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Record W2173808549

When words matter: evaluating the quality of open educational resources through lexicon.

2015· article· en· W2173808549 on OpenAlexaboutno aff
Gabriella Agrusti, Valeria Damiani

Bibliographic record

VenueIris (Roma Tre University) · 2015
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLexiconQuality (philosophy)Computer scienceNatural language processingLinguisticsArtificial intelligenceEpistemologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

There is a general agreement on the advantages of open education in e-learning in terms of inclusiveness and equity (Wiley, 2010). However, there is a relatively less developed debate about the quality evaluation of open educational resources (OERs), accessible for free and without spatial or time limits. Usually the OERs’ quality is assessed on structural features, i.e. accessibility, usability, learning goals, possibility for the learner to assess his/her progress during learning (McGill et al. 2013; Ehlers & Joosten, 2009). When it comes to the content of OERs, this is considered mainly at the macro-level (i.e. text cohesion and coherence, cultural differences among potential users, gender differences), and not at the micro-level of its lexical components. Conversely, one of the most debated issues within the quality of education, both traditional face-to-face education and distance technologyenhanced education, is on how the educational mediation takes place in order to develop transversal competences and basic skills linked to the use of language (Marconi, 1997). W. Nagy, expert in vocabulary development, asks, “Which words should a teacher teach?” (2011) and, more in general, then he considers how, irrespectively of the disciplinary contents, words that compose the educational
\nmessage do have an intrinsic value and should descend from an intentional choice in the instructional design phase.
\nMeasures related to text readability are generally based on the frequency of a word in a corpus of sufficiently wide dimensions. Texts with rare words are more difficult to understand than those that contain common words. However, the emphasis on the use of these tools to study the adequacy of textbooks and learning materials has been criticized (Davison - Green, 1988), as not always common words are easy to define (e.g. the article “the” it is very common but with difficult to define) whereas rare words in a written texts can be easier as, for instance, they are common in the spoken language (e.g. “t-shirt” or “fireman”). Building a structure of semantic relationships between words (similarities/oppositions, inclusion/exclusion, semantic fields) is instead one of the ways to help memorization, and it makes likely the passage from receptive to productive lexicon in learners. On these premises, it can be envisaged a set of criteria to evaluate the appropriateness of an OER text with respect to its learning goals.
\nThe paper discusses the assumptions to evaluate the lexical aspects of OERs’ instructional messages, and presents the first results obtained from an exploratory study carried out on a set of OERs’ in Italian language on a variety of contents.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.142
GPT teacher head0.370
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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