Investigating Sociocultural Issues in Instructional Design Practice and Research
Bibliographic record
Abstract
This chapter is a narrative account of the process involved to initiate a program of research to explore how instructional designers around the world use design to make a social difference locally and globally. The central research question was, “Are there social and political purposes for design that are culturally based?” A growing body of research is concerned with the design of culturally appropriate learning resources and environments, but the focus of this research is the instructional designer as the agent of the design. Colloquially put, if, as has been suggested, we tend to design for ourselves, we should understand the sociocultural influences on us and how they inform our practices. We should also develop respect for, and learn from, how various global cultures address similar design problems differently. The authors report the results of a preliminary investigation held with instructional designers from ten countries to examine culturally situated values and practices of instructional design, describe the research protocol developed to expand the investigation internationally, and share emerging issues for instructional design research with international colleagues. In this chapter, the authors link their earlier work on instructional designer agency with the growing research base on instructional design for multicultural and/or international learners. This research takes the shape of user-centred design and visual design; international curriculum development, particularly in online or distance learning; and emphasis on culturally appropriate interactions. We have suggested that instructional designers’ identity, including their values and beliefs about the purpose of design, are pivotal to the design problems they choose to work on, the contexts in which they choose to practice, and with whom. Our interest in the culture of design, then, is less process-based (how to do it) than interrogative (why we do it the way we do). And that has led us to ask, “Is there one culture of instructional design, or are there many, and how are these cultures embodied in instructional designers’ practice?” The idea of design culture is well established. Most notably, investigations of professional culture have attracted significant attention (Boling, 2006; Hill, J., et. al., 2005; Snelbecker, 1999). These investigations have concentrated on how different professions, such as architecture, drama, engineering and fine art approach design differently, with the goal of informing the practice of design in instructional design (ID). The decision-making processes of design professionals have also been illuminated by scholars like Donald Schon (1983) who described knowing-in-action and suggested the link between experience, (sociocultural) context, and intuition with design made visible through reflective practice.
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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 | 0.131 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.022 | 0.062 |
| Scholarly communication | 0.036 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| 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".