For the most part, questions involving mathematical content did require the use or application of some form of mathematical work and as such we categorised these questions as being ‘maths explicit’. However, we found a few instances of questions which had an undoubted reliance on mathematical information or a mathematical idea but which did not necessarily involve doing much with the **mathematics**. In such cases we recorded the question as ‘maths not explicit’. Under ‘demand’ we categorised questions according to content demand rather than cognitive demand. The categories of Standard (S), Underlined (U) and Bold (B) refer to the content categories used in the reformed GCSE **Mathematics** (DfE, 2013) which we have used as the reference point for this work. The newly reformed GCSE **Mathematics** will be examined for the first time in 2017. As such, the first cohorts of students who will take the reformed **science** A- levels, under consideration here, will not have taken this particular qualification. However, it will be the new GCSE **Mathematics** qualification going forward. The remaining demand

Show more
20 Read more

The Pre–professional studies track is actually more of an advising track than a major. At the university–level, pre–professional stu- dents normally choose a specific major such as biology or chem- istry. Students planning to seek certification as secondary **school** **science** teachers should take SPAN 1115 in the General Education Elective category. He or she should then take SPAN 1215 as an additional elective beyond the required hours for the A.S. degree. Required Hours ........................................................ 62-66

On the other hand, the content knowledge of teachers toward professional development has a positive effect on teachers' beliefs and self-efficacy for teaching [12]. Also, the teachers who have high self-efficacy perception create a better classroom environment and aid their students to improve literacy skills ([13]; [14]; [15]). Pajares and Kranzler [9] stated that **mathematics** self-efficacy had a significant effect on **mathematics** anxiety and problem solving performance. Hackett and Betz [16] found out that **mathematics** self-efficacy was significantly positively correlated with attitudes toward **mathematics** and effectance motivation. Dede [3] reported that **mathematics** teachers’ levels of self-efficacy for effective teaching of **mathematics** and of self-efficacy for teaching are higher than levels of self-efficacy for helping and motivating students towards **mathematics**. Peker [17] concluded that **mathematics** teaching anxiety had a significant effect on pre-service primary **school** teachers’ levels of self-efficacy for **mathematics** teaching. Similarly, primary **school** **science** teaching is one area, in which, however, low level of teacher efficacy has long been a problem [18]. That is why **science** teachers are expected to have high levels of self-efficacy for **science** teaching. According to Yaman, Cansüngü-Koray and Altunçekiç [19], **science** teachers with higher levels of self-efficacy successfully tend to use student-centered approaches, spend more time teaching **science** and bring inquiry-based teaching methods to their classrooms while those with lower levels of self-efficacy prefer teacher-centered methods of instruction, such as lecture.

Show more
10 Read more

Students in this study also reported frequent use of the iPads to access edmodo, an online classroom website, designed to allow communication with teachers and peers, collaboration opportunities, assessment options and a digital platform for sharing resources. The educational website uses a social network format designed to be appropriate for the classroom. Students can share ideas with peers or teachers and receive feedback on their work through teacher-monitored posts. They are able to collaborate on group assignments outside of the classroom through the website as well as turn in assignments to allow for a more paperless learning environment. Heinrich’s (2012) study of a middle **school** one-to-one iPad initiative found students and teachers felt the program was positively impacting the learning and teaching in the **school** through its abilities to be used for communication among peers and teachers, to work more efficiently, create and deliver presentations, and share resources. When considering the device itself, iPads have been shown to increase collaboration and communication at the university level (Fisher et al., 2013). Fisher discovered the devices were able to change the classroom workspace into one that promoted the sharing of ideas as students were incorporating their iPads into almost all interactions with other students. Van Dusen and Otero (2012) also found iPads in the high **school** **science** classroom promoted collaboration and engagement. The iPads were used to assist students in their construction of knowledge, created excitement for learning that went beyond the class time, and promoted responsibility for their own learning.

