000			08097nam a22002537a 4500
008			201210b2022 a|||f bm|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aAyman Aboulmagd Ahmed Abdelraheem Farghaly _92120
245	1		_aPPPP: _bPRECOLLEGE PREDICTION OF PROGRAMMING PERFORMANCE; A TALENT IDENTIFICATION FRAMEWORK / _cAyman Aboulmagd Ahmed Abdelraheem Farghaly
260			_c2022
300			_a 55 p. _bill. _c21 cm.
500			_3Supervisor: Dr. Passent M. El-Kafrawy
502			_aThesis (M.A.)—Nile University, Egypt, 2022 .
504			_a"Includes bibliographical references"
505	0		_aContents: Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix Chapters: 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Introduction to Cognitive Abilities . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Relevant broad abilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Definition of programming performance . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Spatial skills and STEM achievement . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4.1 Components of spatial skills in relation to programming . . . . . . . . . . . 8 2.4.2 Males vs females differences . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.5 Theoretical frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.5.1 Object-visual-verbal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.5.2 Spatial encoding strategy ”SpES” theory . . . . . . . . . . . . . . . . . . . 9 2.6 Neuroimaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.6.1 Relevant neuroimaging studies . . . . . . . . . . . . . . . . . . . . . . . . . 11 3. Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1 Systematic literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Conducting searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.3 Exclusion Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.4 Grey Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 xv 4. Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.2 Predictive modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1.3 Model evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.4 Shortlisted cognitive Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.5 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.1.6 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5. Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.1 PSTV-R Spatial rotation test results . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Non-Verbal Reasoning test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.3 Gender Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.4 Predictive Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.4.1 Method 1: Aggregate score of cognitive tests . . . . . . . . . . . . . . . . . . 35 5.4.2 Method 2: Individual answers of questions as features . . . . . . . . . . . . 38 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.5.1 Threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6. Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
520	3		_aAbstract: Universities and students face the challenge of finding the career that best suits the students. Programming is currently one of the highly sought-after careers and is one of the highest paying jobs. However not all students succeed in this career. In fact failure rates in programming field is relatively high globally. Thus, the need for an assessment of student skills is required before admission to a CS program. Cognitive tests are widely used in this regard in addition to other means (i.e. school grades, personal information...etc). The aim of this study is to develop a model to predict programming performance with the minimum set of tests. We conducted a literature review on previously used cognitive tests to identify programming performance. Then we used a spatial rotation test and Non-Verbal reasoning test, as well as gender, at the beginning of the semester to observe the relationship between those tests and students’ relative programming grades at the end of the semester. We conducted this research on 3 groups. Group 1 are CS students studying programming course, group 2 are non-CS students studying programming course and group 3 CS students studying non-programming course. For each group, we created a predictive model using 2 methods. Method 1 uses the aggregate score of cognitive tests and method 2 uses answers to selected questions in each test. In method 2, we applied a data-driven methodology that utilises individual answers to questions as input to the model. The modeling results show usefulness of those test to differentiate programming and non-programming course performance using both methods. Furthermore, method 2 demonstrates much higher superiority over method 1. The initial results show that those tests correlate highly to introductory programming grades as indicators/estimators to programming performance in early CS majors. We also observed a negative correlation of those tests and performance in non-programming courses which manifests the usefulness of those tests as a differentiator for different specializations. This has the benefit of helping students in choosing their career and it also helps learning institutions to segment students according to their programming performance to create customized learning experience.
546			_aText in English, abstracts in English and Arabic
650		4	_ainformatics
655		7	_2NULIB _aDissertation, Academic _9187
690			_ainformatics _92121
942			_2ddc _cTH
999			_c9844 _d9844