Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees

Abstract

Abstract-The assessment of students' metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, we built a fuzzy inference system (FIS) with the extracted rules as a rule base and classified the test dataset with the FIS. The extracted rules are evaluated using measures such as the overall efficiency and Kappa coefficient.

Multispectral Image Analysis using Decision Trees

“…We classified all samples from the training set data using the FIS and compared predicted and actual categories. Similar approach was proposed by Taylor et al [27] for extracting knowledge from MARSI dataset. The work presented in this paper differs from the earlier work in [23], [26] in two aspects a) we have considered the problem of overfitting and b) we have validated extracted rules using the FIS.…”