Rethinking eLearning Trends in Higher Education: A Critical Pacific Island Perspective
The rapid expansion of eLearning trends including learning analytics, BYOD and mobile learning (mLearning), virtual and augmented reality (VR/AR), makerspaces, and eAssessment—has reshaped higher education globally. However, in Pacific Island Countries (PICs), these trends must be critically evaluated within specific socio-economic, infrastructural, and cultural contexts rather than being adopted as universal solutions (Johnson et al., 2016). While these technologies offer significant potential, their effectiveness depends on how well they align with local realities.
Learning Analytics: Enhancing Learning or Oversimplifying It?
Learning analytics is increasingly positioned as a transformative tool for improving student outcomes through data-driven insights. Defined as the measurement and analysis of learner data to optimize learning environments (Siemens & Baker, 2012), it enables educators to track engagement, identify at-risk students, and design targeted interventions. At institutions such as the University of the South Pacific (USP), systems like the Early Warning System (EWS) demonstrate the practical application of learning analytics in monitoring student performance .
However, the effectiveness of learning analytics in PICs remains contested. While it can reveal patterns of engagement, it often fails to capture the underlying causes of disengagement, which in this context may include limited internet access, financial constraints, or competing social responsibilities. As Ferguson and Clow (2017) argue, there is still insufficient evidence regarding the long-term impact of learning analytics on learning outcomes. Thus, reliance on quantitative data alone risks reducing complex learning experiences into simplistic metrics. A more balanced approach would combine analytics with qualitative insights such as student feedback and contextual understanding.
BYOD and mLearning: Accessibility versus Equity
BYOD and mLearning have emerged as highly relevant trends in PICs, where geographical dispersion presents significant challenges to traditional education delivery. Mobile devices enable flexible, anytime-anywhere learning and can significantly enhance access to educational resources (Traxler, 2007). In many cases, smartphones are more accessible than computers, making mLearning a practical solution for expanding educational reach.
Nevertheless, this apparent accessibility masks deeper inequalities. Differences in device quality, data affordability, and internet reliability can create uneven learning experiences among students. As highlighted in the Horizon Report (Johnson et al., 2016), BYOD strategies may inadvertently shift the burden of access onto students, thereby reinforcing rather than reducing the digital divide. Consequently, institutions must adopt inclusive strategies such as low-bandwidth content and offline access to ensure equitable participation.
VR and AR: Potential versus Practicality
Virtual and augmented reality technologies are frequently promoted as tools for immersive and experiential learning. These technologies can enhance engagement and improve understanding of complex concepts by simulating real-world environments (Radianti et al., 2020). In Economics education, for instance, VR could be used to simulate market dynamics or policy environments.
However, in PICs, the adoption of VR and AR is constrained by high costs, limited infrastructure, and gaps in digital literacy. Furthermore, much of the available content is developed in non-Pacific contexts, raising concerns about cultural and contextual relevance. As such, the assumption that these technologies will automatically enhance learning outcomes is questionable. A more realistic approach would involve gradual adoption, focusing on cost-effective and mobile-compatible solutions.
Makerspaces: Innovation and Sustainability
Makerspaces, grounded in constructionist learning theory, promote hands-on, collaborative, and problem-based learning. They encourage creativity, critical thinking, and interdisciplinary engagement (Peppler & Bender, 2013). While traditionally associated with STEM, makerspaces can be adapted to disciplines such as Economics through activities like financial planning, business modeling, and data analysis projects.
However, the sustainability of makerspaces in PICs remains a critical concern. Establishing and maintaining such environments requires significant investment, technical expertise, and institutional commitment. Without these, makerspaces risk becoming short-lived initiatives rather than integrated components of the learning ecosystem.
eAssessment and Feedback: Rethinking Evaluation in the Digital Age
The integration of eAssessment has transformed traditional assessment practices by enabling continuous, formative feedback and more flexible evaluation methods. Digital tools such as quizzes, discussion forums, and student response systems allow for immediate and personalized feedback, which is essential for enhancing learning (Boud, 2000).
However, challenges such as plagiarism and the limitations of tools like Turnitin highlight the need for more authentic assessment design. Rather than relying solely on detection tools, educators should focus on creating assessments that emphasize application, critical thinking, and originality. The rise of generative AI further reinforces the need to redesign assessments to prioritize higher-order cognitive skills.
Application to Economics Teaching
In my context as an Economics educator, I would integrate learning analytics to monitor student engagement, identify difficult topics, and provide targeted interventions. For example, Moodle logs can reveal patterns in student participation, enabling early support for those at risk. However, I would complement this with direct student interaction to ensure a deeper understanding of learning challenges.
Additionally, I would adopt mLearning strategies by providing mobile-friendly resources, including short videos, quizzes, and discussion forums, to enhance accessibility for students in remote areas. Incorporating makerspace-inspired assessments, such as real-world financial analysis projects, would further bridge the gap between theory and practice.
While eLearning trends offer transformative potential for higher education in PICs, their adoption must be guided by critical reflection rather than technological enthusiasm. The key challenge is not simply adopting new technologies, but ensuring that they are implemented in ways that are equitable, contextually relevant, and pedagogically effective.
Ultimately, the future of higher education in PICs depends not on adopting the most advanced technologies, but on choosing the most appropriate ones—those that address local challenges while enhancing meaningful learning outcomes.
References
Boud, D. (2000). Sustainable assessment: Rethinking assessment for the learning society. Studies in Continuing Education, 22(2), 151–167.
Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 56–65.
Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC Horizon Report: 2016 Higher Education Edition. The New Media Consortium.
Peppler, K., & Bender, S. (2013). Maker movement spreads innovation one project at a time. Phi Delta Kappan, 95(3), 22–27.
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education. Computers & Education, 147, 103778.
Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254.
Traxler, J. (2007). Defining, discussing, and evaluating mobile learning. International Review of Research in Open and Distributed Learning, 8(2).
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