Healthy Patients: A Systematic Review of Survey-Based Studies.

Background: Systemic antibiotic prophylaxis is frequently prescribed by dentists performing dental implant surgery to avoid premature implant failure and postoperative infections. The scientific literature suggests that a single preoperative dose suffices to reduce the risk of early dental implant failure in healthy patients. Material and Methods: A systematic review was made based on an electronic literature search in the PubMed-Medline, Embase, Web of Science, Scopus and Open Gray databases. The review addressed the question: “which antibiotic prophylaxis regimens are being used in dental implant surgery in healthy patients according to survey-based studies?” The identification, screening, eligibility and inclusion phases were conducted according to the PRISMA statement by two independent reviewers. The following data were collected: country, number of surveyed dentists, number of dentists who responded (n), response rate, routine prescription of antibiotic prophylactic treatment (yes, no, or conditioned prescription), prescription regimen (preoperative, perioperative or postoperative) and antibiotic choice (first and second choice). Cohen’s kappa coefficient (k) evaluated the level of agreement between the two reviewers. The analysis of risk of bias was performed follow the Joanna Briggs Institute checklist for observational studies. A descriptive statistical analysis was performed to calculate total target sample, sample size and total mean. Results: A total of 159 articles were identified, of which 12 were included in the analysis. Two thousand and seventy-seven dentists from nine different countries on three continents were surveyed. The median response rate was low and disparate between studies. About three-quarters of the surveyed dentists claimed to routinely prescribe systemic antibiotic prophylaxis for dental implant surgery. The prescription regimen was perioperative, postoperative and preoperative, in decreasing order of frequency. The most frequent first choice drug was amoxicillin, with amoxicillin-clavulanic acid as second choice. Conclusions: A majority of dentists from different countries do not prescribe systemic antibiotic prophylaxis for dental implant surgery following the available scientific evidence and could be overprescribing. Efforts are needed by dental educators and professionals to reduce the gap between the use of antibiotic prophylaxis for dental implant surgery as supported by the scientific evidence and what is being done by clinicians in actual practice.

Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment.

In this study, the occupational risk assessment of carbon nanotubes (CNTs) was performed by means of a probabilistic approach. Chronic and subchronic inhalation exposure studies were retrieved during the hazard identification phase of the study. These studies were then used to obtain a guidance value (BMCh, expressed as a lognormal distribution with geometric mean ± geometric standard deviation = 10.0 ± 4.2 µg/m3) for occupational inhalation exposure to CNTs. An exposure scenario was selected from the scientific literature: three different work events (WEs) related to the production of conductive films were considered: (WE1) manufacturing of single walled carbon nanotubes films during normal operation using local exhaust ventilation (LEV); (WE2) manufacturing of SWCNT film without LEV; and (WE3) cleaning of one of the reactors. For each WE, a probability distribution function was applied, considering exposure expressed as mass concentration, as derived from three different measurement techniques. The ratio of the exposure and the BMCh distributions (i.e., the risk characterization ratio—RCR) was used to calculate the probability of occurrence of a relevant occupational risk. All the considered WEs indicated the presence of a risk (i.e., RCR distributions ≥ 1); however, only WE2 resulted in a statistically significant level of risk.

Tableau Business Intelligence Using the 9 Steps of Kimball’s Data Warehouse & Extract Transform Loading of the Pentaho Data Integration Process Approach in Higher Education

This paper discusses about how to build a data warehouse (DW) in business intelligence (BI) for a typical marketing division in a university. This study uses a descriptive method that attempts to describe the object or subject under study as it is, with the aim of systematically describing the facts and characteristics of the object under study precisely. In the elaboration of the methodology, there are four phases that include the identification and source data collection phase, the analysis phase, the design phase, and then the results phase of each detail in accordance with the nine steps of Kimball’s data warehouse and the Pentaho Data Integration (PDI). The result is a tableau as a tool of BI that does not have complete ETL tools. So, the process approach in combining PDI and DW as a data source certainly makes a tableau as a BI tool more useful in presenting data thus minimizing the time needed to obtain strategic data from 2-3 weeks to 77 minutes.

Pre-prediabetes (Hyperinsulinemia and/or Insulinic Resistance Marker): The Early Diagnosis of the Risks of Cardiovascular Diseases, Diabetes, Metabolic Syndromes

