Activity-Based Instructional Design for online teaching / <i>Diseño Instruccional Basado en Actividades para la enseñanza en línea</i>
Bibliographic record
Abstract
The goal of this paper is to describe the Activity-Based Instructional Design (ABID) model for the online course designer, who may not have expertise in learning theory. ABID provides the course designer and instructor in tertiary education with a naturally modular, student-centred and theory-informed template which reconceptualizes the online course as a series of interconnected and interrelated activities that provides the learner with the set of psychological tools needed to achieve the learning objectives of the course. In this Vygotskian framework, two crucial ideas emerge: the learning activity and not the software that is the unit of analysis; and the learning outcome of each activity provides the psychological tools used by a learner in subsequent activities. We employ Engeström’s cultural-historical activity theory as a framework for the design of the online course. To illustrate this design process, we apply the ABID model to the design of an online introductory statistics course. Because ABID is based on a well-developed learning theory, it enables us to make clear proscriptions to achieve better student learning.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".