Information To Users Research Articles

How reliable is the artificial intelligence product large language model ChatGPT in orthodontics?

To evaluate the reliability of information produced by the artificial intelligence-based program ChatGPT in terms of accuracy and relevance, as assessed by orthodontists, dental students, and individuals seeking orthodontic treatment. Frequently asked and curious questions in four basic areas related to orthodontics were prepared and asked in ChatGPT (Version 4.0), and answers were evaluated by three different groups (senior dental students, individuals seeking orthodontic treatment, orthodontists). Questions asked in these basic areas of orthodontics were about: clear aligners (CA), lingual orthodontics (LO), esthetic braces (EB), and temporomandibular disorders (TMD). The answers were evaluated by the Global Quality Scale (GQS) and Quality Criteria for Consumer Health Information (DISCERN) scale. The total mean DISCERN score for answers on CA for students was 51.7 ± 9.38, for patients was 57.2 ± 10.73 and, for orthodontists was 47.4 ± 4.78 (P = .001). Comparison of GQS scores for LO among groups: students (3.53 ± 0.78), patients (4.40 ± 0.72), and orthodontists (3.63 ± 0.72) (P < .001). Intergroup comparison of ChatGPT evaluations about TMD was examined in terms of the DISCERN scale, with the highest value given in the patients group (57.83 ± 11.47) and lowest value in the orthodontist group (45.90 ± 11.84). When information quality evaluation about EB was examined, it GQS scores were >3 in all three groups (students: 3.50 ± 0.78; patients: 4.17 ± 0.87; orthodontists: 3.50 ± 0.82). ChatGPT has significant potential in terms of usability for patient information and education in the field of orthodontics if it is developed and necessary updates are made.

An Evaluation of a Winter Mortality Model for the Mountain Pine Beetle

The winter mortality of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae caused by temperatures below survivable thresholds and sudden decreases in temperature is among the most influential factors limiting population growth. Due to the importance of winter mortality in the demise of mountain pine beetle infestations, a widely used winter mortality model was developed at the Canadian Forest Service and the United States Forest Service. It predicts lethal temperatures and survival probabilities given temperature time series over the winter season. We present a rare and possibly the first peer-reviewed validation of this winter mortality model, wherein we independently tested the model at a new region in Canada by comparing model predictions to the observed lower lethal temperature thresholds and cold-associated mortality. Model predictions were biologically reasonable but slightly biased. Bias was exacerbated by the inaccurate translation of air temperature data from weather stations to temperatures under the bark where larvae develop. The spatial prediction of relative mortality observed across the study area in Banff National Park was poor—likely because the mountainous terrain presents a difficult prediction challenge when under-bark temperatures are not directly observed. Our results will help inform users of model constraints and how to optimize the accuracy of model predictions.

Barriers to Early Progress in Adult Cochlear Implant Outcomes.

Adult cochlear implant (CI) recipients obtain varying levels of speech perception from their device. Adult CI users adapt quickly to their CI if they have no peripheral "bottom-up" or neurocognitive "top-down" limiting factors. Our objective here was to understand the influence of limiting factors on the progression of sentence understanding in quiet and in noise, initially and over time. We hypothesized that the presence of limiting factors, detected using a short test battery, would predictably influence sentence recognition with practical consequences. We aimed to validate the test battery by comparing the presence of limiting factors and the success criteria of >90% sentence understanding in quiet 1 month after activation. The study was a single-clinic, cross-sectional, retrospective design incorporating 32 adult unilateral Nucleus CI users aged 27 to 90 years (mean = 70, SD = 13.5). Postoperative outcome was assessed through sentence recognition scores in quiet and in varying signal to noise ratios at 1 day, 1 to 2 months, and up to 2 years. Our clinic's standard test battery comprises physiological and neurocognitive measures. Physiological measures included electrically evoked compound action potentials for recovery function, spread of excitation, and polarity effect. To evaluate general cognitive function, inhibition, and phonological awareness, the Montreal Cognitive Assessment screening test, the Stroop Color-Word Test, and tests 3 and 4 of the French Assessment of Reading Skills in Adults over 16 years of age, respectively were performed. Physiological scores were considered abnormal, and therefore limiting, when total neural recovery periods and polarity effects, for both apical and basal electrode positions, were >1.65 SDs from the population mean. A spread of excitation of >6 electrode units was also considered limiting. For the neurocognitive tests, scores poorer than 1.65 SDs from published normal population means were considered limiting. At 1 month, 13 out of 32 CI users scored ≥90% sentence recognition in quiet with no significant dependence on age. Subjects with no limiting peripheral or neurocognitive factors were 8.5 times more likely to achieve ≥90% score in quiet at 1 month after CI switch-on (p = 0.010). In our sample, we detected 4 out of 32 cases with peripheral limiting factors that related to neural health or poor electrode-neural interface at both apical and basal positions. In contrast, neurocognitive limiting factors were identified in 14 out of 32 subjects. Early sentence recognition scores were predictive of long-term sentence recognition thresholds in noise such that limiting factors appeared to be of continuous influence. Both peripheral and neurocognitive processing factors affect early sentence recognition after CI activation. Peripheral limiting factors may have been detected less often than neurocognitive limiting factors because they were defined using sample-based criteria versus normal population-based criteria. Early performance was generally predictive of long-term performance. Understanding the measurable covariables that limit CI performance may inform follow-up and improve counseling. A score of ≥90% for sentence recognition in quiet at 1 month may be used to define successful progress; whereas, lower scores indicate the need for diagnostic testing and ongoing rehabilitation. Our findings suggest that sentence test scores as early as 1 day after activation can provide vital information for the new CI user and indicate the need for rehabilitation follow-up.

