ANALISIS KECAKAPAN MATEMATIS MAHASISWA PADA MATA KULIAH STATISTIKA-1 DENGAN PEMBELAJARAN KOLABORATIF BERBASIS MASALAH
Abstract
Salah satu kompetensi yang harus dimiliki oleh seorang pendidik profesional adalah kompetensi profesional yang berkaitan dengan penguasaan terhadap materi pembelajaran secara luas dan mendalam. Mata kuliah statistika 1 merupakan mata kuliah fundamental yang menjadi dasar bagi mata kuliah statistika yang lain. Oleh karena itu, Statistika 1 diambil sebagai obyek dalam pelaksanaan Lesson Study untuk semester ganjil tahun akademik 2013/2014.
Agar sukses dalam belajar matematika maka seseorang harus memiliki kecakapan matematika. Kecakapan Matematika (Mathematics Proficiency) menurut Kilpatrick (2001) terdiri dari (1) pemahaman konseptual (conceptual understanding), (2) kelancaran prosedural (procedural fluency), (3) kompetensi strategis (strategic competence), (4) penalaran adaptif (adaptive reasoning) dan (5) disposisi produktif (productive disposition). Sementara itu, pembelajaran Statistika 1 biasanya dilaksanakan dengan berpusat pada siswa (student centered learning) yang biasanya hanya memfokuskan pada kelancaran prosedural dan kompetensi strategis.
Oleh karena itu, tim MK Statistika 1 menerapkan model pembelajaran kolaboratif berbasis masalah yang dilaksanakan sebagai bagian dari kegiatan Lesson Study yang diharapkan dapat mengembangkan seluruh bagian dari kecakapan matematis tersebut secara terpadu. Kegiatan LS dilaksanakan selama 4 siklus dan setiap siklusnya dibagi atas plan, do dan see. Instrumen yang digunakan yaitu lembar observasi pembelajaran, lembar pengamatan kecakapan matematis mahasiswa. Selain itu, seluruh proses pembelajaran direkam dengan kamera video. Berdasarkan hasil analisa didapatkan bahwa pembelajaran mata kuliah 1 dengan pembelajaran kolaboratif berbasis masalah dilakukan dengan tahapan; 1). Fase 1: membagi tugas, 2). Fase 2: Pembentukan kelompok, 3). Fase 3:Diskusi kelompok, 4). Presentasi kelas. Sedangkan kecakapan matematis mahasiswa secara garis besar meningkat dari sklus yang satu ke siklus yang lain kecuali dari siklus yang ke-2 ke siklus yang ke-3. Desain masalah yang diajukan sangat mempengaruhi bagaimana kecakapan matematis dapat dimunculkan dalam pembelajaran di dalam kelas.
Downloads
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.