A Phenomenographic Analysis of College Students’ Conceptions of and Approaches to Programming Learning: Insights From a Comparison of Computer Science and Non-Computer Science Contexts

2021 ◽  
pp. 073563312199595
Author(s):  
Te-Lien Chou ◽  
Kai-Yu Tang ◽  
Chin-Chung Tsai
Keyword(s):  
College Students ◽  
Computer Science ◽  
Learning Outcomes ◽  
Approaches To Learning ◽  
Digital Age ◽  
Conceptions Of Learning ◽  
Community Interactions ◽  
Learning Programming ◽  
Northern Taiwan ◽  
Rote Memory

Programming learning has become an essential literacy for computer science (CS) and non-CS students in the digital age. Researchers have addressed that students’ conceptions of learning influence their approaches to learning, and thus impact their learning outcomes. Therefore, we aimed to uncover students’ conceptions of programming learning (CoPL) and approaches to programming learning (APL), and analyzed the differences between CS and non-CS students. Phenomenographic analysis was adopted to analyze 31 college students (20 CS-related, and 11 not) from northern Taiwan. Results revealed six categories of CoPL hierarchically: 1. memorizing concepts, logic, and syntax, 2. computing and practicing programming writing, 3. expressing programmers’ ideas and relieving pressure, 4. applying and understanding, 5. increasing one’s knowledge and improving one’s competence, and 6. seeing in a new way. Four categories of APL were also found, namely: 1. copying from the textbook, teachers, or others, 2. rote memory, 3. multiple exploration attempts, and 4. online or offline community interactions. Furthermore, we found that most CS students held higher level CoPL (e.g., seeing in a new way) than non-CS students. However, compared with non-CS students, CS students adopted more surface approaches to learning programming, such as copying and rote memory. Implications are discussed.

College Students’ Conceptions of Learning of and Approaches to Learning Computer Science

2019 ◽  
Vol 58 (3) ◽  
pp. 662-686 ◽  
Author(s):  
Karthikeyan Umapathy ◽  
Albert D. Ritzhaupt ◽  
Zhen Xu
Keyword(s):  
College Students ◽  
Computer Science ◽  
Learning Strategies ◽  
Approaches To Learning ◽  
Internal Consistency Reliability ◽  
Stepwise Multiple Regression ◽  
Conceptions Of Learning ◽  
Approach To Learning ◽  
Research Institution ◽  
Moderating Factors

The purpose of this research was to examine college students’ conceptions of learning computer science and approaches to learning computer science and to examine the relationships among these two important constructs and possible moderating factors. Student data ( N = 193) were collected using the conceptions of learning computer science and the approaches to learning computer science surveys at one public research institution in the southeastern United States. Data were analyzed with descriptive statistics, Confirmatory Factor Analysis models, internal consistency reliability, Pearson correlations, stepwise multiple regression models, and Multivariate Analysis of Variance models. The results suggest that college students most favorably employ a deep strategy approach for learning computer science in which prior knowledge is activated and meaningful learning strategies are used. College students appear to be more extrinsically motivated to learn computer science than intrinsically. Higher level learning conceptions are associated with a deep strategy approach to learning (e.g., Seeing in a new way) whereas low-level conceptions are associated with a surface strategy (e.g., Memorizing) approach to learning. Male college students have slightly higher conceptions of programming than their female counterparts. The findings are discussed and both limitations and delimitations of the study are enumerated.

scholarly journals An Overviuw Of The Teaching And Learning Process Basic Programming In Algorithm And Programming Courses

2021 ◽  
Vol 12 (2) ◽  
pp. 2934-2944
Author(s):  
Wilda Susanti, Et. al.
Keyword(s):  
Computer Science ◽  
Programming Languages ◽  
Learning Outcomes ◽  
Programming Language ◽  
Learning Process ◽  
Teaching And Learning ◽  
Computer Programming ◽  
Student Difficulties ◽  
Learning Programming ◽  
Programming Courses

In this paper, we review the literature related to computer programming learning, where Algorithms and Programming are the topic domains of the Informatics and Computer science clusters. There are 4 competencies in learning outcomes, such as: 1) understand algorithmic concepts; 2) master algorithm concepts and principles; 3) master programming language concepts; and 4) master programming languages and algorithms. The main focus of this review is on beginner programming and topics related to student difficulties in learning programming. Various problems experienced by beginners were identified from the literature to some of the solutions offered by researchers.

scholarly journals Analysis of some factors influencing the performance of college students: An example of Computer Science education

2020 ◽  
Vol 22 (2) ◽  
pp. 171-196
Author(s):  
L. V. Gubina ◽  
T. V. Alekseeva ◽  
O. A. Strakhov
Keyword(s):  
College Students ◽  
Science Education ◽  
Vocational Education ◽  
Computer Science ◽  
Learning Outcomes ◽  
Education System ◽  
Moscow Region ◽  
Secondary Vocational Education ◽  
Level Of Knowledge ◽  
Regions Of Russia

