PPPP: (Record no. 9844)
[ view plain ]
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 |
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 |