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Record W2116818028 · doi:10.5430/jct.v3n1p63

Text Possession and Teachers’ Pedagogical Practices in the Teaching of Prose Literature-in-English in Some Schools in Ibadan

2014· article· en· W2116818028 on OpenAlexvenueno aff
Francis Ogbonnaya Ezeokoli, Patience Igubor

Bibliographic record

VenueJournal of Curriculum and Teaching · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPossession (linguistics)PsychologyNonprobability samplingMathematics educationPedagogySociologyPopulationLinguistics

Abstract

fetched live from OpenAlex

Performance in school examinations has remained one of the reliable indices of the quality of education in manycountries. For over two decades in Nigeria, students’ performance in most subjects on the school curriculumincluding Literature-in-English has been persistently declining. A number of explanations are offered for thisunsatisfactory situation. Many students experience frustration in their efforts to study Literature due to poorproficiency in the English language as well as non-facilitative methods and strategies adopted by teachers. Studies onthe teaching of Literature further revealed that the focus of research was on such issues as methods, strategies andproblems of teaching Literature in the secondary school. Only a few studies centered on text possession while littleor none seem available on whether the teacher’s methods and pedagogical practices were sensitive to students’ extentof text possession. The study, therefore, investigated the extent of prescribed text possession by Literature-in-Englishstudents as well as whether the level of text possession by students influenced the teacher’s methods and pedagogicalpractices. The descriptive survey research design was adopted in the study. Participants in the study comprised 100Senior Secondary School II teachers of Literature-in-English and their 500 students in Ibadan metropolis. Theparticipants were selected using purposive random sampling techniques. Three instruments used for the collection ofdata were: Questionnaire on Students’ Possession of Prescribed Prose Literature Texts (r= .76), Questionnaire onTeachers’ Organization of the Teaching of Literature (r= .75) and Classroom Observation Schedule for the Teachingof Prose Literature (r= .84). Four research questions were answered. Data analysis involved the use of frequencycounts and percentages. Results revealed that a majority of the students do not possess the prescribed Literature texts(60.2% and 65.5%) for African and non- African novels respectively. Teachers generally adopted the read aloud andexplain method of teaching Literature (61%).This is followed by the teacher assigning chapters to be read from homeand discussed in class (37%). There is also the use of the lecture method (27.18%). Results further indicate that whena majority or all the students possessed the prescribed texts, teachers used read aloud and explain method (43.1%) aswell as the lecture method (25.8%) and discussion method (1.8%). Similarly, when either a few or none of thestudents possessed the prescribed texts, teachers used read aloud and explain methods (36.4%) followed by thelecture method (28%). It was concluded that lack of text possession by the students and teachers’ inflexible use ofmethods in spite of varying contexts of teaching and learning are strong pointers to students declining performance inprose Literature. Government and parents should take realistic measures to provide prescribed texts for studentswhile teachers should be encouraged to use innovative methods that are consistent with the teaching and learningcontexts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.375
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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