Students often face difficulties while taking basic programming courses due to several factors. In response, research has presented subjective assessments for diagnosing learning problems to improve the teaching of pr...Students often face difficulties while taking basic programming courses due to several factors. In response, research has presented subjective assessments for diagnosing learning problems to improve the teaching of programming in higher education. In this paper, the authors propose an Object Oriented conceptual map model and organize this approach into three levels： constructing a Concept Effect Propagation Table, constructing Test Item-Concept Relationships and diagnosing Student Learning Problems with Matrix Composition. The authors＇ work is a modification of the approaches of Chert and Bai as well as Chu et al., as the authors use statistical methods, rather than fuzzy sets, for the authors＇ analysis. This paper includes a statistical summary, which has been tested on a small sample of students in King Abdulaziz University, Jeddah, Saudi Arabia, illustrating the learning problems in an Object Oriented course. The experimental results have demonstrated that this approach might aid learning and teaching in an effective way.展开更多
Concept index （CI） is a very fast and efficient feature extraction （FE） algorithm for text classification. The key approach in CI scheme is to express each document as a function of various concepts （centroids） ...Concept index （CI） is a very fast and efficient feature extraction （FE） algorithm for text classification. The key approach in CI scheme is to express each document as a function of various concepts （centroids） present in the collection. However,the representative ability of centroids for categorizing corpus is often influenced by so-called model misfit caused by a number of factors in the FE process including feature selection to similarity measure. In order to address this issue, this work employs the ＂DragPushing＂ Strategy to refine the centroids that are used for concept index. We present an extensive experimental evaluation of refined concept index （RCI） on two English collections and one Chinese corpus using state-of-the-art Support Vector Machine （SVM） classifier. The results indicate that in each case, RCI-based SVM yields a much better performance than the normal CI-based SVM but lower computation cost during training and classification phases.展开更多
文摘Students often face difficulties while taking basic programming courses due to several factors. In response, research has presented subjective assessments for diagnosing learning problems to improve the teaching of programming in higher education. In this paper, the authors propose an Object Oriented conceptual map model and organize this approach into three levels： constructing a Concept Effect Propagation Table, constructing Test Item-Concept Relationships and diagnosing Student Learning Problems with Matrix Composition. The authors＇ work is a modification of the approaches of Chert and Bai as well as Chu et al., as the authors use statistical methods, rather than fuzzy sets, for the authors＇ analysis. This paper includes a statistical summary, which has been tested on a small sample of students in King Abdulaziz University, Jeddah, Saudi Arabia, illustrating the learning problems in an Object Oriented course. The experimental results have demonstrated that this approach might aid learning and teaching in an effective way.
文摘Concept index （CI） is a very fast and efficient feature extraction （FE） algorithm for text classification. The key approach in CI scheme is to express each document as a function of various concepts （centroids） present in the collection. However,the representative ability of centroids for categorizing corpus is often influenced by so-called model misfit caused by a number of factors in the FE process including feature selection to similarity measure. In order to address this issue, this work employs the ＂DragPushing＂ Strategy to refine the centroids that are used for concept index. We present an extensive experimental evaluation of refined concept index （RCI） on two English collections and one Chinese corpus using state-of-the-art Support Vector Machine （SVM） classifier. The results indicate that in each case, RCI-based SVM yields a much better performance than the normal CI-based SVM but lower computation cost during training and classification phases.
