Academic literature on the topic 'Modèle Doi Edwards'

Students of Elementary School Teacher Education programs must be able to have higher-order thinking skills (HOTS) so that they can train students to have HOTS through learning activities created when they have become elementary school teachers. This study aims to explain students' high-level thinking skills in solving HOTS-oriented questions in Instructional Evaluation courses. This study uses qualitative research methods with data collection techniques using cognitive test instruments in the form of descriptions. Data analysis techniques use simple descriptive statistics. The results showed the level of thinking ability of students in answering HOTS practice questions still needed improvement. Students who have high learning abilities are better at answering HOTS-oriented questions compared to students in the medium and low categories. Recommendations for future research are required learning modules that can facilitate learning activities that lead to HOTS so that students are skilled in answering and making HOTS-oriented practice questions for elementary school students when they become a teacher. References Abdullah, Abdul Halim; Mokhtar, Mahani; Halim, Noor Dayana Abd; Ali, Dayana Farzeeha; Tahir, Lokman Mohd; Kohar, U. H. A. (2017). Mathematics Teachers’ Level of Knowledge and Practice on the Implementation of Higher-Order Thinking Skills (HOTS). EURASIA Journal of Mathematics, Science and Technology Education, 13(1), 3–17. https://doi.org/10.12973/eurasia.2017.00601a Altun, M., & Akkaya, R. (2014). Mathematics teachers’ comments on PISA math questions and our country’s students’ low achievement levels. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 29(1), 19–34. Bakry, & Md Nor Bakar. (2015). The process of thinking among Junior High School students in solving HOTS question. International Journal of Evaluation and Research in Education (IJERE), 4(3), 138–145. Budsankom, P; Sawangboon, T; Damrongpanit, S; Chuensirimongkol, J. (2015). Factors affecting higher order thinking skills of students: A meta-analytic structural equation modeling study. Educational Research and Review, 10(19), 2639–2652. doi:10.5897/err2015.2371 Chinedu, C. C., Olabiyi, O. S., & Kamin, Y. Bin. (2015). Strategies for improving higher order thinking skills in teaching and learning of design and technology education. Journal of Technical Educationand Training, 7(2), 35–43. Retrieved from http://penerbit.uthm.edu.my/ojs/index.php/JTET/article/view/1081/795 Didis, M. G., Erbas, A. K., Cetinkaya, B., Cakiroglu, E., & Alacaci, C. (2016). Exploring prospective secondary mathematics teachers’ interpretation of student thinking through analysing students’work in modelling. Mathematics Education Research Journal, 28(3), 349–378. https://doi.org/10.1007/s13394-016-0170-6 Duan, J. (2012). Research about Technology Enhanced Higher-Order Thinking. IEEE Computer Society, (Iccse), 687–689. https://doi.org/10.1109/ICCSE.2012.6295167 Edwards, L. (2016). EDUCATION, TECHNOLOGY AND HIGHER ORDER THINKING SKILLS Lucy Edwards, 1–18. Ersoy, E., & Başer, N. (2014). The Effects of Problem-based Learning Method in Higher Education on Creative Thinking. Procedia - Social and Behavioral Sciences, 116, 3494–3498. https://doi.org/10.1016/j.sbspro.2014.01.790 Hugerat, M., & Kortam, N. (2014). Improving higher order thinking skills among freshmen by teaching science through inquiry. Eurasia Journal of Mathematics, Science and Technology Education, 10(5), 447–454. https://doi.org/10.12973/eurasia.2014.1107a Kaur, C., Singh, S., Kaur, R., Singh, A., & Singh, T. S. M. (2018). Developing a Higher Order Thinking Skills Module for Weak ESL Learners, 11(7), 86–100. https://doi.org/10.5539/elt.v11n7p86 King, F. J., Goodson, L., & Rohani, F. (1998). Higher order thinking skills. Publication of the Educational Services Program, Now Known as the Center for Advancement of Learning and Assessment. Obtido de: Www.Cala.Fsu.Edu, 1–176. Retrieved from http://www.cala.fsu.edu/files/higher_order_thinking_skills.pdf Kusuma, M. D., Rosidin, U., Abdurrahman, A., & Suyatna, A. (2017). The