Diffusion of Innovation: Adoption of Learning Management System Technology in Emerging Market Economies
Bibliographic record
Abstract
The market for the next-generation learning management system (LMS) for higher education is poised to grow by the US $3.04 billion during 2020-2024 (MarketWatch, 2020), creating technological opportunities in the higher education learning landscape particularly due to growth in remote learning due to COVID-19 pandemic. This rapid growth necessitates an urgent need to integrate technology with the instructional design in academic programs at the Higher Educational Institutes (HEIs) globally. The existing literature on the LMS suggests a considerable resistance among the instructor group towards technology adoption in pedagogic strategy. LMS has been in existence for almost two decades, however, they have not been leveraged to their full potential. An understanding of the nature of technology adoption among instructor group is even more pronounced for the HEIs of emerging market economies (EMEs). An efficient and technology-inspired educational construct will boost the overall competitiveness of the EME's in their respective population skill development and attracting foreign investments for industrial growth. Moreover, given the lessons learned from the epidemiological uncertainties, such as most recently, the COVID-19 pandemic, educators should be prepared to utilize LMS to their full potential. The purpose of this study is to investigate the barriers and motivators in LMS adoption among emerging market economy HEIs and to propose a technology adoption model based on the Unified Theory of Acceptance and Use of Technology (V. Venkatesh et. al., 2012). The results of this study suggest implementing a dynamic feedback mechanism of technology adoption by instructors, and the need for HEIs to articulate strategic plan goals that focus on faculty professional development in the use of technology to build confidence among instructor group to enable the adoption of technology for instruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".