Algorithms for clustering data
Algorithms for clustering data
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Objective teaching refers to letting learners meet a preset goal over a certain time frame, and for this purpose it is essential to find suitable teachers, or a combination of teachers most appropriate for the task. This paper uses clustering and decision tree technologies to solve the problem of how to select teachers and students for objective teaching. First, students are classified into clusters according to their characteristics that are determined based on their pretest performance in English skill areas, such as listening, reading, and writing. Second, several classes of each skill area are available for the students freely to choose from, each class already assigned a teacher. After the course of instruction is completed, an analysis is made of the posttest results in order to evaluate both teaching and learning performances. Then, the participant teachers who have taught particularly well in a certain skill area are thus selected and recommended as appropriate for different clusters of students who have failed the posttest. This study, with the teaching experiment having turned out with highly encouraging results, not only proposes a new method of how to achieve objective teaching, but also presents a prototype system to demonstrate the effectiveness of this method.