Model Pembelajaran Learning Together
Shanae Maerz
Learning Together constituted as a model of learning in which this model lesson gives students the opportunity to be able to work together with other students in a structured task. The purpose of this research ia to examines the differences of students learning outcomes by using cooperative learning model of the type of learning together with the students convensional learning model, as well as to find out how considerable the potential result of student learning. This research is true eksperimental research. The method of this research is true eksperiment using a Randomized Post Test Only Control Group Design. The technics of data analysis using Independent Sample T-Test with the help of SPSS version 16. The result of the analysis showed: there is significant difference in result of student learning by using cooperative learning model of the type of learning together with concept mapping to convensional learning type. The potential result of student learning by using cooperative learning model of the type learning together with concept mapping is higher compared to the potential result of student learning by using convensional model.
Penelitian ini bertujuan untuk mengetahui pengaruh model pembelajaran Learning Together (LT) berbantuan media monopoli terhadap hasil belajar matematika siswa kelas IV SD N 2 Pangenrejo, Purworejo.Penelitian ini merupakan jenis penelitian pre eksperimen. Subjek penelitian dipilih secara Sampling Jenuh. Sampel yang diambil sebanyak 31 siswa. Metode pengumpulan data dilakukan dengan menggunakan tes. Uji validitas instrumen tes menggunakan teknik korelasi product moment Uji prasyarat analisis terdiri dari uji normalitas dan uji homogenitas. Analisis data menggunakan teknik statistik Wilcoxon. Hasil penelitian menunjukkan bahwa model pembelajaran Learning Together (LT) berbantuan media monopoli berpengaruh positif terhadap hasil belajar matematika. Hal ini dibuktikan dari hasil analisis uji Wilcoxon pada hasil pretest dan posttest. Berdasarkan hasil analisis dan pembahasan, diperoleh nilai Z sebesar -2,038 dengan Asymp. Sig 2 tailed sebesar 0,042 (0,042
Mengamati kecenderungan siswa akan sulitnya dan rendahnya minat belajar siswa terhadap mata pelajaran matematika menjadikan nilai hasil evaluasi yang mereka perolepun sangat rendah. Hal ini membuat siswa mengalami kecenderungan belajar dengan sikap yang pasif. Dengan demikian akan berpengaruh terhadap hasil belajar dan prestasi belajar yang diperoleh masing-masing siswa pun cenderung rendah. Untuk mengatasi permasalahan tersebut diperlukan cara efektif dalam memilih model pembelajaran seperti model pembelajaran Learning Together. Model pembelajaran Learning Together ini merupakan salah satu cara yang mampu melatih siswa untuk dapat mengembangkan disiplin intelektual dan keterampilan berpikir dengan memunculkan pertanyaan-pertanyaan atas dasar rasa ingin tahu mereka sendiri sehingga mereka dapat menemukan jawaban-jawaban dari permasalahan yang mereka hadapi. Dengan demikian perlu dilakukan suatu penelitian untuk meningkatkan prestasi belajar dengan model pembelajaran Learning Together dalam proses pelaksanaan pembelajaran mata pelajaran Matematika di kelas X MIPA 3 di SMA Negeri 2 Cikarang Utara. Instumen yang digunakan yaitu berupa test kognitif yang telah melalui beberapa tahapan seperti judgement, validitas, realibiltas dengan berdasarkan pada tingkat kesukaran dan daya pembeda. Dan sebagai tujuan akhir yaitu hasil yang dicapai dari penelitian ini menunjukan suatu peningkatan prestasi belajar siswa dalam pengetahuan dan keterampilan setelah diterapkannya model pembelajaran Learning Together.
Supervised learning algorithms train on sample data that specifies both the algorithm's input and output. For example, the data could be images of handwritten numbers that are annotated to indicate which numbers they represent. Given sufficient labeled data, the supervised learning system would eventually recognize the clusters of pixels and shapes associated with each handwritten number.
In contrast, unsupervised learning algorithms train on unlabeled data. They scan through new data and establish meaningful connections between the unknown input and predetermined outputs. For instance, unsupervised learning algorithms could group news articles from different news sites into common categories like sports and crime.
In machine learning, you teach a computer to make predictions, or inferences. First, you use an algorithm and example data to train a model. Then, you integrate your model into your application to generate inferences in real time and at scale. Supervised and unsupervised learning are two distinct categories of algorithms.
In supervised learning, you train the model with a set of input data and a corresponding set of paired labeled output data. The labeling is typically done manually. Next are some types of supervised machine learning techniques.
Logistic regression predicts a categorical output based on one or more inputs. Binary classification is when the output fits into one of two categories, such as yes or no and pass or fail. Multiple class classification is when the output fits into more than two categories, such as cat, dog, or rabbit. An example of logistic regression is predicting whether a student will pass or fail a unit based on their number of logins to the courseware.
A neural network solution is a more complex supervised learning technique. To produce a given outcome, it takes some given inputs and performs one or more layers of mathematical transformation based on adjusting data weightings. An example of a neural network technique is predicting a digit from a handwritten image.
Unsupervised machine learning is when you give the algorithm input data without any labeled output data. Then, on its own, the algorithm identifies patterns and relationships in and between the data . Next are some types of unsupervised learning techniques.
The clustering unsupervised learning technique groups certain data inputs together, so they may be categorized as a whole. There are various types of clustering algorithms depending on the input data. An example of clustering is identifying different types of network traffic to predict potential security incidents.
Association rule learning techniques uncover rule-based relationships between inputs in a dataset. For example, the Apriori algorithm conducts market basket analysis to identify rules like coffee and milk often being purchased together.
You can use supervised learning techniques to solve problems with known outcomes and that have labeled data available. Examples include email spam classification, image recognition, and stock price predictions based on known historical data.
You can use unsupervised learning for scenarios where the data is unlabeled and the objective is to discover patterns, group similar instances, or detect anomalies. You can also use it for exploratory tasks where labeled data is absent. Examples include organizing large data archives, building recommendation systems, and grouping customers based on their purchasing behaviors.
When applying categories to a large document base, there may be too many documents to physically label. For example, these could be countless reports, transcripts, or specifications. Training on the unlabeled data to begin with helps identify similar documents for labeling.
Amazon Web Services (AWS) offers a wide range of offerings to help you with supervised, unsupervised, and semi-supervised machine learning (ML). You can build, run, and integrate solutions of any size, complexity, or use case.
Amazon SageMaker is a complete platform to build your ML solutions from the ground up. SageMaker has a full suite of prebuilt supervised and unsupervised learning models, storage and compute capabilities, and a fully managed environment.
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