Human beings are almost infinitely diverse

Human beings are almost infinitely diverse Analyzing the learners and the context of learning is important for many reasons. Although nearly everyone knows of the basic advice that one should œknow the audience when giving a speech or teaching a lesson, the ISD process stresses a systematic look at what characteristics of the audience are most relevant. What do you look at? Almost any characteristic of either the learners or the setting that could affect how you teach and how your learners learn might be part of your analysis here. This includes such learner characteristics as prior knowledge and skills, attitudes, and so on. It also might include characteristics of the environment like group pressures or the physical layout of rooms. You need to decide what will be important for your project. When you are doing your learner analysis, there are two key questions. First, which characteristics of your learners are relevant. Second, what do we do with that information? What characteristics of learners are relevant? Human beings are almost infinitely diverse. They differ in overall intelligence as well as in specific abilities. Some have more background knowledge than others. Some have skills that other have yet to learn. Various researchers have divided people into different categories based on their learning styles, from reflective/impulsive to field dependent/field independent to visual/verbal/tactile and so on. Some learners are older; some younger. People differ in their motivation levels and foci as well as in their interests and the extent to which they care about different incentives, such as grades. Any particular group of learners (the target population) could differ from other groups along these dimension as well as along many others. In addition, target populations can differ in the range you find within the population on any of these dimensions. For example, if we look at engineering students as opposed to English students, we might expect to find that the engineering students are better in math: higher aptitude, more knowledge, more skills. We would also expect that the engineering students would be less variable on these traits. After all, there is nothing that would necessarily prevent an English major from taking math courses or otherwise being good in the subject. However, all engineering students, by definition, are going to be selected on the basis of their math aptitudes and backgrounds. Since people have so many different characteristics, when we do a learner analysis we must be selective. Fortunately, many human characteristics simply do not seem to have much to do with teaching and learning. Whether a student has red or blond hair probably has little relationship to how they learn mathematics best (if you disagree, show me the research!). If you are teaching someone how to be a dental assistant, then you probably want to know about their ability to learn, their manual dexterity, even their squeamishness about working inside someone else’s mouth rather than about their hair color, number of freckles, or many other traits. It is important in our learner analyses that we concentrate on characteristics that make a difference for teaching and learning. Dick, Carey, and Carey list eight different categories of learner characteristics. You may not be able to identify important traits about your learners in all eight categories, but you should at least try. Can you think of other categories as well? What do we do with the information? The real question that needs to be asked here is œSo What? Just because we know our audience (or our environment) it does not follow that our teaching automatically changes. We have to know what to do with the information we have gathered. This is the purpose of the œImplications column in the table you are to complete for this assignment. Sometimes the implications of the our knowledge of learners and context is clear. If we have an audience that does not read well (or at all), then we probably have to find ways of conveying essential information that do not rely on text. Populations with high math anxiety need instructional and motivational strategies that help alleviate and bypass those fears. If your population already has certain key entry behaviors, then the implication is likely to be that you can skip teaching them. Other implications are not so clear. What do we do with different learning styles, for example? The research is very ambiguous on how to make good use of such information. Overall, it has been very difficult to find consistent œaptitude-treatment interactions that clearly lead to teaching different populations in different ways. One conclusion, however, is clear: There is no point in spending a lot of time œknowing the audience if that knowledge has no practical consequences for how we design and deliver the instruction. Context Analysis Parallel to Learner Analysis is Context Analysis. The context of the instruction is the environment or setting in which it will take place. This can include the ph ysical environment, the social setting, the institutional context, and so forth. This can be divided into the Performance Context and the Learning Context. Depenn your situation, these may overlap to the point where they are virtually identical. Similar to: TO ORDER FOR THIS QUESTION OR A SIMILAR ONE, CLICK THE ORDER NOW BUTTON AND ON THE ORDER FORM, FILL ALL THE REQUIRED DETAILS THEN TRACE THE DISCOUNT CODE, TYPE IT ON THE DISCOUNT BOX AND CLICK ON ˜USE CODE’ TO EFFECT YOUR DISCOUNT. THANK YOU

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