Research on the nature of science and science education enjoys a longhistory, with its origins in Ernst Mach's work in the late nineteenthcentury and John Dewey's at the beginning of the twentieth century.As early as 1909 the Central Association for Science and MathematicsTeachers published an article – ‘A Consideration of the Principles thatShould Determine the Courses in Biology in Secondary Schools’ – inSchool Science and Mathematics that reflected foundational concernsabout science and how school curricula should be informed by them. Sincethen (...) a large body of literature has developed related to the teaching andlearning about nature of science – see, for example, the Lederman and Meichtry reviews cited below. As well there has been intensephilosophical, historical and philosophical debate about the nature of scienceitself, culminating in the much-publicised ‘Science Wars’ of recent time. Thereferences listed here primarily focus on the empirical research related to thenature of science as an educational goal; along with a few influential philosophicalworks by such authors as Kuhn, Popper, Laudan, Lakatos, and others. Whilenot exhaustive, the list should prove useful to educators, and scholars in otherfields, interested in the nature of science and how its understanding can berealised as a goal of science instruction. The authors welcome correspondenceregarding omissions from the list, and on-going additions that can be made to it. (shrink)

Researchers have raised concerns about teachers’ ability to embed nature of science in their science instruction, a complicated situation that is certainly impacted by the availability of adequate resources to assist K-12 science teachers. In light of the implementation of the ideas from the Framework for K-12 Science Education and the Next Generation Science Standards in the USA, this study sought to identify and evaluate resources aimed at guiding NOS instruction. A search of the National Science Teachers Association database for (...) Next Generation Science Standards -aligned instructional resources resulted in an analytical sample of eight lessons. All materials accompanying these lessons were analyzed for their representations of 10 NOS aspects. The evaluation of these materials revealed a prevalence of implicit, naïve representations in the sample lessons. Examination of the connections to NOS in these lessons leads to a set of recommendations to improve the quantity and quality of NOS representations in future NGSS-aligned instructional resources. The analytical approach used and the issues raised about the presentation and treatment of NOS in precollege lessons are of interest to the broader science education community. (shrink)

Stemming from the realization of the importance of the role of explanation in the science classroom, the Next Generation Science Standards call for appropriately supporting students to learn science, argue from evidence, and provide explanations. Despite the ongoing emphasis on explanations in the science classroom, there seems to be no well-articulated framework that supports students in constructing adequate scientific explanations, or that helps teachers assess student explanations. Our motivation for this article is twofold: First, we think that the ways in (...) which researchers in science education have studied scientific explanation are, at best, leaves much to be desired and, at worst, simply incomplete. Second, we believe that research about the teaching and learning of explanation in science classrooms must be guided by explicit models or frameworks that specify elements involved in constructing explanations particularly applicable to science. More importantly, we think that the development of such models or guidelines should be based on theoretical and philosophical foundations. In order to develop these frameworks or guidelines, we first outline and clarify models of scientific explanation developed by philosophers of science over the last few decades. In the second section of this article, we present a more recent philosophical work on scientific explanation, the pragmatic approach to studying scientific explanations. This approach suggests a toolbox for analyzing scientists’ scientific explanations, which provides a useful instrument to science education. In Section 3, we discuss the ways by which the previous two sections are useful in developing a K-12 scientific explanation schema. Implications for future research on students’ explanations are discussed. (shrink)

Total quality; commerce; economics; religious aspects; Islam; Islamic ethics.

The novel coronavirus disease is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have (...) control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory and gated recurrent unit have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait. (shrink)

Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. (...) Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. Apache Spark, Apache Kafka, and Twitter Streaming API are used to develop the second component. The best model with the highest accuracy obtained from the first component predicts breast cancer in real time from tweets’ streaming. The results showed that the best model is the random forest classifier which achieved the best accuracy. (shrink)

Islamic ethics; Muslims; conduct of life.

Bringing together international research on nature of science representations in science textbooks, this unique analysis provides a global perspective on NOS from elementary to college level and discusses the practical implications in various regions across the globe. Contributing authors highlight the similarities and differences in NOS representations and provide recommendations for future science textbooks. This comprehensive analysis is a definitive reference work in science education.

Islamic philosophy; Muslim theology; Islam; doctrines.

Tales of good deeds in the light of Hadith.