(Invited) Hypersectral Sensing of Biological Markers at the Point of Need

There is an urgent need for truly reagent free and agnostic sensing at the point of need for biological and chemical targets. Indeed, the COVID-19 pandemic has re-emphasized the significant need for truly agnostic diagnostics at the point of need. Whereas highly targeted and tailored diagnostics can help us address a known and anticipated threat, they do not prepare us against the next emerging outbreak. To address the significant challenge of developing truly agnostic diagnostics that can improve situational awareness and guide decision making, our team has looked to spectroscopy as an inspiration for the sensing modality; and innate immunity as an inspiration for the biological process and signatures. All biological molecules emanate spectral signatures along the electromagnetic spectrum, integral information from which can provide insights into processes or signatures more effectively. Therefore, we propose the use of hyperspectral sensing as a modality for accurate diagnosis and detection of signatures. With regards to the biology - innate immunity is a broad, agnostic pathogen sensing strategy that allows for the early recognition of all pathogens – known and unknown – with an associated response. Our team has developed strategies to mimic this response in the laboratory, culminating with the (ongoing) development of a data-science and machine learning enabled effort to unravel the complexity of the immune recognition (host cytokine and chemokine response), allowing for their ability to inform on categories of pathogens/disease. It is important to integrate this knowledge with a point of contact diagnostic approach in order to be able to translate the data into usable diagnostic information. To this end, we are working on developing a hyperspectral approach that measures these signatures without reagents from saliva samples in a few seconds. Charged by a back-end machine learning/artificial intelligence algorithm, our approach uses Pattern’s ProSpectral Sensor that measures signatures across various realms of the electromagnetic spectrum. Data on developing this sensing modality as a diagnostic in real time – challenges and advantages – will be presented.

Accounting and innovation: Paths forward for research

Glaeser and Lang (2024; GL) reviews the accounting literature on innovation, which has increased substantially in recent years. GL makes an important contribution to accounting research by bringing into the literature the implications of Romer's Nobel Prize winning endogenous growth theory and by explaining how accounting research addresses questions related to innovation. We contribute to accounting research by building on GL's foundation to suggest three main paths forward for future innovation research. First, focus on innovation's three defining attributes: novelty, nonrivalry, and partial excludability. Second, determine the needs of various users of information about a firm's innovation activities and how to meet those needs; we focus on the needs of investors. Third, address questions our discussion highlights as potentially important for future research on financial reporting and innovation, including the crucial question of an innovation's identifiability.

Level of Satisfaction of Selected Students in Using Digital Library of an Accredited Higher Education Institution

Over the last decade, there have been significant initiatives and experiments to digitize libraries. It was found out that the activities in developing digital libraries were dependent highly on the building of the technology, telecommunications, and standards, the so-called metadata, infrastructures that will make the digital libraries to function technically with efficiency and effectiveness. Little attention and concern is given in assessing the extent to which the digital libraries met the needs of the users in information, research, articles, other academic tasks, facilitated outcomes and the expected quality service. This research study aims to determine and assess the level of satisfaction in the usability of the software, accessibility and services offered by the digital library of First City Providential College (FCPC). The findings from this assessment can have a number of significant implications for the continued development of the digital library of FCPC. The study used descriptive (quantitative) research method and the statistical tools consisted of the Likert Scale in determining the factors related to the level of satisfaction of the respondents; the percentage for the demographic profile on the following: age, sex, and course; the average weighted mean to find out the level of satisfaction of the respondents in using the components and provisions of the FCPC digital library. Results revealed that the respondents using the digital library of FCPC were very satisfied in using the resources and were appreciative of the assistance and services rendered by the librarian and staff to them. They presented one suggestion that in the inclusion of new online apps in navigating data or information, they could be given a thorough orientation or webinar on the said item or provision.

