An appropriate terminal behavior Essays

An appropriate terminal behavior Essays An appropriate terminal behavior Paper An appropriate terminal behavior Paper You are giving tennis lessons to a beginning tennis player. Describe how you would teach the proper tennis swing through shaping.  Specify:  a. An appropriate terminal behavior  b. A reinforcer you might use  c. The specific steps you would take during shaping  d. When you would use continuous reinforcement  e. When you would use intermittent reinforcement  A.  An appropriate terminal behavior would be to tell the student that he or she must make contact with the ball, hit it over the net and into the other side of the tennis court. A reinforcer I might use would be to praise the student if he or she succeeded, or to offer to buy the student lunch or ice cream if he or she succeeded a certain amount of times. During shaping, I would first reinforce any attempt the student made that came relatively close to the goal of hitting the ball. Secondly, I would reinforce a response that more closely approximates the terminal behavior-such as hitting the ball over the net. Thirdly, I would reinforce a response that resembles the terminal behavior even more closely-for example, if the student hit the ball over the net and came close to hitting it in. I would continue to reinforce closer and closer approximations to the terminal behavior until I was finally reinforcing only the terminal behavior of hitting the ball over the net and in. I would use continuous reinforcement at first, when the student needed positive thinking the most, in order to help he or she improve more quickly. I would reinforce the student for simply trying. However, after he or she gets the hang of hitting the ball over the net and in, I would only intermittently reinforce him or her when he or she performed this terminal behavior, and not reinforce him or her every time he or she tried to hit the ball. Above and beyond, it is important for students not just to use models in their science teaching but also gain knowledge about the nature and purpose of scientific models (Grosslight et al, 1991, Van Driel and Verloop, 1999). Moreover, learning to model should be a social procedure that involves discussion and negotiation of meaning, because this provides the best opportunity for each student to construct the desired knowledge (Harrison and Treagust, 1998). However, as a research suggests (Grosslight et al., 1991), students should have more experience in using models as tools for learning and experience with discussions underlying the role of models in scientific concepts. Wilensky and Reisman (2006) highlight the need for further experience with models in science education by saying that all students seek to understand science and the world around them. Besides, when students manage to accomplish modeling skills they can use them in novel situations in the domain of instruction (White, 1993, White and Frederiksen, 1990). The use of models in science education requires great effort and there are difficulties that not only students but also teachers need to overcome, in order to achieve meaningful and efficient use of modeling. Teaching students about models and modeling has proven a quite challenging and difficult task (Schwartz White, 2005). However, research showed that neither students nor their teachers possess efficient knowledge about the nature and purpose of scientific models (Van Driel Verloop, 1999). Consequently, some students fail to understand the purpose of engaging with the modeling process (Barrowy Roberts, 1999) and they also might not realize the nature of models or modeling, even if they are engaged in creating and revising models (Carey and Smith, 1993; Grosslight et al., 1991). On the other hand research has shown (Louca Constantinou, 2002) that learning about models and modeling can be accomplished in early middle school ages by guiding students through a process of developing and refining models about natural phenomena. Therefore teachers role in teaching science through an efficient and successful modeling approach is important. Teachers should develop their knowledge in teaching scientific concepts and achieve self-efficacy in teaching and as Bandura (1981) argues self-efficacy can be enhanced through modeling. Similarly, Enochs et al. (1995) support that in order for elementary teachers to achieve self-confidence, well planned and modeled based lessons are required. Also, when students are building models and using their own analogies, instead of those of teachers, will be more benefited (Harrison and Treagust, 1998) and this is due to the fact that students analogies are more familiar and easier to understand (Zook, 1991). On the other hand, students find it difficult to select appropriate analogies, so they expect from the teacher to give an analogy or a model, even if they have difficulties in mapping it (Harrison and Treagust, 1998). Moreover, some difficulties that students find when trying to construct meaning in science are due to the fact that they dont have efficient ability and knowledge in developing conceptual models of physical phenomena (Golin, 1997). Consequently, teachers should use analogies and models in their teaching through an approach that involves focus, action and reflection (Treagust et al., 1998). Also, considering the importance of hand-on lessons, primary teachers should continuously improve their teaching methods especially in the area of hands-on activity planning (Dickinson et al, 1997). Modeling teaching practices can be an appropriate and useful tool, since they promote teaching though practical demonstrations (Hudson, No date). Though, some times models that are used in physics only demonstrate the end product of physics to students (Steinberg, 2000), something that can limit students critical thinking and take from them the opportunity to observe and find out new phenomena by themselves.

