Journal of Paddisengeng Technology

The Digital Divide 2.0: Analyzing Connectivity and Device Quality Gaps in Remote Learning

2026-03-29T21:07:26+07:00

Background. The rapid shift to remote learning has intensified concerns about digital inequality, traditionally understood as disparities in access to internet connectivity and devices. Emerging evidence indicates that access alone is insufficient, as differences in connectivity stability and device performance critically shape students’ ability to participate effectively in online learning environments. These developments call for a reconceptualization of inequality within the framework of Digital Divide 2.0.

Purpose. This study aims to analyze the impact of connectivity stability and device quality on student engagement and learning effectiveness, and to conceptualize these disparities as multidimensional components of Digital Divide 2.0.

Method. A mixed-methods research design was employed involving 312 students from diverse educational contexts. Data were collected through structured surveys, objective connectivity tests, device quality assessments, and qualitative interviews. Quantitative data were analyzed using descriptive and inferential statistics to examine relationships and interaction effects, while qualitative data were analyzed through thematic coding to capture student experiences.

Results. The findings indicate that connectivity stability is a strong predictor of student engagement, while device quality significantly influences learning effectiveness. Interaction effects demonstrate that combined limitations in connectivity and device performance produce compounded disadvantages in learning outcomes. Qualitative insights further reveal that students facing unstable connections and low-performing devices experience frustration, reduced participation, and limited access to interactive learning opportunities.

Conclusion. Digital inequality in remote learning is multidimensional and extends beyond access to include quality-based disparities in connectivity and devices. Addressing Digital Divide 2.0 requires comprehensive strategies that integrate improvements in digital infrastructure with access to adequate technological resources. Policymakers and educators are encouraged to adopt holistic interventions to ensure equitable and effective remote learning environments.

Total Quality Management (TQM) in Higher Education: Implementation Challenges in Developing Nations

2026-03-19T06:37:51+07:00

Background. The growing demand for accountability, global competitiveness, and institutional performance has driven higher education institutions—particularly in developing countries—to adopt Total Quality Management (TQM) frameworks. However, despite strong policy endorsement, TQM implementation often remains fragmented and compliance-oriented due to structural, cultural, and resource-related constraints.

Purpose. This study aimed to examine the key challenges in implementing TQM in higher education institutions across developing countries and to identify the critical determinants that influence the effectiveness of TQM practices.

Method. A mixed-methods sequential explanatory design was employed. Quantitative data were collected through surveys involving 387 academic and administrative staff, while qualitative data were obtained from semi-structured interviews with 24 institutional leaders and quality assurance officers. Statistical analyses were used to identify significant predictors, followed by thematic analysis to deepen interpretation of the quantitative results.

Results. The quantitative findings revealed that leadership commitment, organizational culture readiness, and resource adequacy significantly predict the effectiveness of TQM implementation, with organizational culture functioning as a mediating factor. Qualitative results further indicated that governance instability, limited financial investment, and resistance to managerial reforms constitute persistent barriers to successful implementation.

Conclusion. The study concludes that TQM implementation challenges in developing countries are primarily systemic rather than technical. Effective and sustainable quality enhancement requires integrated alignment among leadership, institutional structures, and organizational culture, as well as context-sensitive adaptation of global quality management models to local higher education realities.

Immersive History: Measuring the Impact of Virtual Reality (VR) on Empathy and Historical Retention

2026-03-29T21:10:17+07:00

Background. Virtual Reality (VR) has emerged as an innovative educational technology that enables immersive learning experiences, particularly in history education where contextual understanding and perspective-taking are essential. Traditional instructional approaches often face limitations in fostering emotional engagement and sustaining long-term knowledge retention.

Purpose. This study aims to examine the impact of VR on student empathy and historical retention, as well as to explore the relationship between immersive experience and learning outcomes in history education.

Method. A quasi-experimental mixed-methods design was employed involving 104 students assigned to experimental (VR-based learning) and control (traditional instruction) groups. Data were collected through pretest–posttest assessments to measure retention, empathy questionnaires to assess affective responses, and qualitative reflections to capture students’ experiential insights. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were examined through thematic analysis.

Results. The findings indicate that VR significantly enhances both student empathy and historical retention compared to conventional instructional methods. A strong relationship is observed between immersive experience, emotional engagement, and improved memory outcomes. Empathy is identified as a mediating factor that strengthens the connection between immersion and knowledge retention.

Conclusion. VR offers an effective pedagogical approach for integrating cognitive and affective dimensions of learning in history education. The results highlight the potential of immersive technologies to create meaningful and engaging learning experiences that support deeper understanding and long-term retention. Educators are encouraged to incorporate VR strategically to maximize both emotional engagement and academic achievement.

Web 2.0 and Higher Education: Exploring Emerging Trends in Digital Teaching and Learn-ing

2026-03-29T21:05:47+07:00

Background. The rapid expansion of internet technologies has significantly transformed university education, reshaping how knowledge is created, shared, and experienced. The emergence of Web 2.0 innovations has introduced interactive, collaborative, and user-generated approaches that challenge traditional pedagogical models and redefine the dynamics of teaching and learning in higher education.

Purpose. This paper aims to provide a comprehensive overview of the role of the Internet, particularly Web 2.0 technologies, in transforming university education, with a focus on emerging trends in teaching strategies, student engagement, and participatory learning environments.

