# Perception versus Reality Determining Business Students Computer Literacy Skills and Need for Instruction in Information Concepts and Technology

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6 Perception versus Reality Likewise, the results of the Excel pretest, which also was administered prior to any course instruction in Excel, revealed a mean of Following course instruction in Excel, the students posttest revealed a mean of 82.4 with a standard deviation of 8.8. In the Excel testing, students showed an increase of 23 points as a result of course instruction. As noted by Table 1, the Excel and Concepts pretest means are both below 60%, indicating an unacceptable level of prerequisite knowledge in these areas. After instruction, scores improved by an average of 21 points in Computer Concepts and 23 points in Excel as shown in Table 1. Table 1: Excel and Concepts Test Results Means and Standard Deviations Excel Concepts Pretest Posttest Pretest Posttest Mean SD Examination of pretest to posttest performance (see Figure 1) indicates that students across all initial performance levels benefited from instruction, though the lower performing students benefited more Excel Posttest Concept Posttest Excel Pretest Concept Pretest Figure 1: Trend Line Comparison of Pretest and Posttest Performance on the Excel and Concepts Test Note: The diagonal line would indicate no change from pretest to posttest. The test data were analyzed as a 2 (Excel test and Concepts test) X 2 (Pretest and Posttest) repeated measures analysis of variance (ANOVA). The posttest scores were significantly greater than the pretest scores and the Excel scores were significantly greater than the concepts scores (see Table 2). 146

7 Wallace & Clariana Table 2: ANOVA Results Source SS df MS F Sig. PRETEST_POSTTEST error EXCEL_CONCEPTS error PRE_POST * EX_CON error The interaction of pretest/posttest and excel/concepts was also significant (p = ). This interaction is shown in Figure 2, indicating that Excel scores increased more from pretest to posttest than did the Concept scores Score (100%) Excel Concepts 50 Pretest Posttest Figure 2: Graph of the Significant Interaction of Excel and Concepts Scores from Pretest to Posttest As noted in Table 3, the correlation relationships between these tests is also revealing. The Excel and Concepts pretests were most related (r =.72) but were less related after instruction (r = 0.50). 147

8 Perception versus Reality Table 3: Pearson Correlation of Tests EX EX CON CON PRE POST PRE POST Excel pretest 1 Excel Posttest Concepts Pretest Concepts posttest Note: All significance at p < 0.01 Data Analysis of Students with Below Passing Grades Another interpretation of the data can be made by analyzing the student data by removing from the data analysis the students that would have tested out of the course. To test out of the course, a student would have to obtain a score of at least 60 percent on both the Excel and Computer Concepts tests. Therefore, in this study, 64 percent (89 of 140) of the students failed, or scored below 60 percent, one of the two tests. However, the percentage that failed both tests was 39 percent (54 of 140). Consequently, the data from this study revealed that only 36 percent (50 of 140) of the students tested could exempt or test out of the course if given that option. Table 4 below reveals the means of the 89 students that obtained below passing scores on one or both tests and, thus, would not be eligible to exempt or test out of the course. Table 4: Means of Students Scoring below 60 Percent (n = 89) Excel Concepts pretest (11.2) (8.2) posttest (8.7) (8.6) gain Of particular note in Table 4 is the mean gain or improvement of 26 points in the Excel post test and 23 points in the Computer Concepts post test of the 89 students. In addition, the data revealed that following instruction, only one student failed the excel posttest and only two students failed the computer concepts posttest. This data indicates that course instruction was effective for the majority of students and achieved the desired effect of providing meaningful knowledge in information technology concepts and skills. 148

11 Wallace & Clariana Biographies Patricia Wallace teaches courses in Information Systems and Management as an Associate Professor in the School of Business at The College of New Jersey (TCNJ). She earned her doctorate from Temple University and completed post-doctoral study in Management Science and Information Systems at Penn State University. Roy Clariana is an Associate Professor in the Instructional Systems program in the College of Education at the Pennsylvania State University. He earned his doctorate from Memphis State University. 151

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