Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JURNAL JPSD (Jurnal Pendidikan Sekolah Dasar) Knowledge Engineering and Data Science
Saleh, Abdulrazak Yahya
Unknown Affiliation
Computer Science & IT Education Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Factors influencing student engagement in online ideological and political education: a qualitative study of vocational college students in China Chunxiu, Sun; Saleh, Abdulrazak Yahya
Jurnal JPSD (Jurnal Pendidikan Sekolah Dasar) Vol. 12 No. 1 (2025): May
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jpsd.v12i1.a30759

Abstract

The COVID-19 pandemic accelerated the global shift to online education, exposing both opportunities and challenges in ideological and political (I&P) courses at Chinese higher vocational colleges, where student engagement remains pivotal yet underexplored. This qualitative study examines how students perceive and experience engagement in online I&P courses, framed by Activity Theory, Social Interaction, and Critical Pedagogy. Semi-structured interviews were conducted with 30 students (15 males, 15 females; freshman to junior cohorts) from Anhui Vocational and Technical College, using Tencent Meetings for 30-minute sessions. Thematic analysis identified two core themes: (1) Infrastructure—students emphasized the necessity of clear rules, stable platforms (e.g., MOOCs, ClassIn), and teacher responsiveness to foster accountability; (2) Engagement Dynamics—peer collaboration, real-world case studies, and critical discussions enhanced motivation, while poor internet connectivity, abstract content, and self-regulation struggles impeded participation. Notably, students highlighted the transformative potential of interactive tools (e.g., real-time Q&A, role-playing simulations) in bridging theory and practice. Limitations include the single-institution sample, potential response bias, and lack of longitudinal data. Nevertheless, findings offer actionable insights: educators should design modular content aligned with vocational contexts, integrate adaptive technologies to mitigate connectivity issues, and implement structured peer-review systems to sustain motivation. Institutional support for digital literacy training and hybrid learning models is also critical. Future research should expand to diverse regions, incorporate mixed methods, and track long-term outcomes to strengthen pedagogical strategies in online I&P education.
Stress Classification using Deep Learning with 1D Convolutional Neural Networks Saleh, Abdulrazak Yahya; Xian, Lau Khai
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset is utilized in this paper. The PS dataset consists of twelve features obtained from the expert. The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. The 1D CNNs outputs 99.7% in stress classification, which outperforms the Backpropagation (BP), only 65.57% in stress classification. Therefore, the findings yield a promising outcome that the 1D CNNs effectively classify stress compared to BP. Further explanation is provided in this paper to prove the efficiency of 1D CNN for the classification of stress.
Co-Authors Chunxiu, Sun Xian, Lau Khai