Development of Higher Order Thinking Skill (Hots) Instrument Assessment In Physics Study. IOSR Journal of Research & Method in Education (IOSRJRME), 07(01), 26–32. https://doi.org/10.9790/7388-0701052632 Marzano, R. J. (1993). How classroom teachers approach the teaching of thinking. Theory Into Practice, 32(3), 154–160. https://doi.org/10.1080/00405849309543591 McLoughlin, D., & Mynard, J. (2009). An analysis of higher order thinking in online discussions. Innovations in Education and Teaching International, 46(2), 147–160. https://doi.org/10.1080/14703290902843778 Miri, B., David, B. C., & Uri, Z. (2007). Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking. Research in Science Education, 37(4), 353–369. https://doi.org/10.1007/s11165-006-9029-2 Nagappan, R. (2001). Language teaching and the enhancement of higher-order thinking skills. Anthology Series-Seameo Regional Language Centre, (April 2000), 190–223. Retrieved from http://nsrajendran.tripod.com/Papers/RELC2000A.pdf Nguyen, T. (2018). Teachers ’ Capacity of Instruction for Developing Higher – Order Thinking Skills for Upper Secondary Students – A Case Study in Teaching Mathematics in Vietnam, 10(1), 8–19. Puchta, H. (2007). More than little parrots: Developing young learners’ speaking skills. Www.Herbertpuchta.Com. Raiyn, J., & Tilchin, O. (2015). Higher-Order Thinking Development through Adaptive Problem-based Learning. Journal of Education and Training Studies, 3(4), 93–100. https://doi.org/10.11114/jets.v3i4.769 Retnawati, H., Djidu, H., Kartianom, K., Apino, E., & Anazifa, R. D. (2018). Teachers’ knowledge about higher-order thinking skills and its learning strategy. Problem of Education in the 21st Century, 76(2), 215–230. Retrieved from http://oaji.net/articles/2017/457-1524597598.pdf Snyder, L. G., & Snyder, M. J. (2008). Teaching critical thinking and problem solving skills. The Delta Pi Epsilon Journal, L(2), 90–99. https://doi.org/10.1023/A:1009682924511 Stahnke, R., Schueler, S., & Roesken-Winter, B. (2016). Teachers’ perception, interpretation, and decision-making: a systematic review of empirical mathematics education research. ZDM - Mathematics Education, 48(1–2). https://doi.org/10.1007/s11858-016-0775-y Sulaiman, T., Muniyan, V., Madhvan, D., Hasan, R., & Rahim, S. S. A. (2017). Implementation of higher order thinking skills in teaching of science: A case study in Malaysia. International Research Journal of Education and Sciences (IRJES), 1(1), 2550–2158. Retrieved from http://www.masree.info/wp-content/uploads/2017/02/20170226-IRJES-VOL-1-ISSUE-1-ARTICLE-1.pdf Tan, S. Y., & Halili, S. H. (2015). Effective teaching of higher-order thinking (HOT) in education. The Online Journal of Distance Education and E-Learning, 3(2), 41–47. Thomas, A., & Thorne, G. (2009). How to increase higher level thinking | center for development and learning. The Center for Learning and Development Blog. Retrieved from http://www.cdl.org/articles/how-to-increase-high-order-thinking/ Thompson, T. (2008). Mathematics teachers’ interpretation of higher-order thinking in Bloom’s taxonomy. International Electronic Journal of Mathematics Education, 3(2), 96–109. https://doi.org/10.1126/science.318.5856.1534 Watson, J. M., Collis, K. F., Callingham, R. A., & Moritz, J. B. (1995). A model for assessing higher order thinking in statistics. Educational Research and Evaluation,(Vol.1). https://doi.org/10.1080/1380361950010303 Zohar, A. (2013). Challenges in wide scale implementation efforts to foster higher order thinking (HOT) in science education across a whole school system. Thinking Skills and Creativity, 10, 233–249. https://doi.org/10.1016/j.tsc.2013.06.002 Zohar, A., & Schwartzer, N. (2005). Assessing teachers’ pedagogical knowledge in the context of teaching higher-order thinking. International Journal of Science Education, 27(13), 1595–1620. https://doi.org/10.1080/09500690500186592 Zulkpli, Z., Mohamed, M., & Abdullah, A. H. (2017). Assessing mathematics teachers’ knowledge in teaching thinking skills. Sains Humanika, 9(1–4), 83–87. https://doi.org/10.11113/sh.v9n1-4.1129

