Abstract Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained.
Photovoltaic (PV) systems have witnessed a rapid increment, the yield mainly relies on the working condition. In most cases, it is hard to obtain the optimal yield. Therefore, Maximum Power Point Tracking (MPPT) controllers witness much attention as an important optimization field of PV systems. These controllers employ different algorithms and they vary in their efficiency, performance, modernity, complexity, and tracking speed. MPPT controllers have witnessed a rapid improvement, they can be generally classified as conventional and advanced methods. Conventional methods are relatively simple but they can't distinguish between the local and global peaks if partial shading occurs, therefore, their efficiency is relatively low. Advanced tracking methods are widely used due to their superior efficiency. Due to the limitation of the singular conventional and advanced methods, hybrid methods find their way to solve these limitations. Selecting the finest MPPT method is still an open issue, this issue can be solved by implementing a survey of the applied methods. This study gives a concise classification and evaluation review of all the applied MPPT methods. This study also provides an accessible reference to undertake mass research works in MPPT in the near future.
BACKGROUND: To provide valid assessments of answers to prognostic questions, systematic reviews must appraise the quality of the available evidence. However, no standard quality assessment method is currently available. PURPOSE: To appraise how authors assess the quality of individual studies in systematic reviews about prognosis and to propose recommendations for these quality assessments. DATA SOURCES: English-language publications listed in MEDLINE from 1966 to October 2005 and review of cited references. STUDY SELECTION: 163 systematic reviews about prognosis that included assessments of the quality of studies. DATA EXTRACTION: A total of 882 distinct quality items were extracted from the assessments that were reported in the various reviews. Using an iterative process, 2 independent reviewers grouped the items into 25 domains. The authors then specifically identified domains necessary to assess potential biases of studies and evaluated how often those domains had been addressed in each review. DATA SYNTHESIS: Fourteen of the domains addressed 6 sources of bias related to study participation, study attrition, measurement of prognostic factors, measurement of and controlling for confounding variables, measurement of outcomes, and analysis approaches. Reviews assessed a median of 2 of the 6 potential biases; only 2 (1%) included criteria aimed at appraising all potential sources of bias. Few reviews adequately assessed the impact of confounding (12%), although more than half (59%) appraised the methods used to measure the prognostic factors of interest. LIMITATIONS: Reviews may have been missed by the search or misclassified because of incomplete reporting. Validity and reliability testing of the authors' recommendations are required. CONCLUSIONS: Quality appraisal, a necessary step in systematic reviews, is incomplete in most reviews of prognosis studies. Adequate quality assessment should include judgments about 6 areas of potential study biases. Authors should incorporate these quality assessments into their synthesis of evidence about prognosis.
AIM: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process. BACKGROUND: Recent evidence-based practice initiatives have increased the need for and the production of all types of reviews of the literature (integrative reviews, systematic reviews, meta-analyses, and qualitative reviews). The integrative review method is the only approach that allows for the combination of diverse methodologies (for example, experimental and non-experimental research), and has the potential to play a greater role in evidence-based practice for nursing. With respect to the integrative review method, strategies to enhance data collection and extraction have been developed; however, methods of analysis, synthesis, and conclusion drawing remain poorly formulated. DISCUSSION: A modified framework for research reviews is presented to address issues specific to the integrative review method. Issues related to specifying the review purpose, searching the literature, evaluating data from primary sources, analysing data, and presenting the results are discussed. Data analysis methods of qualitative research are proposed as strategies that enhance the rigour of combining diverse methodologies as well as empirical and theoretical sources in an integrative review. CONCLUSION: An updated integrative review method has the potential to allow for diverse primary research methods to become a greater part of evidence-based practice initiatives.
