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Predicting student performance in in...

State University of New York at Albany.

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Predicting student performance in introductory computer programming courses.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting student performance in introductory computer programming courses./
作者:	Doane, William E. J.
面頁冊數:	112 p.
附註:	Source: Dissertation Abstracts International, Volume: 69-04, Section: A, page: 1197.
Contained By:	Dissertation Abstracts International69-04A.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3312236
ISBN:	9780549603498

Predicting student performance in introductory computer programming courses.
Doane, William E. J.

Predicting student performance in introductory computer programming courses. - 112 p.

Source: Dissertation Abstracts International, Volume: 69-04, Section: A, page: 1197.

Thesis (Ph.D.)--State University of New York at Albany, 2008.

For decades, computer science education researchers have sought to improve computing education by refining curricula, instructional methods, and choice of first language. Central to the task of improving computer science education is the identification of students in need of assistance, ideally as early in their academic career as possible. Until recently, no known assessment instrument offered a good predictor of student performance in introductory computer programming courses. Such an instrument, should it be created, would allow educators to identify students who would be likely to have difficulty learning to program. It would also allow instructors to design instruction intended to support those students, and to allocate instructional resources more appropriately. In 2006, researchers in the United Kingdom identified an assessment instrument that shows promise as a predictor of students' final grades in introductory computer programming courses. In that same year, researchers in Massachusetts found that the commercially available logic puzzle MasterMindRTM also showed promise as a predictor of in-class programming test scores. What connects these techniques? What makes them more successful than past assessment instruments designed to test programming potential?

ISBN: 9780549603498Subjects--Topical Terms:

626642
Computer Science.

Predicting student performance in introductory computer programming courses.
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520 $a For decades, computer science education researchers have sought to improve computing education by refining curricula, instructional methods, and choice of first language. Central to the task of improving computer science education is the identification of students in need of assistance, ideally as early in their academic career as possible. Until recently, no known assessment instrument offered a good predictor of student performance in introductory computer programming courses. Such an instrument, should it be created, would allow educators to identify students who would be likely to have difficulty learning to program. It would also allow instructors to design instruction intended to support those students, and to allocate instructional resources more appropriately. In 2006, researchers in the United Kingdom identified an assessment instrument that shows promise as a predictor of students' final grades in introductory computer programming courses. In that same year, researchers in Massachusetts found that the commercially available logic puzzle MasterMindRTM also showed promise as a predictor of in-class programming test scores. What connects these techniques? What makes them more successful than past assessment instruments designed to test programming potential?
520 $a In this study, novice programmers at the undergraduate and high school levels completed a modified version of the paper-based assessment instrument designed by U.K. researchers. Students also were asked to complete web-based tasks based on MasterMindRTM and Sudoku.
520 $a The purpose of this study was to collect information about novice computer programming students and to use that information effectively to predict their final numeric course grade in introductory computer programming courses. In order effectively to extract all of the most relevant information from the initial collected data, both model-based and algorithmic prediction methods were used in the predictive analyses. Regression trees were used, in addition to model-based multiple regression methods, to derive both generalizable and interpretable predictive results, given the available data sets.
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