The Past and Present of Instructional Design in Online Learning: Trends and Emerging Directions
Bibliographic record
Abstract
With the rapid evolution of online learning, driven by technological advancements and the global transition to distance education during the COVID-19 pandemic, the demand for effective instructional design models has become increasingly critical. This study conducted a systematic mapping analysis of instructional design models tailored for online learning environments to offer a comprehensive classification and evaluation of these models. The analysis delved into the theoretical underpinnings, practical applications, and implications for educators and instructional designers. Models were categorized based on instructional conditions, desired learning outcomes, and theoretical-methodological frameworks, and thus provided a structured approach to understanding their relevance and effectiveness. The findings underscored a paradigm shift from traditional, content-centric frameworks toward adaptive, learner-centered designs that emphasize motivation, social interaction, personalization, and technological integration. Additionally, this study offered practical recommendations for selecting and implementing models that align with the dynamic needs of learners and supported future advancements in instructional design, to foster innovation and effective learning in diverse educational contexts.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".