Introduction: According to Ralph A. DeFronzo, insulin resistance is the most important metabolic risk factor than glucose tolerance, increased risk of cardiovascular disease (CVD) even before hyperglycemia and increased risk of diabetes. By identifying high-risk patients early, we prevent progression to pre-diabetes and diabetes. As the function of β cells is already impaired in pre-diabetes, attention should be paid to the identification phase PRE-PREDIABETES. Fasting and post-TOG glucose levels increased 13 years before the development of diabetes in the Whitehall II Study. Insulin sensitivity was reduced for 13 years before the diagnosis of pre-diabetes. Therefore, the function of β cells involved in the pre-diabetic state many years before diabetes develops. "All CVD risk factors are closely related to insulin resistance and, if you reverse insulin resistance, you will correct the biggest CVD risk factor." Several moderate studies such as hyperinsulinemia and insulin resistance contribute to the development of atherosclerosis and cardiometabolic diseases. Diagnosis made according to a study published in 2019 by Catherine AP Crofts, Joseph R. Kraft that evaluated the sensitivity and specificity in the diagnosis of hyperinsulinemia and we added to studies of TOG1H> 154 with normal TOG2H that already show subclinical damage to target organs and a higher risk of CVD and progression to diabetes. Methodology: Patients with BMI above 25 without diagnostic criteria for type 2 diabetes underwent the ORAL GLUCOSE TEST (TOG) AND INSULIN (INS) on an empty stomach and 2 hours after 75g of dextrosol. The diagnosis of PRE- PREDIABETES occurs when TOG IN 1 HOUR (TOG1H)> 154mg / dl AND INS FAST OR 2H GREATER 30mcu / ml WITH TOG IN 2H (TOG2H) NORMAL OR WITH TOG2Hnormal and INS GREATER 45. RESULTS: of the 197 patients, mean age between 21 and 64 years, 55% female, only the results of TOG2hNORMAL were evaluated, 75% of which were fasting ins> 30 mcu / ml in 8%, in 2h> 30 in 95%, ins 2h> 45 in 76%, and TOG1HMAIOR 154mg / dl ins 30% and we combined the TOG AND INS results. CONCLUSIONS: A - PRE-PREDIABETES DIAGNOSIS WITH NORMAL TOG2H MAY BE WITH THE BELOW CRITERIA COMBINED OR NOT WITH LOW NEGATIVE FALSE NEGATIVE 1-INS OF FASTING GREATER 30 and / or 2- TOG1H> 154 AND INS 2H > 30 3- INS 2H > 45.

Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes

The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts.

Barriers to service recovery in B2B markets: a TISM approach in the context of IT-based services

PurposeTo address service failure issues, scholars have recognized several service recovery (SR) mechanisms. However, there exist many barriers that retard an effective SR process, and those often lead to negative consequences. Although this is a generic problem applicable across markets, it becomes critical in B2B markets, as they deal with high impact and corporate customers. The purpose of this paper is to identify these SR barriers in B2B markets and explore their linkages toward identifying the key driving barriers.Design/methodology/approachA four-phased methodology to address this call in the IT services industry was used. First, the identification phase led to the selection of 27 barriers based on literature. Second, in the prioritization phase, 12 salient barriers were prioritized with the help of an expert panel. Third, in the modelling phase, the total interpretive structural modelling (TISM) technique was used to examine the mutual influences among these barriers toward modelling their linkages using a digraph. Finally, in the classification phase, these barriers were graphically plotted and classified into four quadrants based on their dependence and driving powers by using Matriced Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis.FindingsTwo strategic barriers, namely, technology obsolescence and poor top management commitment, presented the strongest challenges against effective SR, as they were least dependent on other barriers and exerted strong influence over the operational barriers to executing SR.Practical implicationsBecause the strategic level barriers, exerting strong influence against effective SR, arise from the strategic choices of board and the C-suite, this paper helps them anticipate the recovery-related after effects of their choices, thus helping them in making better decisions.Originality/valueThis paper contributes to research as it is first of its kind to explore and link SR barriers in B2B markets.

Systematic approach towards establishing a National Inventory of Dangerous Pathogens

ABSTRACT International regulations stipulate that countries need to organize their biosafety and biosecurity systems to minimize the risk of accidental (biosafety) or malicious intentional (biosecurity) release of dangerous pathogens. International Health Regulations (IHR) benchmarks from the WHO state that even for a level of limited capacity countries need to ‘Identify and document human and animal health facilities that store/maintain dangerous pathogens and toxins in the relevant sectors and health professionals responsible for them’. This study provides a stepwise, systematic approach and best practices for countries to initiate a national inventory of dangerous pathogens. With a national inventory of dangerous pathogens a country can identify and document information in a dedicated electronic database on institutes that store or maintain dangerous pathogens. The systematic approach for the implementation of a national inventory of dangerous pathogens consists of four stages; identification, preparation, implementation, and maintenance and evaluation. In the identification phase, commitment of the relevant national ministries is to be established, and a responsible government entity needs to be identified. In the preparatory phase, a list of pathogens to be incorporated in the inventory, as well as a list of institutes to include, is to be agreed upon. In the implementation phase, the institutes are contacted, and the collected data is stored safely and securely in a electronical database. Finally, in the maintenance and evaluation phase meaningful insights are derived and reported to the relevant government authorities. Also, preparations for updates and modifications are undertaken, such as modifications of pathogen lists or institute lists. The approach and database, which is available from the authors, have been tested for the implementation of a national inventory of dangerous pathogens in multiple East-African countries. A national inventory of dangerous pathogens helps countries in strengthening national biosafety and biosecurity as well as in their compliance to IHR.

