Pembelajaran Berdiferensiasi pada Jenjang Pendidikan Sekolah Dasar

Learning at elementary school level should be carried out interactively, fun, effectively, and able to motivate students to participate actively, independently, and provide sufficient space for the development of talents, interests and creativity in learning activities. This goes straight with the basic concept of differentiated learning, namely that differentiated learning activities are an attempt to adjust the position of the learning process in the classroom to facilitate the different learning needs of each individual student. Therefore, researchers are interested in conducting a literature review regarding the implementation of differentiated learning at the elementary school level. Based on the results of the views and analysis of the literature review, it can be concluded that (1) the differentiated approach can be combined with several learning models that support differentiated learning (2) the implementation of differentiated learning is able to increase and improve student learning outcomes; (3) the differentiated approach can and is well used at the elementary school level because it is able to accommodate all students' learning needs by taking into account students' interests, talents, profiles, abilities, learning styles

A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.Comment: Accepted by SIGIR 2020. Updated the results of balanced accuracy scores in Table 3 since we found a bug in our source code. Nevertheless, our model still achieves higher balanced accuracy scores than the baselines after we fixed this bu