Learning gains in web programming course with automatic code generator program using a structured flowchart
Following normalized gain concept, including class average normalized gain, single student normalized gain, single test item normalized gain, and conceptual dimensional normalized gain. The aim of this
Learning gains in web programming course with automatic code generator program using a structured flowchart
Following normalized gain concept, including class average normalized gain, single student normalized gain, single test item normalized gain, and conceptual dimensional normalized gain. The aim of this research was to assess learning gain for the computer programming course using automatic code generator using a structured flowchart (CGF tool). Moreover, the research aims to compare the understanding of the programming course between the experimental group and the control group. Data were collected from 58 students enrolled in web programming course. The data were collected through pre-test and post-test and then analyzed with descriptive statistics and inferential statistics using t-test and normalized gain. The findings, notably revealed that 1) Post-test score average of an experimental group was significantly higher than a control group at the level of 0.05. 2) Class average normalized gain of the experimental group was in the medium gain <g> = 0.59 and control group was in the low gain <g> = 0.23. 3) Single student normalized gain results showed that 3.1) an experimental group was in the high gain (39.02 percent), medium gain (46.35 percent) and low gain (14.63 percent). 3.2) a control group was in the high gain (5.88 percent), medium gain (29.41 percent) and low gain (64.71 percent). 4) Single test item normalized gain was in the high gain (3 items), medium gain (6 items) and low gain (1 item). 5) Conceptual dimensional normalized gain was in the high gain (1 concept) and medium gain (3 concepts).
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