Novel Triplex Procedure for Ranking the Ability of Software Engineering Students Based on Two levels of AHP and Group TOPSIS Techniques

Ranking the strengths and weaknesses of software engineering students in software development life cycle (SDLC) process level is a challenging task owing to (1) data variation, (2) multievaluation criteria, (3) criterion importance and (4) alternative member importance. According to the existing literature, no specified procedure can rank the ability of software engineering students based on SDLC process levels to figure out the strengths and weaknesses of each student. This study aims to present a novel triplex procedure for ranking the ability of software engineering students to address the literature gap. The methodology of the proposed work is presented on the basis of three phases. In the identification phase, four steps are implemented, namely, processing dataset, identifying the criteria, distributing the courses to the software engineering body of knowledge and proposing the pre-decision matrix (DM). The data comprise the GPA and soft skills from 60 software engineering students who graduated from Universiti Pendidikan Sultan Idris in 2016. In the pre-processing phase, three steps are involved as follows. Analytic hierarchy process (AHP) is first used to assign weights to the courses and then multiply the assigned weight by courses, which is the first procedure in the proposed work. In this phase, the construction of DM is presented based on multimeasurement criteria (GPA and soft skills), with SDLC process levels as alternatives. In the development phase, AHP is used again to weight the multimeasurement criteria, and this is the second procedure. In such case, the coordinator and head of the software engineering department are consulted to obtain subjective judgments for each criterion. Technique for order performance by similarity to ideal solution (TOPSIS) is then used to rank the students, which is the third procedure. In the validation, statistical analysis is performed to validate the results by checking the accuracy of the systematic ranking. Results show that (1) integrating AHP and group TOPSIS is suitable for ranking the ability of students. (2) The 60 students are categorized into five ranking groups based on their strength level: 14 collector requirements, 13 designers, 5 programmers, 13 testers and 15 maintenances. (3) Significant differences are observed between the groups’ scores for each level of SDLC, indicating that the ranking results are identical for all levels.

Pathology of Human Social Relationships in the Qur'an

Improvement in the quality of relationships and benefit from bio-social as much as possible is one of the goals of the Qur'an that has been reflected in social verses. This paper is an interdisciplinary and applied research that aims to extract and represent the three stages of pathology and the connection between Quranic teachings and modern sciences and descriptively-analytically identify the harms of social relations. By combining the triangle of behavior mechanism and the social cognitive theory of Bandura and by following the three stages of pathology, an innovative structure in the pathology of social relationships has been achieved, which is in line with the findings of psychology and confirms Quranic teachings. The structure first identifies the desired state, then diagnoses harms, and finally presents strategies for preventing and treating harms in four dimensions: cognitive, emotional, behavioral, and environmental. It also demonstrates that in the identification phase the absence or defect of any of the components may damage interpersonal relationships. In the diagnosis and introduction phases, any of the harms can take the relationship out of the desired and effective state. In the prevention and treatment phases, the teachings of the Holy Quran have a systematic structure that can create a great change in improving social relations.

Systematic Mapping of Open Data Studies: Classification and Trends From a Technological Perspective

The objective of this paper is to classify and analyse all research on open data performed in the scientific community from a technological viewpoint, providing a detailed exploration based on six key facets: publication venue, impact, subject, domain, life-cycle phases and type of research. This paper therefore provides a consolidated overview of the open data arena that allows readers to identify well-established topics, trends, and open research issues. Additionally, we provide an extensive qualitative discussion of the most interesting findings to pave the way for future research. Our first identification phase resulted in 893 relevant peer-reviewed articles, published between 2006 and 2019 in a wide variety of venues. Analysis of the results shows that open data research grew slowly from 2006 but increased significantly as from 2009. In 2019, research interest in open data from a technological perspective overall decreased. This fact could indicate that research is beginning to stabilise, i.e., the open data research hype is over, and the research field is reaching maturity. Main findings are (i) increasing effort in researching on Semantic Web technologies as a mechanism to publish and reuse linked open data, (ii) software systems are proposed to solve open data technical problems; and (iii) considering technological aspects of legislation and standardization is needed to widely introduce open data in society. Finally, we provide complementary insights regarding open data innovation projects, with special emphasis on publication (e.g., open data portals) and consumption (e.g., open data as business enabler) of open data.

텍스트 유형에 따른 초등학생의 영어 단어 식별력 비교

This study investigated the effects of different text types on elementary school students’ word identification. Eighty-four students in the 6th grade of an elementary school were sampled for the experiment. Students were divided into two groups and treated during an 8 week period. Students were taught reading instruction using different types of reading materials: Decodable texts and high-frequency vocabulary texts. The reading materials were chosen from popular textbook series and revised based on CWF (Critical Word Factor). Texts were based on curriculum vocabulary, and matching communication functions in the textbook and the word patterns which had low learning difficulties such as CV, VC, CVC, CCVC. After the experiment, a post-test was carried out on students’ word identification abilities and the students’ reading miscue was analyzed using Reading Miscue Analysis for students’ word strategies use. The research found that students in the decodable text group made significant progress in their word identification than the high-frequency vocabulary text group. It means that the decodable texts scaffolded for using word identification strategies. It was shown that they advanced to the latter part of the word identification phase that they have sufficient knowledge about letter-sound relationships for word decoding. Based on the results, several suggestions on textbook development and reading instruction are discussed.