Digital divide: A critical context for digitally based assessments
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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