Accelerated Digital Transformation of Higher Education in the Wake of COVID-19: A Systematic Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has accelerated digital transformation (DT) across various industries, including higher education (HE). In response to the dynamic demands of contemporary society, higher education institutions (HEIs) must swiftly adapt and transform. However, existing research has revealed a prevalent lack of strategic vision regarding DT in HE, often limited to the mere integration of technology. This study employs a systematic literature review (SLR) as a methodological framework to identify and categorize DT challenges and strategies within HE accelerated after the pandemic event. Findings from this SLR highlight four distinct categories of challenges and strategies in DT: Strategic-Administrative, Teaching-Learning, Technical-Technological, and Social-Cultural. Notably, the literature tends to focus more on identifying challenges, revealing an unbalanced emphasis compared to analyzing how HEIs are actively progressing in their DT efforts. Furthermore, there is a significant absence of impact analysis regarding these DT strategies within HE. To address these gaps, recommendations for future research are proposed, including (i) Exploration of strategic processes in HE toward DT, (ii) Empirical analysis of the Digital Maturity of HEIs, and (iii) Assessment of the impact of the strategic responses of HE toward DT. In conclusion, this study underscores the urgency for a more strategic approach to DT in HE, emphasizing the need to shift the focus from technology integration toward holistic, effective, and outcome-driven strategies. These recommendations aim to guide future research toward a more interdisciplinary and comprehensive understanding of DT within the realm of HE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it