Interactivity of Information Representations in e-Learning Environments
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
This chapter is concerned with interactivity of information representations in e-learning environments (ELEs)—where interactivity refers to the quality or condition of interaction with representations in an ELE. An ELE is any interactive computer-based software that mediates and supports learners’ engagement with information. This chapter draws upon literature from the areas of human-information interaction, distributed cognition, and learning sciences with the goal of developing and exploring the features of a preliminary framework for thinking about interactivity in the context of ELEs. In this chapter we provide some background and motivation for such a framework, and identify and elaborate upon 10 structural elements of interaction that affect the interactivity of information representations: actual affordances and constraints, articulation mode, control, event granularity, focus, action flow, reaction flow, propagation, transition, and perceived affordances and constraints. Each of these has an effect on the learning and cognitive processes of learners, and the overall interactivity of an ELE is an emergent property of a combination of these elements. Collectively, these elements can serve as a framework to help thinking about design and analysis of interactivity in ELEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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