The Students’ Error in Using Conjunction (Because, Since, as, in Case) in the Sentences
Bibliographic record
Abstract
This research deals with the students’ error in using conjunction (because, since, as, in case) in the sentences. Conjunction is very important for the learners to develop a skill in grammar. And grammar is the most important part of language for anyone. Should be first understood before being able to construct sentences, rules based on the grammar of language are used to express a certain idea represented in a sentence. By understanding the grammatical rules, the sentences can be arranged to produce the desired meaning. The objective of the study are to find out the students’ difficulties in using subordinate conjunction and to find out the causes of the students’ problems in using subordinate conjunction. Finding of the study expected to provide information for the improvement of teaching conjunction. In addition the findings and description would be of some use for the teachers in teaching conjunction. The outcomes would be useful in minimizing the difficulties in teaching conjunctions. The data of this research were the scores of the students based on the number of items that they hard correctly. A test was administered to collect the data; the research was helped by the classroom English teacher. Based on the value of standard reliability, the result of reliability of the test is 0.97; it means that the value of the standard reliability of test is very good. It was shown that students made more errors working with items numbers 5,6,9,10,14, and 15 the total frequency of errors were 237 all items. It means that the second students of Senior High School, SMK Swasta HKBP Pematangsiantar good errors in using subordinate conjunction (as, in, case, because, since) in the sentence (complex sentences).
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".