Watershed models are powerful tools for simulating the effect of watershed processes and management on soil and water resources. However, no comprehensive guidance is available to facilitate model evaluation in terms of the accuracy of simulated data compared to measured flow and constituent values. Thus, the objectives of this research were to: (1) determine recommended model evaluation techniques (statistical and graphical), (2) review reported ranges of values and corresponding performance ratings for the recommended statistics, and (3) establish guidelines for model evaluation based on the review results and project-specific considerations; all of these objectives focus on simulation of streamflow and transport of sediment and nutrients. These objectives were achieved with a thorough review of relevant literature on model application and recommended model evaluation methods. Based on this analysis, we recommend that three quantitative statistics, Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR), in addition to the graphical techniques, be used in model evaluation. The following model evaluation performance ratings were established for each recommended statistic. In general, model simulation can be judged as satisfactory if NSE > 0.50 and RSR < 0.70, and if PBIAS + 25% for streamflow, PBIAS + 55% for sediment, and PBIAS + 70% for N and P. For PBIAS, constituent-specific performance ratings were determined based on uncertainty of measured data. Additional considerations related to model evaluation guidelines are also discussed. These considerations include: single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude. A case study illustrating the application of the model evaluation guidelines is also provided.
A good number of abstracts and research articles (in total 74) published, so far, for evaluating antioxidant activity of various samples of research interest were gone through where 407 methods were come across, which were repeated from 29 different methods. These were classified as in vitro and in vivo methods. And those are described and discussed below in this review article. In the later part of this review article, frequency of in vitro as well as in vivo methods is analyzed with a bar diagram. Solvents are important for extracting antioxidants from natural sources. Frequency of solvents used for extraction is also portrayed and the results are discussed in this article. As per this review there are 19 in vitro methods and 10 in vivo methods that are being used for the evaluation of antioxidant activity of the sample of interest. DPPH method was found to be used mostly for the in vitro antioxidant activity evaluation purpose while LPO was found as mostly used in vivo antioxidant assay. Ethanol was with the highest frequency as solvent for extraction purpose.
BACKGROUND: Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan ( http://rayyan.qcri.org ), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan's users and collected feedback through a built-in feature. RESULTS: Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The "taster" review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The "suggestions" and "hints," based on the "prediction model," appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. CONCLUSIONS: Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training. Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers employ accuracy as a measure to discriminate the optimal solution during the classification training. However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less informativeness and bias to majority class data. This paper also briefly discusses other metrics that are specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration in constructing a new discriminator metric.
A central theme in recent research on attitudes is the distinction between deliberate, "explicit" attitudes and automatic, "implicit" attitudes. The present article provides an integrative review of the available evidence on implicit and explicit attitude change that is guided by a distinction between associative and propositional processes. Whereas associative processes are characterized by mere activation independent of subjective truth or falsity, propositional reasoning is concerned with the validation of evaluations and beliefs. The proposed associative-propositional evaluation (APE) model makes specific assumptions about the mutual interplay of the 2 processes, implying several mechanisms that lead to symmetric or asymmetric changes in implicit and explicit attitudes. The model integrates a broad range of empirical evidence and implies several new predictions for implicit and explicit attitude change.
Knowledge production within the field of business research is accelerating at a tremendous speed while at the same time remaining fragmented and interdisciplinary. This makes it hard to keep up with state-of-the-art and to be at the forefront of research, as well as to assess the collective evidence in a particular area of business research. This is why the literature review as a research method is more relevant than ever. Traditional literature reviews often lack thoroughness and rigor and are conducted ad hoc, rather than following a specific methodology. Therefore, questions can be raised about the quality and trustworthiness of these types of reviews. This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper. It also discusses common pitfalls and how to get literature reviews published.
<b><i>Background and Objectives:</i></b> There is a substantial body of evidence assessing the effects of equine-assisted therapy on physiological and psychological aspects of individuals with disabilities. This study aimed to evaluate the physiological benefits of this alternative therapy for children with cerebral palsy (CP) by means of a systematic review and meta-analysis. <b><i>Methods:</i></b> This systematic review included all randomized and nonrandomized clinical trials of hippotherapy (HT), therapeutic horse riding (THR), and artificial saddle (AS) for the treatment of children with CP by a systematic search in Medline, Embase, Cochrane Library, and other databases up to November 2012. Articles were assessed for inclusion eligibility and quality by two independent reviewers. Any discordant case was re-reviewed and consensus was obtained after sufficient discussion. A random effects model of meta-analysis was applied to provide summary statistics for each outcome. <b><i>Results:</i></b> Seven randomized controlled trials (RCTs), 4 non-RCTs, and 7 self-controlled studies were included for quality assessment. Ten studies assessed the effect of HT, 5 evaluated THR, and 3 evaluated AS. The sample size differed from 3 to 72, and the quality ranged from low to moderate. Six studies were included in the meta-analysis, and there was a significant improvement in the 66-item Gross Motor Function Measure (GMFM-66), the GMFM-66/88 total score, and the dimension E of the GMFM. Although the asymmetry score tended to be reduced, it failed to reach statistical significance. <b><i>Conclusions:</i></b> HT, THR, and AS seem to improve the total score of the gross motor function via improvement of the walking, running, and jumping dimension. However, they are not likely to be of benefit to the symmetry of postural muscle activity. Studies included in this review lack high-quality RCTs with a sufficient number of subjects, which thus warrants further evaluations of these modalities using large-scale well-designed RCTs.
