DocumentCode
2283103
Title
Notice of Retraction
A Graded Task-driven Methodology for Computer Science Education
Author
Yihong Dong
Author_Institution
Dept. of Comput. Sci., Ningbo Univ., Ningbo, China
Volume
3
fYear
2010
fDate
6-7 March 2010
Firstpage
654
Lastpage
656
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In traditional task-driven methodology the tasks devised by the teachers are the same for every student. However, because of different ability of professional competencies of the students, some students with strong professional qualities will achieve the tasks perfectly, while others with poor professional abilities are at a loss what to do. In this paper, a graded task-driven methodology is proposed. Recognizing the different of the students´ abilities, we devise several kinds of tasks for the students to choose. The tasks are graded by its difficulty level, so every student can find the task that fit for him/her. Several years´ practice in the courses of ??Artificial Neural Network?? and ??Data Mining?? shows that this innovation is successful.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In traditional task-driven methodology the tasks devised by the teachers are the same for every student. However, because of different ability of professional competencies of the students, some students with strong professional qualities will achieve the tasks perfectly, while others with poor professional abilities are at a loss what to do. In this paper, a graded task-driven methodology is proposed. Recognizing the different of the students´ abilities, we devise several kinds of tasks for the students to choose. The tasks are graded by its difficulty level, so every student can find the task that fit for him/her. Several years´ practice in the courses of ??Artificial Neural Network?? and ??Data Mining?? shows that this innovation is successful.
Keywords
computer science education; teacher training; artificial neural network; computer science education; data mining; graded task-driven methodology; professional competencies; students abilities; teachers; Artificial neural networks; Collaboration; Computer science; Computer science education; Data mining; Educational institutions; Educational technology; Information science; Organizing; Technological innovation; computer science education; graded; task-driven;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.333
Filename
5458897
Link To Document