Show more
19 Read more

Scholars have consistently reported that secondary **school** academic performance is highly pre- dictive of university performance (Harackiwicz, Barron, Tauer, & Elliot, 2002; McKenzie & Schweitzer, 2001; Nicholas, Poladian, Mack, & Wilson, 2015). In the Australian system, stu- dents with high Australian Tertiary Admissions Ranking (ATAR) scores out-perform students with lower scores (Everett & Robins, 1991) at university. For example, a positive correlation between ATAR scores and university academic performance has been observed across first year (Messinis & Sheehan, 2015), primary education (Wright, 2015), and health **science** (Hine et al., 2015) degrees. The use of a Grade Point Average (GPA) to calculate and report university performance is common practice, and across a wealth of studies, GPA has been used as a con- trolled covariate to gain insights into other relationships (Bacon & Bean, 2006). Specifically, research findings suggest that competency in university **mathematics** courses (Poladian & Nicholas, 2013; Rylands & Coady, 2009) and university **science** courses (Armstrong, Fielding, Kirk, & Ramagge, 2012; Nicholas et al., 2015; Sadler & Tai, 2007) depend on the level of **mathematics** studied at secondary **school**. For instance, Hine et al. (2015) found considerable differences within a cohort of first-year university students enrolled in a health sciences degree. Irrespective of gender, it was determined that those students who had studied a more difficult **mathematics** pathway at secondary **school** had a significantly higher GPA than those who had taken an easier **mathematics** pathway. Similarly, research conducted by both Anderton et al. (2016) and Green et al. (2009) concluded that particular secondary **school** **science** courses are associated with academic performance of first-year university students enrolled in bioscience degrees. However, it is unknown the extent to which specific **mathematics** and **science** courses influence specific university courses, in particular when addressing ATAR and GPA perfor- mance, and based on the level of **mathematics** and **science** subjects completed at secondary **school**.

Show more
14 Read more

The finding that these trends are observed among prospective teachers is deeply concerning. In fact, among students with offers to study ITE there was also a halving of the proportions studying intermediate and advanced maths – trends not seen in other cohorts. The increasingly low levels of mathematical study amongst prospective teachers have the potential to create an internal cycle of diminishing maths and **science** in schools; as the teachers who are currently exiting are replaced by teachers whose knowledge in maths is lower. Such a cycle can lead to a society with an insufficient knowledge base in maths and **science**. This knowledge base forms the foundations for technological and economic development and is required to maintain the current standard of living. Levels of participation and attainment in **mathematics** and **science** education must be lifted if Australia is to compete with international economies whose benchmarks in education already surpass our current standards. If participation rates continue to fall we are committing future generations to a decline in educational and economic standards and to an ever reducing capacity to redress them. It is indeed a slippery slope.

Show more
14 Read more

For a study of **school** **mathematics** it is essential to establish a consistent definition for achievement and to establish what counts as a significant achievement result. Guskey (2013) broadly defines achievement as the accomplishment of **mathematics** learning goals. It is associated with specific curricular targets or aptitudes yet ubiquitously represented as aggregate scores; summaries of students’ item-level test responses. These characteristics are common in a great many papers that are reliant on achievement results. Type of instrument used to collect data, however, still allows for interpretive latitude. Achievement is variously defined as scores derived from teacher-generated classroom tests, standardized cross-sectional tests, and standardized longitudinal tests (e.g., Hyde et al., 2008; Hedges & Nowell, 1995; Lubienski et al., 2013; Voyer & Voyer, 2014). It is also encountered as scores derived from standardized aptitude tests and scores derived from standardized curriculum-based tests (Brochu et al., 2013; Mullis, Martin, Foy, & Arora, 2012). About the only reliable characteristic of studies in this respect seems to be a lack of discussion about what achievement is or how different instruments influence interpretations about achievement and, ultimately, claims about mathematical ability. In the current paper, therefore, achievement is defined as aggregate scores associated with students’ responses to standardized curriculum-based tests. This is a reasonable provisional definition that is consistent with many published gender and **mathematics** ability research studies. What counts as significant achievement results, meanwhile, is more a matter of convention. The disparate between similarities and differences in ability claims arguably rests on the relative importance afforded to Cohen’s