Introduction. In recent years, the structure and content of training of specialists of further education have significantly improved. The increased popularity of the secondary vocational education system is evidenced by the fact that more than half of the Russian teenagers after graduating from the main stage of secondary school become applicants of technical schools and colleges. In order to manage students’ education more effectively, among other measures, it is necessary to diagnose the motivation of enrolled students at the stage of admission to the educational institution and to identify the degree of awareness of their future career choice, as these factors directly depend on the success of educational programmes.The aim of the article was to reveal the trends of influence of students’ results at the Basic State Exam (OGE – the exam, which is taken when finishing education in the 9th (final) form of comprehensive school) on the level of knowledge of Computer Science in colleges and to find out the subjective reasons of students’ preferences for the secondary vocational education system to continue studies and to enter a profession.Methodology and research methods. In the course of the study, a review and generalisation of the content of scientific sources related to the problems of professional choice and training motivation were used. Diagnostics of motivation of college students in Moscow, Moscow region and the regions of Russia was conducted through the methods of testing, surveys and anonymous questioning. Processing of the obtained data was carried out by the methods of correlation, variance and regression analysis; the degree of statistical reliability of the results was evaluated by calculating the Student’s t-test and the Fisher’s F-test.Results and scientific novelty. The authors have calculated the numerical indicators of relationship between students’ academic performance in computer science and the Basic State Exam taking, the reasons for choosing the secondary vocational education and the specialty. Constructed graphs and approximating curves prove the fact that the success degree when learning the certain discipline in college results from the assessment within the discipline taken at the Basic State Exam in school. In the regions of Russia, this factor guarantees a higher level of knowledge on Computer Science – by about 20%, and in the Moscow region – by 10%. The statistics on respondents’ professional orientation were collected. A regression model, demonstrating the impact of students’ motivational attitudes on their training in the subject discipline, is presented. It was found out that the motives “subsequent admission to a specialised university”, “obtaining a profession”, “business attitude to a profession” and “prestige of a profession” have the most positive influence in this context. The variance analysis confirmed the determinism of the learning outcomes by the reasons for the choice of secondary vocational education and profession. It is concluded that the reasons for the low or medium students’ performance include not only their weak motivation for education, but also the state of the entire education system, including the institutions of the Basic State Exam (OGE) and the Unified State Exam (EGE – high school final and university entrance exam taken upon completion of the 11th form), as well as the lack of clear criteria for the admission of applicants to the institutions of secondary vocational education. To get a specific specialty, the desire to study, its informed choice and prestige of profession positively affect students’ learning outcomes in Computer Science education.Practical significance. The research materials can be useful for teachers of secondary vocational education and for specialists involved in career guidance.

scholarly journals Barriers to Digital Literacy: Learning to Program

2020 ◽  
Author(s):  
Andréa Cartile
Keyword(s):  
Computer Science ◽  
Digital Literacy ◽  
Teaching And Learning ◽  
Engineering Students ◽  
Approaches To Learning ◽  
Literacy Learning ◽  
First Year ◽  
Science Domain ◽  
Computer Based ◽  
Learning Programming

There are many challenges associated with teaching and learning computer programming for first year engineering students in non-computer based fields. This paper discusses barriers to acquiring the digital literacy needed to learn end-user programming, or programming as a tool to support activities in a non-computer science domain. The first barrier discussed is the gap in educational curriculum, where the first formal introduction to computer science and programming is found in pre-university preparatory courses. The second barrier is a lack of consensus in approaches to learning programming in online resources. A solution of integrating opportunities to use programming as a tool in existing course curriculum activities is proposed, as a way to improve programming accessibility and allow future engineers to use digital skills to innovate in non-computer based applications.

Exploring college students’ conceptions of learning computer science: a draw-a-picture technique study

2020 ◽  
pp. 1-23
Author(s):  
Zhen Xu ◽  
Albert D. Ritzhaupt ◽  
Karthikeyan Umapathy ◽  
Yang Ning ◽  
Chin-Chung Tsai
Keyword(s):  
College Students ◽  
Computer Science ◽  
Conceptions Of Learning

scholarly journals Neural alignment predicts learning outcomes in students taking an introduction to computer science course

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Brain Activity ◽  
Activity Patterns ◽  
Real Life ◽  
Final Exam ◽  
Neural Representations ◽  
Real Life Setting ◽  
The Right ◽  
Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

The influence of the multimedia and modality principles on the learning outcomes, satisfaction, and mental effort of college students with and without dyslexia

2021 ◽  
Author(s):  
Kara Dawson ◽  
Jiawen Zhu ◽  
Albert D. Ritzhaupt ◽  
Pavlo Antonenko ◽  
Kendra Saunders ◽  
...  
Keyword(s):  
College Students ◽  
Learning Outcomes ◽  
Mental Effort

The Impact of Gamification on Learning Outcomes of Computer Science Majors

2020 ◽  
Vol 20 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Adnan Ahmad ◽  
Furkh Zeshan ◽  
Muhammad Salman Khan ◽  
Rutab Marriam ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Science Majors ◽  
Computer Science Majors ◽  
The Impact
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