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Record W2285912668 · doi:10.5539/elt.v9n3p235

A Needs Analysis for a Discipline-Specific Reading Intervention

2016· article· en· W2285912668 on OpenAlexvenueno aff
Naomi Adjoa Nana Yeboah Boakye

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)VocabularyReading comprehensionClass (philosophy)Intervention (counseling)CognitionComprehensionMathematics educationVariety (cybernetics)Sociology of EducationPedagogyLinguisticsComputer science

Abstract

fetched live from OpenAlex

<p class="Normal1">This paper reports on a needs analysis that sought to explore students’ reading challenges as an initial step in designing an appropriate reading intervention programme for first-year Sociology students. The aim of the paper is to suggest conditions for the production of an effective reading intervention programme by determining the needs of the students in the first-year Sociology class. A survey using an open-ended questionnaire was used to explore students’ reading challenges. The responses were analysed using content analysis. The analysis showed a variety of learner needs and revealed that most of the students have difficulty in reading their first-year Sociology texts. Comprehension was the main challenge, but other specific areas such as vocabulary, length of texts, language, and affective issues such as motivation and interest were also mentioned. The findings show that this cohort of first-year Sociology students had reading challenges that involve cognitive, language and affective issues. Based on the results of the needs analysis an intervention programme that addresses cognitive, language and affective issues is recommended for this cohort of students.</p>

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.345
Teacher spread0.323 · 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 designQualitative
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

Citations20
Published2016
Admission routes1
Has abstractyes

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