Content Classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using Vector Space Model (VSM)

The official website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) is an important platform for disseminating information to a diverse audience. Efficiently categorizing the vast amount of content available on the website is essential for enhancing user experience and optimizing information retrieval. These categories will also become an identifier and topic classification based on the content inside the article. This study presents a systematic approach to content classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using the Vector Space Model (VSM). The methodology involves preprocessing the text data, constructing a term-document matrix, and implementing cosine similarity to measure the relevance of documents to predefined categories. The study demonstrates the effectiveness of VSM in accurately classifying content, thus facilitating streamlined access to information for users navigating the website. Furthermore, the findings offer insights into enhancing the organization and accessibility of governmental online platforms, contributing to improved user experience and information dissemination.

INVESTORS' INTEREST IN THE REGULATORY EFFECT OF CASH FLOWS ON EXTREME CORPORATE SOCIAL RESPONSIBILITY

Today, information users are interested in disclosing non-financial information regarding activities carried out within the scope of corporate social responsibility (CSR). In this context, the reflection of CSR activities, which can increase company reputation and stakeholder interest, on financial performance is important. The research aims to determine the relationship of cash flows with firm value at optimal and extreme CSR levels. The research uses data on companies with ESG scores in Turkey to determine the impact of CSR activities on firm value. The research applies panel data analysis to determine the impact of optimal and extreme CSR activities on firm value. The research points out that optimal CSR levels have a negative effect on firm value, while extreme CSR levels have a positive and significant effect. Additionally, the results reveal that cash flows positively moderate the relationship between extreme CSR levels and firm value. The research findings support that cash flows positively moderate the relationship between firm value and CSR at extreme levels. The research emphasizes the mediating role of cash flows by distinguishing between extreme and optimal CSR activities, in line with the literature on the relationship between CSR levels and firm value.

Secure beamforming design for MISO URLLC networks in IoT applications

This paper investigates joint beamforming and artificial noise (AN) design for secure multiple-input single-output (MISO) ultra-reliable and low latency communication (URLLC) networks in Internet of Things (IoT) applications. In considered system, a base station (BS) transmits confidential information for individual IoT users using the short-packet communication technique under the wiretap of eavesdroppers. To enhance physical layer security, the BS injects additional dedicated AN symbols to degrade the information retrieval ability at eavesdroppers. In this paper, we aim to joint design the transmit beamforming and AN symbol to maximize the minimum URLLC secrecy rate of IoT user subject to the power budget of the BS. The optimization design problem is highly nonconvex due to coupled variables in the URLLC secrecy rate and channel dispersion expressions, and thus it is mathematically challenging to solve this problem directly. To overcome this issue, we first introduce various convex inner approximations to convexify the nonconvex terms, and then we develop an efficient iterative algorithm based on the sequential convex programming approach. Extensive numerical simulation results are conducted to investigate the URLLC secrecy rate region. In conclusion, the two new URLLC parameters, i.e., the transmit packet blocklength and block error probability, will cause the considerable degradation on the URLLC secrecy rate region when comparing to that of the traditional beamforming design based on the Shannon capacity.

Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline.

The growing adoption and use of health information technology has generated a wealth of clinical data in electronic format, offering opportunities for data reuse beyond direct patient care. However, as data are distributed across multiple software, it becomes challenging to cross-reference information between sources due to differences in formats, vocabularies, and technologies and the absence of common identifiers among software. To address these challenges, hospitals have adopted data warehouses to consolidate and standardize these data for research. Additionally, as a complement or alternative, data lakes store both source data and metadata in a detailed and unprocessed format, empowering exploration, manipulation, and adaptation of the data to meet specific analytical needs. Subsequently, datamarts are used to further refine data into usable information tailored to specific research questions. However, for efficient analysis, a feature store is essential to pivot and denormalize the data, simplifying queries. In conclusion, while data warehouses are crucial, data lakes, datamarts, and feature stores play essential and complementary roles in facilitating data reuse for research and analysis in health care.

