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Influence of pre and post testing on return on investment calculations in training and development.
When expenses become an issue, training is often one of the first budget items to be cut. There have been a number of evaluation studies about rates of return from training interventions. Most results are based on interviewing participants about the value of the intervention and its effect on their productivity. This often results in quadruple digit return on investment indications. Decision makers who control the budget often view these kinds of results with skepticism. This study proposes a methodology to evaluate training interventions without asking participants their opinions. The process involves measuring learning through a series of pre-tests and post-tests and determining if scores on pre-tests can be used as predictors of future return on investment results. The study evaluates a series of return on investment scores using analysis of variance to determine the relationship between pre-tests and final return on investment results for each participant. Data is also collected and evaluated to determine if the financial results of the organization during the period of the training intervention could be correlated to the results of the training intervention. The results of the study suggest that the proposed methodology can be used to predict future return on investment from training interventions based on the use of pre-tests. These rates of return can be used as a method of selecting between competing training intervention proposals. It is a process that is easily understood by the key decision makers who control the allocation of financial resources. More importantly, it is a process that can maximize the value of each dollar spent on training.
Instructor immediacy and presence in the online learning environment: An investigation of relationships with student affective learning, cognition, and motivation.
Bivariate correlation was used to examine possible relationships between instructor immediacy and instructor presence, and a statistically significant correlation was found. Multiple linear regression analysis was used to determine whether the linear combination of instructor immediacy and presence caused significant variance in student affective learning, cognition, and motivation. For all three of the latter dependent variables, the linear combination of instructor immediacy and presence was found to cause statistically significant variance. However, although the overall regression models were significant in all three tests, instructor immediacy was not found to be a significant individual predictor for causing variance in affective learning, cognition, or motivation, whereas instructor presence was found to be a significant individual predictor of all three. Finally, factorial ANOVA revealed that, for perceptions of instructor immediacy, only classification and course type were found to explain significant variance, with undergraduate students in asynchronous courses reporting significantly lower instructor immediacy. For perceptions of instructor presence, graduate students tended to rate their instructors as having higher presence than did undergraduate students, and students in synchronous courses tended to rate their instructors as having higher presence than did students in asynchronous courses.
The relationship between computer use and academic achievements.
Computer technology has been used in education for years, and the government budgets large amounts of money to foster technology. However, it is still a debated whether computer technology makes a difference in students' learning outcomes. The purpose of this study is to find if any relationship exists between computer use by teachers and students and the students' academic achievement in math and reading for both traditional populations and English language learner (ELL) tenth graders. Computer use in this study included the computer activities by students and teachers, in terms of the time, frequency, activities types, the places students use computers, teachers' computer activities, and the training teachers received. This study used data gathered from tenth grade students from the dataset Education Longitudinal Study of 2002 (ELS:2002) of the National Center for Education Statistics (NCES). Fifteen thousand, three hundred and sixty-two students were randomly selected to represent all U.S. tenth-graders attending schools in 2002. The findings showed diverse relationships consistent with the literature. Based on the findings, some suggestions were made to teachers and parents about the quality of school work and computer use by students and teachers.
Using SERVQUAL to Measure Users' Satisfaction of Computer Support in Higher Educational Environments
The purpose of this research was to measure users' satisfaction with computer support in the higher education environment. The data for this study were gathered over a 5-week period using an online survey. Subjects (N=180) were members of a college at a major Texas university, which included both faculty and staff. SERVQUAL was the instrument used in this study. Two-ways statistical ANOVA analyses were conducted and revealed three statistically significant differences for Gender, Classification, and Comfort Level.
Relationships between perceptions of personal ownership of laptop computers and attitudes toward school.
The feeling of ownership is a topic of research that has not been addressed as a component in the integration of technology in the K-12 classroom. The effectiveness of this abstract concept in relationship to digital computing is important in the evaluation of one-to-one initiatives in education. This paper reports findings of a research study conducted using a new ownership survey instrument I developed, the Laptop Usage Inventory (LUI). Also administered during the study was the Student Attitude Survey given in a pretest/posttest design. The instruments were administered to seventh and eighth grade students in a north Texas middle school in the 2007-2008 school year. The methodology used to evaluate the Laptop Usage Inventory consisted of Cronbach's alpha and various scaling methods. LUI scale scores were correlated with the results of the Student Attitude Survey to compare students' attitudes toward school before and after using a laptop computer for the school year. Implications for laptop initiatives and for the classroom are discussed and a future research agenda is presented.
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