Does Computer Experience Increase Knowledge and Improve the Attitude of Teachers - Lab Report Example

Results Data was entered into SPSS version 12.0 for Windows. Descriptive statistics were conducted on demographic data. The study included 25 participants. The minimum number of hours for computer experience (CE) was 2 and maximum number of hours for computer experience (CE) was 35 hours (M = 10.08, SD = 8.65). The minimum number of years for teaching experience (TE) was 1.00 and maximum number of years for teaching experience (TE) was 32 (M = 12.96, SD = 10.72). The minimum “knowledge of CALL” (KOC) score was 41.7% and maximum “knowledge of CALL” (KOC) score was 91.7% (M = 67.0%, SD = 16.4%). The minimum “attitude toward CALL” (ATC) score was 2.38 and maximum “attitude toward CALL” (ATC) score was 4.69 (M = 3.81, SD = .72). This study was specifically done to find out whether more teaching experience (TE) and more computer experience (CE) had an impact on attitude towards CALL (ATC) and knowledge of CALL (CALL), which is normally expected. It was found that there was no linear relationship between attitude toward CALL (ATC) and computer experience(CE). No linear relationship was found between attitude towards CALL (ATC) and teaching experience (TE). The linear regression was done to find out whether teaching experience (TE) and computer experience (CE) were predictors to attitude towards CALL (ATC). It was found that that TE and CE were not predictors to ATC. Pearson’s correlation proved that there was no linear relationship between knowledge of CALL (KOC) and computer experience (CE). The linear regression done for computer experience (CE) and teaching experience (TE) and KOC proved that CE and TE were not predictors for KOC. The expectation of finding better attitude towards CALL and better knowledge of CALL in teachers with more teaching experience and more computer experience was proven wrong. PLAN The study has used different measures to reach to the correct conclusion. It has taken two independent variables and two dependent variables. The number of participants was taken as 25 as it has 80% power to find the effect of a larger size. There were 6 hypothesises, out of which 4 were done using Pearson’s correlation coefficient. Two hypothesis were done using multiple linear regression. Validation A panel of 3-5 knowledgeable people in the field of technology in education were consulted. They were made to review the Attitude and Knowledge questions for “face validity” and check whether or not the scale scores are valid for measuring what they are intended to measure. Hence the study was done with reliable and validated methods. Hypothesis I A Pearson’s correlation was conducted to test the research hypothesis that there is no linear relationship between “attitude toward CALL” (ATC) score and computer experience (CE) to. There was no linear relationship between ATC and CE, r(23) = .169, p = .421, and thus the research hypothesis was supported. Hypothesis II A Pearson’s correlation was conducted to test the research hypothesis that there is no linear relationship between “attitude toward CALL” (ATC) score and teaching experience (TE). There was no linear relationship between ATC and TE, r(23) = -.016, p = .938, and thus the research hypothesis was supported. Hypothesis III A linear regression was conducted to examine whether TE predicted ATC after controlling for CE. CE accounted for 2.8% of the variance in ATC, and the model was not statistically significant, F (1, 23) =.067, p =.421. When TE was entered into the equation, the amount of variance accounted for increased by 0.3% and the model (with both predictors) were still not statistically significant, F (2, 22) = 0.35, p = .711. Table 1 presents the coefficient table where neither CE nor TE were significant predictors. Hypothesis IV A Pearson’s correlation was conducted to test the research hypothesis that there is no linear relationship between “knowledge of CALL” (KOC) and computer experience (CE). There was no linear relationship between KOC and CE, r(23) = .259, p = .211, and thus the research hypothesis is supported. Hypothesis V A Pearson’s correlation was conducted to test the research hypothesis that there is no linear relationship between “knowledge of CALL” (KOC) and teaching experience (TE). There was no linear relationship between KOC and TE, r(23) = .014, p = .947, and thus the research hypothesis is supported. Hypothesis VI A linear regression was conducted to examine whether TE predicted KOC after controlling for CE. CE accounted for 6.7% of the variance in KOC, and the model was not statistically significant, F (1, 23) = 1.659, p = .211. When TE was entered into the equation, the amount of variance accounted for increased by 1.3% and the model (with both predictors) were still not statistically significant, F ( 2, 22) = 0.957, p = .399. Table 2 presents the coefficient table where neither CE nor TE were significant predictors. Conclusion This study was done to find out whether computer experience increases knowledge and improves attitude of teachers. It seems logical that more experience will have positive impact but over-exposure can lead to negative impact also. This was proved by the study that there was no direct correlation between computer experience and attitude towards CALL. Similarly, it looks obvious that more teaching experience should means more knowledge and better attitude. But the practical side could mean more experience as older and demotivated teachers. This again came up in the study that more teaching experience is not showing improvement in attitude or knowledge. It used two independent variables – computer experience(CE) and teaching experience(TE) and two dependent variables- attitude towards call(ATC) and knowledge of CALL(KOC). The stastical analysis was done by using SPSS for windows. For the study six hypothesis were taken into consideration. From these six, four were tested using Pearson’s correlation coefficient. Two hypothesis used multiple linear regression. A sample size of 25 was taken as it has 80% power to a large effect size. First hypothesis was - whether there is direct correlation between ATC and CE and it was found out that there was no direct correlation between ATC and CE. The second hypothesis was to find out whether there is direct correlation between ATC and TE, this also came out as no direct correlation between ATC and TE. The third hypothesis was to see if the amount of CE is controlled, can it predict ATC. It came out that it could not predict ATC. No. four hypothesis was - is there any correlation between KOC and CE, no correlation could be established. Fifth hypothesis was - is there any correlation between KOC and TE, again this also could not be established. The last hypothesis was to see if the amount of CE is controlled, can it predict KOC, it could not be predicted. Table 1 Coefficients for Teaching Experience Predicting ATC after Controlling for Computer Experience Model B S.E. ß t Sig. CE .016 .019 .186 .829 .416 TE .003 .015 .050 .221 .827 Table 2 Coefficients for Teaching Experience Predicting KOC after Controlling for Computer Experience Model B S.E. ß t Sig. CE .573 .415 .302 1.382 .181 TE .185 .334 .121 .553 .586 Read More