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

Climate change beliefs and behaviours: Data collected from 30-year-old offspring and their parents in the Avon Longitudinal Study of Parents and Children (ALSPAC)

Climate change is causing a suite of environmental and social issues. Understanding patterns of climate change beliefs and behaviours, and the factors which shape these attitudes and actions, is therefore essential. This data note describes the novel data collection of climate belief and behaviour questions embedded within the Avon Longitudinal Study of Parents and Children (ALSPAC), a longitudinal population-based birth cohort in southwest England. Two generations of ALSPAC participants completed the questionnaires containing these climate questions: the Generation-1 (G1) offspring (n=4,341), Generation-0 (G0) mothers (n=4,793), and the G0 mother’s partners (n=2,111). The questions asked about: belief in climate change; concern over climate change; whether climate change is caused by human activity; whether individual behaviours can impact climate change; the impacts of climate change in the participants’ local area; and whether participants were engaging in any of 17 pro-environmental behaviours (e.g., recycling more, reducing meat/dairy consumption) for climate change and/or other reasons. The majority of participants believed the climate was definitely changing (~80%), were somewhat or very concerned about climate change (~90%) and attributed most or all of climate change to human activity (~75%), with only small differences between the generations. On average, participants reported engaging in between 5 and 6 actions because of concerns regarding climate change (5.8 in G0 mothers; 5.2 in G1 offspring and G0 partners). Associations with sociodemographic factors (e.g., age, sex, ethnicity and socioeconomic background) were relatively weak, but positive relationships between highest level of education and greater climate change belief, concern and behaviours were evident. Increased climate belief and concern also predicted engaging in more pro-environmental actions. Responses to these climate questions were more similar within-families. This data note provides important information for future users of these data, as well as discussing some of the key strengths, limitations and considerations for data users.

Debt Accounting System Analysis in Accounting Information Systems

The Debt Information System and Accounting Information System support and collaborate with each other to ensure that the company's financial information is well managed, regulatory compliance is maintained, and the right business decisions can be taken. This research uses a literature study method with the data used is secondary data. The research results show that the Debt Accounting System and the Accounting Information System are interrelated in processing and producing financial reports, especially in this case company debt. This is also supported by the sophistication of information technology, user capabilities, user participation, and top management support.

Enhancing Database Query Performance: Analysis of Indexing Techniques

The need to enhance query performance in databases within the contemporary information environment is crucial for ensuring efficient operations across various domains. This paper is dedicated to an analysis of database indexing techniques aimed at understanding their impact on query performance and efficiency. It meticulously examines various types of indexes, including B-trees, hash tables, and textual indexes, analyzing their advantages and limitations. The investigation into the influence of these techniques on query execution time reveals that performance depends on various factors such as query complexity and data volume. Special attention is given to selecting the optimal index type based on specific database needs and characteristics. The research underscores the importance of considering data volume and database structure in choosing effective indexing methods. Additionally, the advantages and disadvantages of different indexing techniques are scrutinized, taking into account their impact on query execution speed and efficiency. Of the study indicate that the correct selection and utilization of appropriate indexing strategies can significantly enhance the performance of database management systems, providing swift and efficient access to information for users. Ultimately, this work makes a significant contribution to understanding and practically applying indexing techniques to improve query performance in databases.

An Indigenous-informed scoping review study methodology: advancing the science of scoping reviews

BackgroundHistorically, Indigenous voices have been silent in health research, reflective of colonial academic institutions that privilege Western ways of knowing. However, Indigenous methodologies and methods with an emphasis on the active involvement of Indigenous peoples and centering Indigenous voices are gaining traction in health education and research. In this paper, we map each phase of our scoping review process and weave Indigenous research methodologies into Arksey and O’Malley’s (2005) framework for conducting scoping reviews.MethodsGuided by an advisory circle consisting of Indigenous Knowledge Keepers and allied scholars, we utilized both Indigenous and Western methods to conduct a scoping review. As such, a circle of Knowledge Keepers provided guidance and informed our work, while our methods of searching and scoping the literature remained consistent with PRISMA-ScR guidelines. In keeping with an Indigenous methodology, the scoping review protocol was not registered allowing for an organic development of the research process.ResultsWe built upon Arksey and O’Malley’s 5-stages and added an additional 3 steps for a combined 8-stage model to guide our research: (1) Exploration and Listening, (2) Doing the Groundwork, (3) Identifying and Refining the Research Question, (4) Identifying Relevant Studies, (5) Study Selection, (6) Mapping Data, (7) Collating, Summarizing and Synthesizing the Data, and lastly, (8) Sharing and Making Meaning. Engagement and listening, corresponding to Arksey and O’Malley (2005)’s optional “consultation stage,” was embedded throughout, but with greater intensity in stages 1 and 8.ConclusionAn Indigenous approach to conducting a scoping review includes forming a team with a wide array of experience in both Indigenous and Western methodologies, meaningful Indigenous representation, and inclusion of Indigenous perspectives to shape the analysis and presentation of findings. Engaging Indigenous peoples throughout the entire research process, listening, and including Indigenous voices and perspectives is vital in reconciliation research, producing both credible and useable information for both Indigenous communities and academia. Our Indigenous methodology for conducting a scoping review can serve as a valuable framework for summarizing Indigenous health-related research.

