The Future of Higher Education: Identifying Current Educational Problems and Proposed Solutions
Bibliographic record
Abstract
It is widely acknowledged that higher education is failing to meet the needs of students and employers, while educational costs and student debt are rapidly increasing. Our aim was to address these issues in an innovative fashion through a structured review combined with recommendations for best practices. Specifically, we aimed to identify and systemize failings of higher ed based on current scholarship, propose solutions, and identify institutions of higher education (IHEs) that have begun to successfully put these solutions in practice. Based on our literature review, this is the first time such a study has been conducted. We performed a structured literature review and identified four key failings in higher education: quality, relevance, access, and cost. From the reviewed literature we extracted a rubric to identify and evaluate twelve IHEs that are effectively applying new and innovative models that address these four problems. We conclude by recommending best practices for the successful redesign of IHEs. The overarching problem we identified was lack of student preparedness to succeed in a highly complex, competitive, and increasingly global, digital world—curricula lack relevance. IHEs are failing to teach the skills and tools needed for sustained success in the workplace: critical and creative thinking, problem-solving, co-operation, tolerance, and collaboration (which incidentally align with the skills and tools needed for effective citizenship) and when they do, they are not using evidence-based pedagogical strategies drawn from research on the science of learning. Additionally, IHEs are failing to provide accessible, high-quality, affordable postsecondary education. Financial and geographic inaccessibility, opaque admissions processes, attrition, poor attention to student health and well-being, lack of Indigenous inclusion, weak utilization of technology, and outmoded teaching methods and content contribute to the barriers to student success. The twelve IHEs we identified are geographically, economically, and pedagogically diverse, each serving as a model for the future of higher education. The novel contributions offered here are (i) a systematic review of higher education’s failings as they impact students and employers, (ii) identification of specific programs and initiatives that can ameliorate these failings, and (iii) identification of IHEs that are engaging in best practices with respect to (i) and (ii).
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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 | 0.079 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".