Penerapan Behavioristik Problem Based Learning Papan Pintar Dalam Meningkatkan Motivasi Belajar Matematika
Penerapan Behavioristik Problem Based Learning Papan Pintar Dalam Meningkatkan Motivasi Belajar Matematika
Abstract
Penerapan teori belajar behavioristik dengan model pembelajaran problem based learning melalui bantuan media papan pintar guna meningkatkan motivasi belajar mata peajaran matematika di kelas IV UPT SD Negeri 73 Gresik. Tujuan penelitian ini adalah umtuk meningkatkan motivasi belajar matematika peserta didik kelas IV UPT SD Negeri 73 Gresik dengan pendekatan teori belajar behavioristik melalui penerapan model pembelajaran problem based learning (PBL) dengan bantuan media papan pintar. Penelitian ini menggunakan metode kuantitatif dengan desain penelitian pre-test dan post-test. Sampel penelitian ini berjumlah 20 peserta didik kelas IV UPT SD Negeri 73 Gresik yang dikumpulkan melalui teknik penelitian purposive sampling . Data diperoleh melalui mekanisme tes dan observasi melalui metode kualitatif. Hasil menunjukkan bahwa teori belajar behavioristik dengan model pembelajaran PBL melalui bantuan media papan pintar dapat meningkatkan motivasi peserta didik kelas IV UPT SD Negeri 73 Gresik untuk belajar mata pelajaran matematika. Nilai rata-rata post-test 81,00 dibandingkan dengan nilai pre-test yaitu 54,00, nilai pre-test dan post-test menunjukkan perbedaan jumlah yang signifikan, dengan nilai p 0,000. Berdasarkan hasil pemaparan sebelumnya, maka dapat ditarik suatu kesimpulan jika pembelajaran yang menggunakan pendekatan teori belajar behavioristik dengan model problem based learning melalui media papan pintar dapat meningkatkan motivasi peserta didik dalam pembelajaran matematika.
Downloads
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International 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.