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
The RE-AIM planning and evaluation framework was conceptualized two decades ago. As one of the most frequently applied implementation frameworks, RE-AIM has now been cited in over 2,800 publications. This paper describes the application and evolution of RE-AIM as well as lessons learned from its use. RE-AIM has been applied most often in public health and health behavior change research, but increasingly in more diverse content areas and within clinical, community, and corporate settings. We discuss challenges of using RE-AIM while encouraging a more pragmatic use of key dimensions rather than comprehensive applications of all elements. Current foci of RE-AIM include increasing the emphasis on cost and adaptations to programs and expanding the use of qualitative methods to understand "how" and "why" results came about. The framework will continue to evolve to focus on contextual and explanatory factors related to RE-AIM outcomes, package RE-AIM for use by non-researchers, and integrate RE-AIM with other pragmatic and reporting frameworks.
Introduction and objective Studies on telemedicine have shown success in reducing the geographical and time obstacles incurred in the receipt of care in traditional modalities with the same or greater effectiveness; however, there are several barriers that need to be addressed in order for telemedicine technology to spread. The aim of this review is to evaluate barriers to adopting telemedicine worldwide through the analysis of published work. Methods The authors conducted a systematic literature review by extracting the data from the Cumulative Index of Nursing and Allied Health Literature (CINAHL) and PubMed (MEDLINE) research databases. The reviewers in this study analysed 30 articles (nine from CINAHL and 21 from Medline) and identified barriers found in the literature. This review followed the checklist from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009. The reviewers organized the results into one table and five figures that depict the data in different ways, organized by: barrier, country-specific barriers, organization-specific barriers, patient-specific barriers, and medical-staff and programmer-specific barriers. Results The reviewers identified 33 barriers with a frequency of 100 occurrences through the 30 articles. The study identified the issues with technically challenged staff (11%), followed by resistance to change (8%), cost (8%), reimbursement (5%), age of patient (5%), and level of education of patient (5%). All other barriers occurred at or less than 4% of the time. Discussion and conclusions Telemedicine is not yet ubiquitous, and barriers vary widely. The top barriers are technology-specific and could be overcome through training, change-management techniques, and alternating delivery by telemedicine and personal patient-to-provider interaction. The results of this study identify several barriers that could be eliminated by focused policy. Future work should evaluate policy to identify which one to lever to maximize the results.
Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.
A systematic search of the research literature from 1996 through July 2008 identified more than a thousand empirical studies of online learning. Analysts screened these studies to find those that (a) contrasted an online to a face-to-face condition, (b) measured student learning outcomes, (c) used a rigorous research design, and (d) provided adequate information to calculate an effect size. As a result of this screening, 51 independent effects were identified that could be subjected to meta-analysis. The meta-analysis found that, on average, students in online learning conditions performed better than those receiving face-to-face instruction. The difference between student outcomes for online and face-to-face classes—measured as the difference between treatment and control means, divided by the pooled standard deviation—was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. Analysts noted that these blended conditions often included additional learning time and instructional elements not received by students in control conditions. This finding suggests that the positive effects associated with blended learning should not be attributed to the media, per se. An unexpected finding was the small number of rigorous published studies contrasting online and face-to-face learning conditions for K–12 students. In light of this small corpus, caution is required in generalizing to the K–12 population because the results are derived for the most part from studies in other settings (e.g., medical training, higher education).
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