The Correlation Between Content Schemata and Reading Comprehension on Expository Text of The Second Year Students at MAN 2 Jombang
DOI:
https://doi.org/10.30587/didaktika.v28i1.3807Keywords:
correlation, content schemata, expository textAbstract
The English language has four skills that must be mastered. In this case, reading is one of them whether the learner can understand the symbol of printed words or not. On the other hand, if they have a good ability in reading, they will have a better chance to succeed in their study. This research is quantitative, it is focused on the product (the result of the test) not the process of teaching-learning, and the objective is to find out the correlation between students' schemata and their reading comprehension. This correlation is Pearson Correlation because the kind of data correlation is ordinal and interval. The total sample is 83 students, and the significance level is 5% or 0,05 r table for 83 students is 0.213. It is higher than the r table. So, it can be compared that rxy is higher than rtable 0.244 > 0.213. it can be concluded that Ha (Alternative hypothesis) is accepted and H0 is rejected, so the data is Positive correlation. The English teacher should open the class by pre-reading activities to reinforce students' schemata and the students should have good critical thinking to develop their reading comprehension of several types of text, especially narrative text.
Downloads
Published
How to Cite
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with DIDAKTIKA: Jurnal Pemikiran Pendidikan agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
