Digital divide: A critical context for digitally based assessments
Bibliographic record
Abstract
Student learning is increasingly taking place in digital environments both within and outside schooling contexts. Educational assessments are following suit, both to take advantage of the conveniences and opportunities that digital environments provide as well as to reflect the mediums of learning increasingly taking place in societies around the world. A social context relevant to learning and assessment in the digital age is the great differences in access to and competence in technology among students from different segments of societies. Therefore, access and competency in relation to technology become critical contexts for evaluations that rely on digitally based assessments. This chapter examines the digital divide between students from different segments of the society and discusses strategies for minimizing effects of digital divide on assessments of student learning. The research focuses on two types of demographic groups—gender and socioeconomic status (SES) groups—that have been highlighted in research on the digital divide. The research utilizes data from IEA’s International Computer and Information Literacy Study (ICILS) 2013 for Grade 8 students administered in 21 jurisdictions around the world. It thus provides an international perspective on digital divide as an important context for international assessments as well as assessments within jurisdictions such as Mexico that are conducting assessments in digitally based environments.
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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 | 0.029 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.054 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".