PPPP: (Record no. 9844)

MARC details
000 -LEADER
fixed length control field 08097nam a22002537a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201210b2022 a|||f bm|| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Ayman Aboulmagd Ahmed Abdelraheem Farghaly
245 1# - TITLE STATEMENT
Title PPPP:
Remainder of title PRECOLLEGE PREDICTION OF PROGRAMMING PERFORMANCE; A TALENT IDENTIFICATION FRAMEWORK /
Statement of responsibility, etc. Ayman Aboulmagd Ahmed Abdelraheem Farghaly
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2022
300 ## - PHYSICAL DESCRIPTION
Extent 55 p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: <br/>Dr. Passent M. El-Kafrawy
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2022 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/><br/>Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii<br/>Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix<br/>List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi<br/>List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii<br/>Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix<br/>Chapters:<br/>1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2<br/>1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>1.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>2. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br/>2.1 Introduction to Cognitive Abilities . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br/>2.2 Relevant broad abilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6<br/>2.3 Definition of programming performance . . . . . . . . . . . . . . . . . . . . . . . . 7<br/>2.4 Spatial skills and STEM achievement . . . . . . . . . . . . . . . . . . . . . . . . . 8<br/>2.4.1 Components of spatial skills in relation to programming . . . . . . . . . . . 8<br/>2.4.2 Males vs females differences . . . . . . . . . . . . . . . . . . . . . . . . . . 8<br/>2.5 Theoretical frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>2.5.1 Object-visual-verbal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>2.5.2 Spatial encoding strategy ”SpES” theory . . . . . . . . . . . . . . . . . . . 9<br/>2.6 Neuroimaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>2.6.1 Relevant neuroimaging studies . . . . . . . . . . . . . . . . . . . . . . . . . 11<br/>3. Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>3.1 Systematic literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>3.1.1 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>3.1.2 Conducting searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br/>3.1.3 Exclusion Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br/>3.1.4 Grey Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br/>3.1.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15<br/>3.1.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br/>xv<br/>4. Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>4.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>4.1.2 Predictive modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26<br/>4.1.3 Model evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27<br/>4.1.4 Shortlisted cognitive Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27<br/>4.1.5 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28<br/>4.1.6 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29<br/>5. Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31<br/>5.1 PSTV-R Spatial rotation test results . . . . . . . . . . . . . . . . . . . . . . . . . . 31<br/>5.2 Non-Verbal Reasoning test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32<br/>5.3 Gender Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35<br/>5.4 Predictive Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35<br/>5.4.1 Method 1: Aggregate score of cognitive tests . . . . . . . . . . . . . . . . . . 35<br/>5.4.2 Method 2: Individual answers of questions as features . . . . . . . . . . . . 38<br/>5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42<br/>5.5.1 Threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44<br/>6. Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45<br/>6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45<br/>6.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46<br/>Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
520 3# - Abstract
Abstract Abstract:<br/><br/>Universities and students face the challenge of finding the career that best suits the students.<br/>Programming is currently one of the highly sought-after careers and is one of the highest paying<br/>jobs. However not all students succeed in this career. In fact failure rates in programming field<br/>is relatively high globally. Thus, the need for an assessment of student skills is required before<br/>admission to a CS program. Cognitive tests are widely used in this regard in addition to other<br/>means (i.e. school grades, personal information...etc). The aim of this study is to develop a model to<br/>predict programming performance with the minimum set of tests. We conducted a literature review<br/>on previously used cognitive tests to identify programming performance. Then we used a spatial<br/>rotation test and Non-Verbal reasoning test, as well as gender, at the beginning of the semester<br/>to observe the relationship between those tests and students’ relative programming grades at the<br/>end of the semester. We conducted this research on 3 groups. Group 1 are CS students studying<br/>programming course, group 2 are non-CS students studying programming course and group 3 CS<br/>students studying non-programming course. For each group, we created a predictive model using<br/>2 methods. Method 1 uses the aggregate score of cognitive tests and method 2 uses answers to<br/>selected questions in each test. In method 2, we applied a data-driven methodology that utilises<br/>individual answers to questions as input to the model. The modeling results show usefulness of those<br/>test to differentiate programming and non-programming course performance using both methods.<br/>Furthermore, method 2 demonstrates much higher superiority over method 1. The initial results<br/>show that those tests correlate highly to introductory programming grades as indicators/estimators<br/>to programming performance in early CS majors. We also observed a negative correlation of those<br/>tests and performance in non-programming courses which manifests the usefulness of those tests as a<br/>differentiator for different specializations. This has the benefit of helping students in choosing their<br/>career and it also helps learning institutions to segment students according to their programming<br/>performance to create customized learning experience.
546 ## - Language Note
Language Note Text in English, abstracts in English and Arabic
650 #4 - Subject
Subject informatics
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School informatics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
655 #7 - Index Term-Genre/Form
-- 187
690 ## - Subject
-- 2121
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library 12/11/2022   610/ A.F.P / 2022 12/11/2022 12/11/2022 Thesis