Content Analytics: The Definition, Scope, and an Overview of Published Research
Bibliographic record
Abstract
With the large amounts of data related to student learning being collected by digital systems, the potential for using this data for improving learning processes educational researchers, practitioners, administrators, and others interested in the intersection of technology and education and the use of this vast amount of data for improving learning and teaching (Buckingham Shum & Ferguson, 2012).Among the different types of data, the analysis of learning content is commonly used for the development of learning analytics systems (Buckingham Shum & Ferguson, 2012; Chatti, Ferguson & Buckingham Shum, 2012).These include various forms of data produced by instructors (course syllabi, documents, lecture recordings), publishers social media postings).In this chapter, we introduce content analytics, an umbrella term used to refer to different types of learning analytics focusing on the analysis of various forms of learning content.We the content analytics domain, identifying potential shortcomings and directions for future studies.We begin by discussing different forms of learning conanalytics.Special attention is given to the range of problems commonly addressed by content analytics, as well as to various methodological approaches, tools, and techniques.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".