The effect of multimedia instruction on achievement of college history students

The purpose of this study was to investigate the effect of multimedia instruction on achievement of college students in AMR 2010 from exploration and discovery to 1865. A non-equivalent control group design was used. The dependent variable was achievement. The independent variables were learning styles, method of instruction, and visual clarifiers (notes). The study was conducted using two history sections from Palm Beach Community College, in Boca Raton, Florida, between August and December, 1998. Data were obtained by means of placement scores, posttests, the Productivity Environmental Preference Survey (PEPS), and a researcher-developed student survey. Statistical analysis of the data was done using SPSS statistical software. Demographic variables were compared using Chi square. T tests were run on the posttests to determine the equality of variances. The posttest scores of the groups were compared using the analysis of covariance (ANCOVA) at the .05 level of significance. The first hypothesis there is a significant difference in students' learning of U.S. History when students receive multimedia instruction was supported, F (1, 52) = 16.88, p < .0005, and F = (1, 53) = 8.52, p < .005 for Tests 2 and 3, respectively. The second hypothesis there is a significant difference on the effectiveness of multimedia instruction based on students' various learning preferences was not supported. The last hypotheses there is a significant difference on students' learning of U.S. History when students whose first language is other than English and students who need remediation receive visual clarifiers were not supported. Analysis of covariance (ANCOVA) indicated no difference between the groups on Test 1, Test 2, or Test 3: F (1, 45) = .01, p < .940, F (1, 52) = .77, p < .385, and F (1, 53) =.17, p < .678, respectively, for language. Analysis of covariance (ANCOVA) indicated no significant difference on Test 1, Test 2, or Test 3, between the groups on the variable remediation: F (1, 45) = .31, p < .580, F (1, 52) = 1.44, p < .236, and F (1, 53) = .21, p < .645, respectively. ^

A computer simulation model for single -lane roundabouts

During the past three decades, the use of roundabouts has increased throughout the world due to their greater benefits in comparison with intersections controlled by traditional means. Roundabouts are often chosen because they are widely associated with low accident rates, lower construction and operating costs, and reasonable capacities and delay. ^ In the planning and design of roundabouts, special attention should be given to the movement of pedestrians and bicycles. As a result, there are several guidelines for the design of pedestrian and bicycle treatments at roundabouts that increase the safety of both pedestrians and bicyclists at existing and proposed roundabout locations. Different design guidelines have differing criteria for handling pedestrians and bicyclists at roundabout locations. Although all of the investigated guidelines provide better safety (depending on the traffic conditions at a specific location), their effects on the performance of the roundabout have not been examined yet. ^ Existing roundabout analysis software packages provide estimates of capacity and performance characteristics. This includes characteristics such as delay, queue lengths, stop rates, effects of heavy vehicles, crash frequencies, and geometric delays, as well as fuel consumption, pollutant emissions and operating costs for roundabouts. None of these software packages, however, are capable of determining the effects of various pedestrian crossing locations, nor the effect of different bicycle treatments on the performance of roundabouts. ^ The objective of this research is to develop simulation models capable of determining the effect of various pedestrian and bicycle treatments at single-lane roundabouts. To achieve this, four models were developed. The first model simulates a single-lane roundabout without bicycle and pedestrian traffic. The second model simulates a single-lane roundabout with a pedestrian crossing and mixed flow bicyclists. The third model simulates a single-lane roundabout with a combined pedestrian and bicycle crossing, while the fourth model simulates a single-lane roundabout with a pedestrian crossing and a bicycle lane at the outer perimeter of the roundabout for the bicycles. Traffic data was collected at a modern roundabout in Boca Raton, Florida. ^ The results of this effort show that installing a pedestrian crossing on the roundabout approach will have a negative impact on the entry flow, while the downstream approach will benefit from the newly created gaps by pedestrians. Also, it was concluded that a bicycle lane configuration is more beneficial for all users of the roundabout instead of the mixed flow or combined crossing. Installing the pedestrian crossing at one-car length is more beneficial for pedestrians than two- and three-car lengths. Finally, it was concluded that the effect of the pedestrian crossing on the vehicle queues diminishes as the distance between the crossing and the roundabout increases. ^

The effect of multimedia instruction on achievement of college history students

The purpose of this study was to investigate the effect of multimedia instruction on achievement of college students in AMH 2010 from exploration and discovery to1865. A non-equivalent control group design was used. The dependent variable was achievement. The independent variables were learning styles method of instruction, and visual clarifiers (notes). The study was conducted using two history sections from Palm Beach Community College, in Boca Raton, Florida, between August and December, 1998. Data were obtained by means of placement scores, posttests, the Productivity Environmental Preference Survey (PEPS), and a researcher-developed student survey. Statistical analysis of the data was done using SPSS statistical software. Demographic variables were compared using Chi square. T tests were run on the posttests to determine the equality of variances. The posttest scores of the groups were compared using the analysis of covariance (ANCOVA) at the .05 level of significance. The first hypothesis there is a significant difference in students' learning of U.S. History when students receive multimedia instruction was supported, F = (1, 52)= 688, p < .0005, and F = (1, 53) = 8.52, p < .005for Tests 2 and 3, respectively. The second hypothesis there is a significant difference on the effectiveness of multimedia instruction based on students' various learning preferences was not supported. The last hypotheses there is a significant difference on students' learning of U.S. History when students whose first language is other than English and students who need remediation receive visual clarifiers were not supported. Analysis of covariance (ANCOVA) indicated no difference between the groups on Test 1, Test 2, or Test 3: F (1, 4 5)= .01, p < .940, F (l, 52) = .77, p < .385, and F (1,53) =.17, p > .678, respectively, for language. Analysis of covariance (ANCOVA) indicated no significant difference on Test 1, Test 2, or Test 3, between the groups on the variable remediation: F (1, 45) = .31, p < .580, F (1, 52) = 1.44, p < .236, and F (1, 53) = .21, p < .645, respectively.