Show more
20 Read more

The underrepresentation of women in **science**, technology, engineering and **mathematics** (STEM)-related fields remains a concern for educators and the scientific community. Gender differences in **mathematics** and **science** achievement play a role, in conjunction with attitudes and self-efficacy beliefs. We report results from the 2011 Trends in **Mathematics** and **Science** Study (TIMSS), a large international assessment of eighth grade students’ achievement, attitudes and beliefs among 45 participating nations (N = 261,738). Small to medium sized gender differences were found for most individual nations (from d = -.60 to +.31 in **mathematics** achievement, and d = -.60 to +.26 for **science** achievement), although the direction varied and there were no global gender differences overall. Such a pattern cross-culturally is incompatible with the notion of immutable gender differences. Additionally, there were different patterns between OECD and non- OECD nations, with girls scoring higher than boys in **mathematics** and **science** achievement across non-OECD nations. An association was found between gender differences in **science** achievement and national levels of gender equality, providing support for the gender segregation hypothesis. Furthermore, the performance of boys was more variable than that of girls in most nations, consistent with the greater male variability hypothesis. Boys reported more favorable attitudes towards **mathematics** and **science** and girls reported lower self-efficacy beliefs. While the gender gap in STEM achievement may be closing, there are still large sections of the world where differences remain.

Show more
24 Read more

The Robert Morris University **School** of Engineering, **Mathematics** and **Science** will help you to prepare yourself to take on additional responsibilities in your organization. By adding value to your professional training, you will increase the odds of career advancement in your favor. It is especially important for engineers to consider their future roles as they take on management-level positions. We can help you to develop the skills needed for business success, communication excellence and the practice of lifelong learning.

12 Read more

In an ever-changing society, it is essential that all learners passing through the primary **school** acquire a functioning knowledge of **Mathematics** that empowers them to make sense of society (Department of Education, 2003:09). A suitable range of Mathematical process skills and knowledge enables an appreciation of the discipline itself. It also ensures access to an extended study of the Mathematical Sciences and a variety of career paths. **Mathematics** is therefore necessary for any learner who intends to pursue a career in the Physical, Mathematical, Computer, Life, Earth, Space and Environmental Sciences or in Technology. The study of **Mathematics** contributes to personal development through a deeper understanding and successful application of its knowledge and skills, while maintaining appropriate values and attitudes (Department of Education, 2003:09). Therefore, Mathematical competence provides access to rewarding activity and contributes to personal, social, scientific and economic development. **Mathematics** enables learners to communicate appropriately by using descriptions in words, graphs, symbols, tables and diagrams, use mathematical process skills to identify, pose and solve problems creatively and critically, organise, interpret and manage authentic activities in substantial mathematical ways that demonstrate responsibility and sensitivity to personal and broader societal concerns, work collaboratively in teams and groups to enhance mathematical understanding, collect, analyse and organise quantitative data to evaluate and critique conclusions and engage responsibly with quantitative arguments relating to local, national and global issues (Department of Education, 2003:09).

Show more
124 Read more

The hierarchical analyses for this chapter were conducted in two stages. In the first stage the analyses quantified across countries the extent to which schools differ in the average achievement of their students, and the extent to which these differences may be due to the home background of the student body. This information provides an overview of the global relationship between home background, schooling, and student achievement, and was helpful in identifying the countries that would be most fruitful for further study. This infor- mation also shows the extent to which schools internationally are seg- regated by home background factors, by describing how much they vary in the home background composition of the student body. The second, more detailed, analyses explored the relationship of student, teacher, and **school** factors to average **school** achievement, while adjusting for characteristics of the students’ home background. This stage involved constructing seven hierarchical models for both **science** and **mathematics** in each of the countries included in the analyses. The analyses reported in this chapter required a valid measure of the socioeconomic and educational background of the students. To that end, a single composite index of home background was created from variables considered to relate to this construct, and found to relate to each other and to student achievement. The home background index was based upon students’ reports on the following: • number of books in the home

Show more
160 Read more

How to increase female access to education has been a global concern. Male-Female enrolment ratio has become a major educational development thrust. A country is considered to be educational developed if a greater percentage of the **school**-age female populations are enrolled in schools. The female participation rates in most of the developing nations are low, while both female dropout and female repetition rates are very high. This is due to low academic achievement of the female students, which is as a result of constraining institutional and societal factors (Owolabi & Fabunmi, 1999; Colclough & Lewin, 1993; Brock & Cammish, 1991; Tietjen & Prather, 1991).