Why did such highly abstract ideas as truth, knowledge, or justice become so important to us? What was the point of coming to think in these terms? In The Practical Origins of Ideas, Matthieu Queloz presents a philosophical method designed to answer such questions: the method of pragmatic genealogy. Pragmatic genealogies are partly fictional, partly historical narratives exploring what might have driven us to develop certain ideas in order to discover what these do for us. The book uncovers an under-appreciated tradition of pragmatic genealogy which cuts across the analytic–continental divide, running from the state-of-nature stories of David Hume and the early genealogies of Friedrich Nietzsche to recent work in analytic philosophy by Edward Craig, Bernard Williams, and Miranda Fricker. However, these genealogies combine fictionalizing and historicizing in ways that even philosophers sympathetic to the use of state-of-nature fictions or real history have found puzzling. To make sense of why both fictionalizing and historicizing are called for, the book offers a systematic account of pragmatic genealogies as dynamic models serving to reverse-engineer the points of ideas in relation not only to near-universal human needs, but also to socio-historically situated needs. This allows the method to offer us explanation without reduction and to help us understand what led our ideas to shed the traces of their practical origins. Far from being normatively inert, moreover, pragmatic genealogy can affect the space of reasons, guiding attempts to improve our conceptual repertoire by helping us determine whether and when our ideas are worth having.

In this work, the model of Doi-Edwards with independant alignment approximation describing the dynamics of polymer melts is simulated. The main aim of this work is the analysis of some new simulation techniques operating on the Fokker-Planck equation related to that model. For this purpose we consider the kinetic theory description of the Doi-Edwards model, implemented in the 2D and 3D cases under shear and elongational flows. The Fokker Planck equation which governs the evolution of the distribution function involves two variables: the tube orientation (described by a unit vector defining the unit surface in 3D and the unit circle in 2D) and the coordinate that locates the segment tube on the molecular chain, taking values in the unit interval. To separate both variables during the problem resolution we make use of the Alternating Direction Implicit method (ADI) which allows reducing the computation time and efforts. A model reduction technique is also proposed and analyzed. It consists of considering an optimal representation basis which is constructed during the problem resolution. Thus, a reduced number of approximation functions, now defined in the whole domain, are enough to describe the solution evolution during the entire time interval considered in the simulation, with significant CPU time savings.

The Horn Mountain Production Spar was installed in 5,400 feet of water in June 2002. This was the deepest floating production unit at that time. A comprehensive instrumentation program was initiated to measure spar and riser responses (Edwards et al, DOT 2003). The present paper discusses the results of these measurements and comparison with analytical predictions of spar behavior during two selected events, hurricane Isidore in September 2002 and a summer storm in August 2003. Particular attention has been placed on the slowly varying surge and pitch motions and the importance of coupling with risers and mooring on hull motions. Our conclusion is that uncoupled analytical models for spar behavior predict accurately the wave frequency responses, however riser coupling has an influence on the slowly varying responses. This conclusion is consistent with earlier measurements of classic spar behavior (Gupta et al, OTC 2000, Prislin et al, OTC 1999).

The Horn Mountain Production Spar was installed in 5,400 feet of water in June 2002. This was the deepest floating production unit at that time. A comprehensive instrumentation program was initiated to measure spar and riser responses (Edwards et al, DOT 2003), while motion comparisons were presented on previous publication (Halkyard et al, OMAE 2004). The present paper discusses the results of these measurements and compares with analytical predictions of spar mooring tension during hurricane Isidore in September 2002. Particular attention has been placed on the importance of Coulomb friction between wire chain and the fairlead bearing to the dynamic tension of mooring lines. Mooring tensions were measured at chain jack location (inboard tension), while analytical models computed those tensions at the fairlead location (outboard tension). Our conclusion is that there is excellent agreement between field measurements and computed tensions at the chain jacks when fairlead friction is included, and when the vessel motions are accurately predicted. Ignoring fairlead friction results in a slightly conservative estimate for the tension at the chain jack. This has been the standard practice in all spar designs to date.

