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. ^

Sugar and coffee: A history of settler agriculture in nineteenth-century Liberia

This dissertation is about commercial agriculture in nineteenth-century Liberia. Based primarily on the archives of the American Colonization Society (founder of Liberia), it examines the impact of environmental and demographic constraints on an agrarian settler society from 1822 to the 1890s. Contrary to the standard interpretation, which linked the poor state of commercial agriculture to the settlers' disdain for cultivation, this dissertation argues that the scarcity of labor and capital impeded the growth of commercial agriculture. The causes of the scarcity were high mortality, low immigration and the poverty of the American “Negroes” who began to settle Liberia in 1822. ^ Emigration to Liberia meant almost certain death and affliction for many immigrants because they encountered a new set of diseases. Mortality was particularly high during the early decades of colonization. From 1822 to 1843, about 48 percent of all immigrants died of various causes, usually within their first year. The bulk of the deaths is attributed to malaria. There was no natural increase in the population for this early period and because American “Negroes” were unenthusiastic about relocation to Liberia, immigration remained sparse throughout the century. Low immigration, combined with the high death rate, deprived the fledgling colony of its potential human resource, especially for the cultivation of labor-intensive crops, like sugar cane and coffee. Moreover, even though females constituted approximately half of the settlers, they seldom performed agricultural labor. ^ The problem of labor was compounded by the scarcity of draft animals. Liberia is in the region where trypanosomiasis occurs. The disease is fatal to large livestock. Therefore, animal-drawn plows, common in the United States, were never successfully transplanted in Liberia. Besides, the dearth of livestock obstructed the development of the sugar industry since many planters depended on oxen-powered mills because they could not afford to buy the more expensive steam engine mills. ^ Finally, nearly half of the immigrants were newly emancipated slaves. Usually these former bondsmen arrived in Liberia penniless. Consequently, they lacked the capital to invest in large-scale plantations. The other categories of immigrants (e.g., those who purchased their freedom), were hardly better off than the emancipated slaves. ^

Molecular markers and human history: A tale of two haplogroup systems

To chronicle demographic movement across African Asian corridors, a variety of molecular (sequence analysis, restriction mapping and denaturing high performance liquid chromatography etc.) and statistical (correspondence analysis, AMOVA, calculation of diversity indices and phylogenetic inference, etc.) techniques were employed to assess the phylogeographic patterns of mtDNA control region and Y chromosomal variation among 14 sub-Saharan, North African and Middle Eastern populations. The patterns of genetic diversity revealed evidence of multiple migrations across several African Asian passageways as well within the African continent itself. The two-part analysis uncovered several interesting results which include the following: (1) a north (Egypt and Middle East Asia) to south (sub-Saharan Africa) partitioning of both mtDNA and Y chromosomal haplogroup diversity, (2) a genetic diversity gradient in sub-Saharan Africa from east to west, (3) evidence in favor of the Levantine Corridor over the Horn of Africa as the major genetic conduit since the Last Glacial Maximum, (4) a substantially higher mtDNA versus Y chromosomal sub-Saharan component in the Middle East collections, (5) a higher representation of East versus West African mtDNA haplotypes in the Arabian Peninsula populations versus no such bias in the Levant groups and lastly, (6) genetic remnants of the Bantu demographic expansion in sub-Saharan Africa. ^

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.