Method. This study adopts a qualitative review approach, synthesizing relevant literature on Web 2.0 technologies in higher education. The analysis focuses on key digital tools such as blogs, wikis, social media platforms, and online learning communities, examining their pedagogical implications and contributions to evolving instructional practices.

Results. The review reveals that Web 2.0 technologies significantly reshape traditional pedagogy by fostering interactive and collaborative learning environments. These technologies enhance student engagement, promote active participation, and support knowledge co-construction through user-generated content. Furthermore, they enable more flexible and student-centered learning approaches, encouraging autonomy, creativity, and continuous interaction beyond the classroom.

Conclusion. The integration of Web 2.0 technologies plays a crucial role in advancing university education in the digital age. These innovations contribute to the development of more participatory and dynamic learning ecosystems that align with contemporary educational needs. Higher education institutions are encouraged to strategically adopt and integrate Web 2.0 tools to enhance teaching effectiveness and optimize student learning experiences.

Gamification and Student Engagement: A Comparative Study Between Intrinsic and Extrinsic Motivation

2026-03-29T21:13:35+07:00

Background. Gamification has emerged as an innovative pedagogical approach to enhance student engagement by integrating game-based elements such as rewards, points, and challenges into learning environments. Existing studies predominantly emphasize its effectiveness in increasing participation; however, limited attention has been given to how gamification differentially influences intrinsic and extrinsic motivation as key drivers of engagement.

Purpose. This study aims to examine the comparative effects of gamification on intrinsic and extrinsic motivation and to analyze how these motivational dimensions subsequently impact student engagement in educational settings.

Method. A mixed-methods research design was employed, combining quantitative and qualitative approaches. Survey data were collected to measure students’ intrinsic and extrinsic motivation levels before and after exposure to gamified learning environments. In addition, semi-structured interviews were conducted to capture in-depth student perspectives on their motivational experiences.

Results. The findings demonstrate that gamification significantly enhances both intrinsic and extrinsic motivation. Students exposed to gamified learning showed a 0.7-point increase in intrinsic motivation and a 0.5-point increase in extrinsic motivation compared to the control group. Qualitative insights further reveal that gamified elements not only increase enjoyment and interest in learning but also reinforce goal-oriented behaviors through structured rewards and feedback mechanisms.

Conclusion. Gamification serves as an effective strategy for fostering student engagement by simultaneously addressing intrinsic enjoyment and extrinsic incentives. The results highlight the importance of designing balanced gamified learning environments that accommodate diverse motivational factors. Educators are encouraged to strategically integrate gamification elements to optimize both participation and learning outcomes.

Adaptive Learning Systems: Personalizing Mathematics Instruction for At-Risk Students

2026-03-29T21:08:57+07:00

Background. Mathematics learning among at-risk students is frequently constrained by heterogeneous learning gaps, low engagement, and limited access to individualized instruction. Conventional teaching approaches often fail to accommodate these differences, leading to persistent disparities in academic achievement.

Purpose. This study aims to examine the effectiveness of adaptive learning systems in personalizing mathematics instruction and improving learning outcomes for at-risk students, as well as to explore the role of student engagement in this process.

Method. A quasi-experimental mixed-methods design was employed involving 112 students assigned to experimental (adaptive learning system) and control (traditional instruction) groups. Data were collected through pretest–posttest assessments to measure achievement, engagement questionnaires to assess behavioral and emotional involvement, and system-generated learning analytics to track progress and interaction patterns. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights were derived through thematic analysis.

Results. The findings indicate that adaptive learning systems significantly improve mathematics achievement and student engagement compared to conventional instructional methods. Personalized learning pathways, immediate feedback, and continuous progress monitoring contribute to enhanced performance and reduced variability among learners. Engagement is identified as a mediating factor that strengthens the relationship between adaptive instruction and academic achievement.

Conclusion. Adaptive learning systems offer an effective and scalable solution for supporting at-risk students in mathematics education. The results underscore the importance of integrating personalization and engagement-focused strategies to promote equitable learning outcomes. Educators and institutions are encouraged to adopt adaptive technologies to create more responsive and inclusive instructional environments.

Generative AI in the Classroom: Ethical Frameworks for Student-AI Collaboration

2026-03-29T21:11:58+07:00

Background. The rapid integration of generative artificial intelligence (AI) in educational settings has transformed student learning practices and introduced new forms of human–AI collaboration. Despite its potential to enhance engagement and expand access to knowledge, the use of AI raises critical ethical concerns related to academic integrity, authorship, and responsible use in academic contexts.

Purpose. This study aims to develop an ethical framework to guide student–AI collaboration in classroom settings and to examine the relationship between AI usage, ethical awareness, and student learning outcomes.

Method. A mixed-methods research design was employed, integrating quantitative and qualitative approaches. Survey data were collected from 212 participants to assess patterns of AI usage, ethical awareness, and perceived learning outcomes. In addition, interviews and focus group discussions were conducted to capture in-depth perspectives. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were examined through thematic coding techniques.

Results. The findings reveal that AI usage significantly enhances student engagement; however, it does not automatically improve ethical awareness. Institutional guidance and clear policy frameworks emerge as critical factors influencing responsible AI use and the maintenance of academic integrity. The analysis further identifies key ethical dimensions underlying student–AI collaboration, including transparency, accountability, fairness, and critical engagement.

Conclusion. The effective integration of generative AI in education requires the development of structured ethical frameworks that align technological practices with pedagogical objectives. The proposed framework offers practical guidance for educators and institutions in fostering responsible, ethical, and meaningful student–AI collaboration.