The United States Department of Defense (DoD) is continually looking for ways to improve test and evaluation techniques to ensure systems meet military requirements prior to acquisition. Recently, the DoD has been pursuing the use of statistical methods to improve test and evaluation. This paper highlights statistical methodologies used by the Air Force Test Center to improve aircraft propulsion system Modeling and Simulation (M&S) efforts. The US Air Force has a long history of using M&S (more than 55 years) during aircraft test and evaluation. In the past, M&S usage was primarily in the aircraft performance and flying qualities areas. Now advancing technology and complex integration are resulting in increased M&S use across broader spectrum of technical disciplines, including propulsion. During propulsion testing, models are used to increase system knowledge in T&E areas which include: Test Planning, Execution, Data Analysis and Evaluation. This paper highlights the 412 Test Wing at Edwards AFB first steps to improve aircraft propulsion system T&E through the implementation of statistically defensible model development techniques. Specifically, this paper will provide an example of typical engineer model development strategies based on past experience, system knowledge, relevant physics and subjective evaluations to determine variables used and structure of the model. This paper will also provide insight into a number of statistics-based approaches including stepwise regression, backwards elimination, the inadequacy of using R-squared and an examination into the effects of mulit-collinearity. However, the focus of this paper is on how Information Theory and Akaike’s Information Criteria (AIC) can be easily applied to compare a variety of models and determine the best model available. This paper presents an example of these model development methods applied during a development of a predictive model used for evaluating thrust response of an aircraft engine with a new digital engine control. A case will be made that statistical approaches provide a more mathematically rigorous approach for model selection as compared to traditional approaches based on engineering judgment.

ABSTRACT Background: In school environments, the No Smoking Area Policy is based on protecting young people who are currently studying at school from exposure to harmful cigarette smoke. It is expected to indirectly reduce student smoking rates. This study aimed to describe the implementation of the No Smoking Area policy at High School 2 Nganjuk, East Java and to identify the factors influencing the policy implementation. Subjects and Methods: This was a qualitative study carried out at High School 2 Nganjuk, East Java. The study subjects were included the principal, student deputy principals, counseling guidance teachers, homeroom teachers, employees, and students of High School 2 Nganjuk. Data were collected using observation, interviews, and documentation. The source triangulation technique used the technique of checking the validity of the data. This study used an interactive model of data analysis technique, which is based on the theory of George C. Edward III, consisted of communication, resources, dispositions, and bureaucratic structures. Results: In High School 2 Nganjuk, the No Smoking Area Policy has not been implemented optimally, particularly on the resource factor. For example, some teachers and staff still smoking in schools. This was not in accordance with the provisions in the No Smoking Area Policy. As the budget for funds from School Operational Assistance (BOS) was integrated with the School Environment Introduction Period (MPLS/MOS as well as the Adiwiyata program, there was no special budget for the implementation of the No Smoking Area policy. There was still not enough amount of billboards about no smoking area. The communication factor was the supporting factor. The policy for the No Smoking Area is always communicated to school residents. The disposition factors was the policy implementers’ engagement. The bureaucratic factor were structure and the presence of SOP in policy implementation. Conclusion: The enforcement of the policy of the No Smoking Area in High School 2 Nganjuk has not been maximized, so all factors, both contact factors, resource factors, disposal factors, and bureaucratic factors, need to be assisted. Keywords: smoking area, high school, policy Correspondence: Yeny Kusumawati. School of Health Sciences, Satria Bhakti Nganjuk, East Java, Indonesia. Email: yenykusumawati.sbn.ngk@gmail.com. Mobile: 082244297997 DOI: https://doi.org/10.26911/the7thicph.04.01