МОДЕЛИРОВАНИЕ НАРУШИТЕЛЯ, ИНФРАСТРУКТУРЫ И АТАК В СИСТЕМАХ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ

Formal models of subjects, infrastructure and attacks for information security systems are proposed. The models include descriptions of information security operators, administrators, users and violators, taking into account their knowledge, qualifications and initial conditions. A comprehensive intruder model is presented, including initial knowledge and access rights, initial location, qualifications and goals. Infrastructure models, vulnerabilities, and information collection methods are also considered, which makes it possible to more accurately predict the behavior of violators and develop effective protection strategies. The results of the study show that the proposed models significantly improve the accuracy of risk assessment and security planning, which is especially important for mission-critical information systems. The practical significance lies in the possibility of using models to develop and improve information network security systems. The results of the practical implementation of the model on real data are also presented.

How much does reclaimed wastewater cost? A comprehensive analysis for irrigation uses in the European Mediterranean context

ABSTRACT The new European Union regulation on the minimum requirements for the use of reclaimed water for irrigation entered into force in June 2023, thereby imposing concerns regarding the costs of this non-conventional resource for potential users in the context of increasing water scarcity in the Mediterranean region. This research offers a comprehensive cost assessment of reclaimed water production based on the financial information gathered from wastewater treatment plants located on the Mediterranean coast of Andalusia (Southern Spain). The results offer valuable information for policy-makers, water operators, and potential users to assess the economic viability of utilising reclaimed water as an alternative source to scarce conventional resources.

Analisis Keberhasilan Sistem Informasi Sentuh Tanahku Badan Pertanahan Nasional Menggunakan Metode ISSM

The use and usefulness of information technology is an important issue in today's life. This can be seen as a way of using technology to support human activities in various sectors or fields, including government and user security. Of course, the use and usefulness of information technology in various agencies is very necessary to support the implementation of better government programs. The use and utilization of technology in the government environment can be carried out with the help of a research information system. Research is located at two government information agencies: Lubuk-Linggau City National Land Agency; The information system used by the National Land Agency is called Touch My Land based on Android. The implementation of an information system is said to be successful if it can be proven that the information system produces significant positive impacts or benefits for some of its users. The DeLone and McLean evaluation concept is considered a successful information system model. The research method used in this research is a quantitative method with a sample size as many as 61 people from National Land Agency. This research uses six variables and nine hypotheses. The sixth variable consists of information quality, system quality, service quality, usage intention, user satisfaction and net profit. The research results show that several research hypotheses are accepted because the t-statistic value is greater than 1.96.

Weakly Supervised Deep Embedding for Product Review Sentiment Analysis

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this project, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines. In our project we have used algorithms like Decision Tree (DT) and Multi Layer Perceptron (MLP). All are measured in terms of accuracy

A Comparison of Using Multiple Choice and Cloze Tests on Students’ Achievement in Reading Comprehension at Second Semester of English Department of FKIP Batanghari University

The purpose of the study was to find out the comparative achievement of multiple choice test and bracket test in reading comprehension in the second semester of the department of english, University of FKIP Batanghari. This study used a mixed method approach. The subject of the test would be second semester english students of FKIP Batanghari University. Data was collected through two instruments, which were a test and an interview. Based on the results of this study, the author found that students scored higher in the cloze test than in the multiple choice test. The result of the interview showed that the students preferred to answer the question in the cloze test than in the multiple choice question because they were easier to understand. These perceptions confirm the differences in student scores on the first research question. However, the result also showed that the multiple choice test is a better measure of students' reading comprehension. The students' impressions and opinions about the tests and their own performance were recorded and considered. Implications of results and recommendations further research is needed to obtain detailed and usable information to improve students' reading comprehension and achievement in the future.