Transforming online teaching and learning: towards learning design informed by information science and learning sciences
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
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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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.003 | 0.018 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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