Wbs Chart Pro Crack 4.9a
Sheron Norsworthy
A teacher may wish to pair SE 4.9.A with SE 4.9.F and assess both SEs at the same time. With SE 4.9.A, students demonstrate knowledge of distinguishing characteristics of well-known children's literature such as folktales, fables, legends, myths, and tall tales. Place students in small groups and task them with writing a folktale. Assign each group a different mode of delivery. Examples might include creating an audio recording of students reading the folktale, a live theatrical performance, a song, an electronic document using hyperlinks and features of the software, or a video recording depicting the folktale. As groups share their folktales with the class, ask them to identify the genre-specific characteristics that are present in the folktale. Then, have students discuss the different types of modalities used to present folktales.
Students should be able to identify and explain the distinguishing characteristics of multimodal texts such as the combination of writing, sound, still images, and moving images within the presentation of a folktale. Be sure to point out how groups used more than one mode of delivery. It may be helpful to create a class anchor chart listing each modality and the characteristics that define the modality.
Summary: Researchers investigated the effects of digital storytelling in improving the writing skills of third grade students in rural primary schools. Students' writing performance was measured before and after the teaching. Results of this digital storytelling process showed learners' progress in word choice, fluency, and writing quality; students also showed improved interactions and increased motivation to write. Researchers determined that the opportunity for digital storytelling allowed students to create meaning through multimodal texts, comprehend the nature of multiform texts, and develop their technology, information, and visual literacies.
Summary: The article argues for the increasing need for schools to support conversational skills in the digital age and provides ways to build opportunities for social communication in the classroom.
Summary: The author examines the value of students' classroom discussion for oral language development. As a collaborative activity, students were required to adapt an assigned story into a multimodal format, which encouraged a "wide range of immediate, complex, and unplanned oral language" discussions as students had to "express views, justify ideas, negotiate, evaluate and collaborate to produce their planned oral scripts."
A graph (used here interchangeably with chart) displays numeric data in visual form. It can display patterns, trends, aberrations, similarities, and differences in the data that may not be evident in tables. As such, a graph can be an essential tool for analyzing and trying to make sense of data. In addition, a graph is often an effective way to present data to others less familiar with the data.
In epidemiology, most graphs have two scales or axes, one horizontal and one vertical, that intersect at a right angle. The horizontal axis is known as the x-axis and generally shows values of the independent (or x) variable, such as time or age group. The vertical axis is the y-axis and shows the dependent (or y) variable, which, in epidemiology, is usually a frequency measure such as number of cases or rate of disease. Each axis should be labeled to show what it represents (both the name of the variable and the units in which it is measured) and marked by a scale of measurement along the line.
In constructing a useful graph, the guidelines for categorizing data for tables by types of data also apply. For example, the number of reported measles cases by year of report is technically a nominal variable, but because of the large number of cases when aggregated over the United States, we can treat this variable as a continuous one. As such, a line graph is appropriate to display these data.
Scenario: Table 4.14 shows the number of measles cases by year of report from 1950 to 2003. The number of measles cases in years 1950 through 1954 has been plotted in Figure 4.1, below. The independent variable, years, is shown on the horizontal axis. The dependent variable, number of cases, is shown on the vertical axis. A grid is included in Figure 4.1 to illustrate how points are plotted. For example, to plot the point on the graph for the number of cases in 1953, draw a line up from 1953, and then draw a line from 449 cases to the right. The point where these lines intersect is the point for 1953 on the graph.
An arithmetic-scale line graph (such as Figure 4.1) shows patterns or trends over some variable, often time. In epidemiology, this type of graph is used to show long series of data and to compare several series. It is the method of choice for plotting rates over time.
Furthermore, the distance between any two tick marks on the x-axis (horizontal axis) represents a period of time of one year. This represents an example of a discrete variable. Thus an arithmetic-scale line graph is one in which equal distances along either the x- or y- axis portray equal values.