Example of Two Sample T Test and Confidence Interval

Example of Two Sample T Test and Confidence Interval Sometimes in statistics, it is helpful to see worked out examples of problems.   These examples can help us in figuring out similar problems.   In this article, we will walk through the process of conducting inferential statistics for a result concerning two population means. Not only will we see how to conduct a hypothesis test about the difference of two population means, we will also construct a confidence interval for this difference.   The methods that we use are sometimes called a two sample t test and a two sample t confidence interval. The Statement of the Problem Suppose we wish to test the mathematical aptitude of grade school children.   One question that we may have is if higher grade levels have higher mean test scores. A simple random sample of 27 third graders is given a math test, their answers are scored, and the results are found to have a mean score of 75 points with a sample standard deviation of 3 points. A simple random sample of 20 fifth graders is given the same math test and their answers are scored. The mean score for the fifth graders is 84 points with a sample standard deviation of 5 points. Given this scenario we ask the following questions: Does the sample data provide us with evidence that the mean test score of the population of all fifth graders exceeds the mean test score of the population of all third graders?What is a 95% confidence interval for the difference in mean test scores between the populations of third graders and fifth graders? Conditions and Procedure We must select which procedure to use. In doing this we must make sure and check that conditions for this procedure have been met. We are asked to compare two population means. One collection of methods that can be used to do this are those for two-sample t-procedures. In order to use these t-procedures for two samples, we need to make sure that the following conditions hold: We have two simple random samples from the two populations of interest.Our simple random samples do not constitute more than 5% of the population.The two samples are independent of one another, and there is no matching between the subjects.The variable is normally distributed.Both the population mean and standard deviation are unknown for both of the populations. We see that most of these conditions are met.   We were told that we have simple random samples.   The populations that we are studying are large as there are millions of students in these grade levels. The condition that we are unable to automatically assume is if the test scores are normally distributed. Since we have a large enough sample size, by the robustness of our t-procedures we do not necessarily need the variable to be normally distributed. Since the conditions are satisfied, we perform a couple of preliminary calculations. Standard Error The standard error is an estimate of a standard deviation. For this statistic, we add the sample variance of the samples and then take the square root. This gives the formula: (s1 2 / n1 s22 / n2)1/2 By using the values above, we see that the value of the standard error is (32 / 27 52 / 20)1/2 (1 / 3 5 / 4 )1/2 1.2583 Degrees of Freedom We can use the conservative approximation for our degrees of freedom. This may underestimate the number of degrees of freedom, but it is much easier to calculate than using Welchs formula. We use the smaller of the two sample sizes, and then subtract one from this number. For our example, the smaller of the two samples is 20. This means that the number of degrees of freedom is 20 - 1 19. Hypothesis Test We wish to test the hypothesis that fifth-grade students have a mean test score that is greater than the mean score of third-grade students. Let ÃŽ ¼1 be the mean score of the population of all fifth graders. Similarly, we let ÃŽ ¼2 be the mean score of the population of all third graders. The hypotheses are as follows: H0: ÃŽ ¼1 - ÃŽ ¼2 0Ha: ÃŽ ¼1 - ÃŽ ¼2 0 The test statistic is the difference between the sample means, which is then divided by the standard error. Since we are using sample standard deviations to estimate the population standard deviation, the test statistic from the t-distribution. The value of the test statistic is (84 - 75)/1.2583. This is approximately 7.15. We now determine what the p-value is for this hypothesis test. We look at the value of the test statistic, and where this is located on a t-distribution with 19 degrees of freedom. For this distribution, we have 4.2 x 10-7 as our p-value. (One way to determine this is to use the T.DIST.RT function in Excel.) Since we have such a small p-value, we reject the null hypothesis. The conclusion is that the mean test score for fifth graders is higher than the mean test score for third graders. Confidence Interval Since we have established that there is a difference between the mean scores, we now determine a confidence interval for the difference between these two means. We already have much of what we need. The confidence interval for the difference needs to have both an estimate and a margin of error. The estimate for the difference of two means is straightforward to calculate. We simply find the difference of the sample means. This difference of the sample means estimates the difference of the population means. For our data, the difference in sample means is 84 – 75 9. The margin of error is slightly more difficult to compute. For this, we need to multiply the appropriate statistic by the standard error. The statistic that we need is found by consulting a table or statistical software. Again using the conservative approximation, we have 19 degrees of freedom. For a 95% confidence interval we see that t* 2.09. We could use the T.INV function in Excel to calculate this value. We now put everything together and see that our margin of error is 2.09 x 1.2583, which is approximately 2.63. The confidence interval is 9  Ã‚ ± 2.63. The interval is 6.37 to 11.63 points on the test that the fifth and third graders chose.