Transforming online teaching and learning: towards learning design informed by information science and learning sciences
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an overview of the practical work of learning designers with the aim of helping members of the information science (IS) and learning sciences (LS) communities understand how evidence-informed learning design of online teaching and online learning in higher education is relevant to their research agendas and how they can contribute to this growing field. Design/methodology/approach Illustrating how current online education instructional designs largely ignore evidence from research, this paper argues that evidence from IS and LS can encourage more effective and nuanced learning designs for e-learning and online education delivery and suggest how interdisciplinary collaboration can advance shared understanding. Findings Recent reviews of the learning design show that tools and techniques from the LS can support students in self-directed and self-regulated learning. IS studies complement these approaches by highlighting the role that information systems and computer–human interaction. In this paper, the expertise from IS and LS are considered as important evidence to improve learning design, particularly vis-à-vis digital divide concerns that students face during the COVID-19 pandemic. Originality/value This paper outlines important ties between the learning design, LS and IS communities. The combined expertise is key to advancing the nuanced design of online education, which considers issues of social justice and equity, and critical digital pedagogy.
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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 | 0.057 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".