Arithmetic-scale line graphs can display numbers, rates, proportions, or other quantitative measures on the y-axis. Generally, the x-axis for these graphs is used to portray the time period of data occurrence, collection, or reporting (e.g., days, weeks, months, or years). Thus, these graphs are primarily used to portray an overall trend over time, rather than an analysis of particular observations (single data points). For example, Figure 4.2 shows prevalence (of neural tube defects) per 100,000 births.
When you create an arithmetic-scale line graph, you need to select a scale for the x- and y-axes. The scale should reflect both the data and the point of the graph. For example, if you use the data in Table 4.14 to graph the number of cases of measles cases by year from 1990 to 2002, then the scale of the x-axis will most likely be year of report, because that is how the data are available. Consider, however, if you had line-listed data with the actual dates of onset or report that spanned several years. You might prefer to plot these data by week, month, quarter, or even year, depending on the point you wish to make.
In some cases, the range of data observed may be so large that proper construction of an arithmetic-scale graph is problematic. For example, in the United States, vaccination policies have greatly reduced the incidence of mumps; however, outbreaks can still occur in unvaccinated populations. To portray these competing forces, an arithmetic graph is insufficient without an inset amplifying the problem years (Figure 4.4).
Another use for the semi-log graph is when you are interested in portraying the relative rate of change of several series, rather than the absolute value. Figure 4.5 shows this application. Note several aspects of this graph:
A histogram is a graph of the frequency distribution of a continuous variable, based on class intervals. It uses adjoining columns to represent the number of observations for each class interval in the distribution. The area of each column is proportional to the number of observations in that interval. Figures 4.7a and 4.7b show two versions of a histogram of frequency distributions with equal class intervals. Since all class intervals are equal in this histogram, the eight of each column is in proportion to the number of observations it depicts.
Using the botulism data presented in Exercise 4.1, draw an epidemic curve. Then use this epidemic curve to describe this outbreak as if you were speaking over the telephone to someone who cannot see the graph. Graph paper is provided at the end of this lesson.
A frequency polygon, like a histogram, is the graph of a frequency distribution. In a frequency polygon, the number of observations within an interval is marked with a single point placed at the midpoint of the interval. Each point is then connected to the next with a straight line. Figure 4.13 shows an example of a frequency polygon over the outline of a histogram for the same data. This graph makes it easy to identify the peak of the epidemic (4 weeks).
As its name implies, a cumulative frequency curve plots the cumulative frequency rather than the actual frequency distribution of a variable. This type of graph is useful for identifying medians, quartiles, and other percentiles. The x-axis records the class intervals, while the y-axis shows the cumulative frequency either on an absolute scale (e.g., number of cases) or, more commonly, as percentages from 0% to 100%. The median (50% or half-way point) can be found by drawing a horizontal line from the 50% tick mark on the y-axis to the cumulative frequency curve, then drawing a vertical line from that spot down to the x-axis. Figure 4.16 is a cumulative frequency graph showing the number of days until smallpox vaccination scab separation among persons who had never received smallpox vaccination previously (primary vaccinees) and among persons who had been previously vaccinated (revaccinees). The median number of days until scab separation was 19 days among revaccinees, and 22 days among primary vaccinees.
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Aims: The study aims to assess the effects of switching from National Center for Health Statistics (NCHS) growth references to World Health Organization (WHO) growth standards on health-care workers' decisions about malnutrition in infants aged
Methods: We conducted a single blind randomised crossover trial involving 78 health-care workers (doctors, clinical officers, health service assistants) in Southern Malawi. Participants were offered hypothetical clinical scenarios with the same infant plotted on NCHS-based weight-for-age charts and again on WHO-based charts. Additional scenarios compared growth charts with a single final weight against charts with the same final weight plus a preceding growth trend. Reported (i) level of concern, (ii) referral suggestions and (iii) feeding advice were elicited with a questionnaire.
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