Show more
13 Read more

The main work of the thesis was commenced in chapter three, wherein weight space techniques were developed to educe finitedimensional, irreducible, typical and atypical, star and grade s[r]

203 Read more

linear programming matrices clustering numerical methods Courses Algorithms Artificial Intelligence Robotics Graphics Vision randomization performance analysis queueing theory scheduling[r]

32 Read more

The test will be marked and discussed with you and you will be given a mark of 0, 1, 2, 3 or 4. The main purpose of the test is to discover whether you have sufficient knowledge to proceed to the next Module. The discussion with the Marker is the key feedback in this module. It provides you with an opportunity to obtain help with any difficulties you may have. If you are given a 3 or 4, you have passed. Then take the marked test back to the Administrator who will record your mark. The marked test will be returned to you; please keep it for the whole semester; it will be useful when you revise for the exam and, in some rare cases, you may be asked to show it once more for administrative reasons. If you are given a mark of 0, 1 or 2 you have not passed, the Administrator will record your mark and you will be asked to return on a different day to take a different test. The Marker may also suggest that you should attend the **Mathematics** Workshop, see Section 8. A maximum of 2 attempts is allowed for each Module, but only if you get a 0, 1 or 2 the first time around: you don’t get a second go if you achieved a 3. The marks awarded for each test (the higher of the 2 marks if you retake a test) count towards your final coursework mark for the course. You may want to check the correct entry of your marks in the Grade Center of the respective Blackboard page, see http://blackboard.soton.ac.uk. If you find any error, please show the corresponding marked test to the Administrator at your next visit to the testing room.

Show more
10 Read more

ACTS 6306 Advanced Actuarial Applications (3 semester hours) Special topics in actuarial **science** will be discussed. This class covers parts of CAS Exam 5 (Basic Techniques for Ratemaking and Estimating Claim Liabilities)/SOA Exam FAP (Fundamentals of Actuarial Practice). Prerequisite: Instructor consent required. (3-‐0) R

Primary and basic **Mathematics** and **Science** education has to be a phase of joyful learning for the child with ample opportunities for exploration of the environment, to interact with it and to talk about it. The main objectives at this stage are to arouse curiosity about the world (natural environment, artifacts and people) and have the child engage in exploratory and hands-on activities that lead to the development of basic cognitive and psychomotor skills through language, observation, recording, differentiation, classification, inference, drawing, illustrations, design and fabrication, estimation and measurement. The curriculum should also help the child internalize the values of cleanliness, honesty, co- operation, concern for life and environment. At the primary stage, children are actively developing their language skills – speaking, reading and writing, which is important to articulate their thoughts and develop the framework for observing the world. This is the stage, therefore, to emphasize on simple concepts.

Show more
Aims of the initiative (launched 2006) included helping schools build on their curricular strengths and share good practice: 17 schools were designated as specialist schools in **science**, technology and **mathematics** An ETI evaluation found that performance in the specialism was good to

The development of inhibition and the control of interference has long been established as a central limiting factor in cognitive development 7, 40 . Children have the capacity to make inhibitory responses from infancy, but only gradually get better at using this ability 41 . During interference control, children show more diffuse frontal cortex activations and a greater recruitment of posterior brain regions; adults by contrast show more focal activation in the DLPFC, ACC and inferior frontal gyrus 42, 43 . Similarly, neuroimaging evidence with children shows a shift from posterior perceptual processing regions to fronto-parietal activations correlating with age and improved performance on logic and mathematical problems 44, 45 . This has been interpreted as showing that children need to inhibit initial perceptually bound beliefs before being able to successfully apply the more abstract and (frontally dependent) reasoning skills required in math and logic. Convincing evidence of this shift was presented in a recent meta-analysis of functional magnetic resonance imaging (fMRI) data obtained over a decade (1999–2008) on more than 800 children and adolescents engaged in numerical tasks. This analysis revealed that, unlike adults, children primarily engage the frontal cortex when solving numerical tasks. This is consistent with the argument that, with increasing age, there is a shift from a reliance on the frontal cortex to reliance on the parietal cortex in mathematical reasoning tasks 46 , perhaps due to reduced cognitive load as children gradually acquire expertise in **mathematics**. Though it should be noted that this conclusion relies on the reference inference that because frontal regions are more active, greater inhibitory control is being exerted. Given the prolonged development of the frontal lobes 43 it is not possible to be entirely sure that functions observed in the developing brain are identical to those observed in the mature adult brain, even if the activation patterns are similar.

Show more
15 Read more