UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
|
||||||||||||||||||||||||||||||
| Abstract : Perpustakaan Tuanku Bainun |
| Over recent years, computer science (CS) teachers and instructors have faced several challenges in helping students strengthen their understanding of programming. The existing assessment methods could be more effective in assessing students' programming skills and knowledge, thus requiring a review of issues surrounding the instruction of programming courses. Against this backdrop, the authors systematically reviewed the current literature to identify several socio-cognitive factors that can help develop a learner model for learning programming. Specifically, the Systematic Reviews and Meta-Analyses (PRISMA) technique was utilized to identify and select relevant articles from three primary online databases: Scopus, Web of Science, and Eric. Initially, 401 relevant papers were identified and retrieved, further reduced to only 24 articles based on specific selection criteria. As revealed, several demographic factors (such as gender, age, ethnicity, and socioeconomic status) and socio-cognitive factors (motivation, attitude, and interest) have been shown to impact student learning of programming significantly. The authors' findings from the systematic literature review helped synthesize the essential elements of the learner model that must be carefully considered and utilized. Arguably, the use of such a new learner model can compel instructors to teach programming more effectively by clarifying several students' socio-cognitive backgrounds, which collectively have a significant impact on student learning of programming courses or subjects at the primary, secondary, and tertiary levels of education, especially in the Malaysian educational context. © 2024, Semarak Ilmu Publishing. All rights reserved. |
| References |
Wang, Yanqing, Hang Li, Yuqiang Feng, Yu Jiang, and Ying Liu. "Assessment of programming language learning based on peer code review model: Implementation and experience report." Computers & Education 59, no. 2 (2012): 412-422. https://doi.org/10.1016/j.compedu.2012.01.007 Tang, Xiaodan, Yue Yin, Qiao Lin, Roxana Hadad, and Xiaoming Zhai. "Assessing computational thinking: A systematic review of empirical studies." Computers & Education 148 (2020): 103798. https://doi.org/10.1016/j.compedu.2019.103798 Qian, Yizhou, and James Lehman. "Students’ misconceptions and other difficulties in introductory programming: A literature review." ACM Transactions on Computing Education (TOCE)18, no. 1 (2017): 1-24. https://doi.org/10.1145/3077618 Troussas, Christos, Akrivi Krouska, and Cleo Sgouropoulou. "A novel teaching strategy through adaptive learning activities for computer programming." IEEE Transactions on Education 64, no. 2 (2020): 103-109. https://doi.org/10.1109/TE.2020.3012744 Ullah, Zahid, Adidah Lajis, Mona Jamjoom, Abdulrahman Altalhi, Abdullah Al-Ghamdi, and Farrukh Saleem. "The effect of automatic assessment on novice programming: Strengths and limitations of existing systems." Computer Applications in Engineering Education 26, no. 6 (2018): 2328-2341. https://doi.org/10.1002/cae.21974 Haspel, Richard L., Asma M. Ali, Grace C. Huang, Matt H. Smith, James B. Atkinson, Devon S. Chabot-Richards, Robin M. Elliott et al. "Teaching genomic pathology: translating team-based learning to a virtual environment using computer-based simulation." Archives of pathology & laboratory medicine 143, no. 4 (2019): 513-517. https://doi.org/10.5858/arpa.2018-0153-OA Alammary, Ali. "Blended learning models for introductory programming courses: A systematic review." PloS one 14, no. 9 (2019): e0221765. https://doi.org/10.1371/journal.pone.0221765 Buyrukoglu, Selim, Firat Batmaz, and Russell Lock. "Improving marking efficiency for longer programming solutions based on a semi-automated assessment approach." Computer Applications in Engineering Education 27, no. 3 (2019): 733-743. https://doi.org/10.1002/cae.22094 Tong, Ke, Yuan Ni Chan, Xiaoqin Cheng, Bobby Cheon, Michelle Ellefson, Restria Fauziana, Shengchuang Feng et al. "Study protocol: How does cognitive flexibility relate to other executive functions and learning in healthy young adults?." PloS one 18, no. 7 (2023): e0286208. https://doi.org/10.1371/journal.pone.0286208 Bergmark, Ulrika. "Teachers’ professional learning when building a research-based education: context-specific, collaborative and teacher-driven professional development." Professional Development in Education 49, no. 2 (2023): 210-224. https://doi.org/10.1080/19415257.2020.1827011 McDonald, Jason K. "The everydayness of instructional design and the pursuit of quality in online courses." Online Learning(2023). https://doi.org/10.24059/olj.v27i2.3470 Kim, Semin, and Hyung-Jin Mun. "Design and development of a self-diagnostic mobile application for learning progress in non-face-to-face practice learning." Applied Sciences 11, no. 22 (2021): 10816. https://doi.org/10.3390/app112210816 Wu, Ting-Ting, and Jian-Ming Chen. "Combining Webduino programming with situated learning to promote computational thinking, motivation, and satisfaction among high school students." Journal of Educational Computing Research 60, no. 3 (2022): 631-660. https://doi.org/10.1177/07356331211039961 Woo, HoSung, and Ja-Mee Kim. "Impacts of Learning Orientation on the Modeling of Programming Using Feature Selection and XGBOOST: A Gender-Focused Analysis." Applied Sciences 12, no. 10 (2022): 4922. https://doi.org/10.3390/app12104922 |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |