Development Of Interactive Media For English Learning

The purpose of this development is to produce an English Language Learning Interactive Media. The selection of this model is based on the consideration that this model is easy to understand, developed systematically, and is based on the theoretical foundation of the developed learning design. The development process involves (1) learning design experts (2) content experts in the field of study (3) small group trials to provide feedback and input for improvements. The results of this development research are that this interactive media product has a material feasibility level of 90%, learning design feasibility is 94% and small group trials are 90% with very decent qualifications and does not need to be revised. Based on the results of this study, it was concluded that using Interactive Learning Media can improve English learning outcomes. The implication of this research is that Learning Interactive Media can be used as a way to improve English learning outcomes

Pengaruh Model Problem Based Learning Versus Direct Learning dan Motivasi Belajar terhadap Hasil Belajar Mata Pelajaran Sejarah Siswa SMA Kelas XI

Tujuan dari penelitian ini adalah untuk (1) untuk mengetahui ada perbedaan hasil belajar antara yang menggunakan model PBL dan Direct Learning, (2) untuk mengetahui ada perbedaan hasil belajar siswa yang mempunyai motivasi berbeda (3) untuk mengetahui ada interaksi antara model Problem Based Learning dan motivasi belajar terhadap hasil belajar pada siswa kelas XI SMA Muhammadiyah 3 Surabaya dan Siswa Kelas XI SMA Muhammadiyah 2 Surabaya.           Penelitian ini menggunakan rancangan kuasi eksperimental faktorial 2X2. Data-data penelitian dikumpulkan dengan menggunakan metode angket dan metode tes. Kemudian data tersebut dianalisis dengan menggunakan teknik analisis statistik ANAVA dua jalur. Populasi penelitian adalah seluruh siswa kelas XI SMA Muhammadiyah 3 Surabaya dan Siswa Kelas XI SMA Muhammadiyah 2 Surabaya. Dalam penelitian ini instrumen yang digunakan adalah angket belajar dan tes hasil belajar sejarah. Metode pengumpulan data adalah teknik atau cara-cara yang dapat dilakukan oleh peneliti untuk mengumpulkan data. Teknik analisis data dalam penelitian kuantitatif menggunakan statistik, dalam hal ini adalah teknik Analisis Varians (ANAVA) dua jalur.           Berdasarkan penelitian didapatkan hasil sebagai berikut (1) Terdapat perbedaan pengaruh antara Model Problem Based Learning (PBL) dengan Direct Learning terhadap hasil belajar Sejarah siswa. Pembelajaran Sejarah dengan menggunakan Model Problem Based Learning (PBL) menghasilkan hasil belajar Sejarah siswa lebih baik dibandingkan dengan Direct Learning, (2) Terdapat perbedaan hasil belajar Sejarah siswa yang memiliki Motivasi Belajar tinggi dengan siswa yang memiliki Motivasi Belajar rendah. Siswa-siswa yang mempunyai tingkat Motivasi Belajar tinggi menghasilkan prestasi belajar Sejarah lebih baik dibandingkan dengan siswa-siswa yang mempunyai tingkat Motivasi Belajar rendah, dan (3) Terdapat interaksi antara model pembelajaran dan Motivasi Belajar terhadap hasil belajar Sejarah. Pembelajaran Sejarah dengan menggunakan Model Problem Based Learning (PBL), siswa-siswa yang mempunyai tingkat Motivasi Belajar tinggi mempunyai hasil belajar Sejarah lebih baik dibandingkan siswa-siswa yang mempunyai tingkat Motivasi Belajar rendah. Berdasarkan hasil penelitian tersebut diperoleh kesimpulan bahwa dengan menggunakan Model Pembelajaran Problem Based Learning (PBL) dan motivasi Belajar dapat meningkatkan hasil belajar sejarah siswa kelas XI SMA Muhammadiyah 3 Surabaya dan Siswa Kelas XI SMA Muhammadiyah 2 Surabaya

A SVM-based method to classify RBM20 affected and not affected exons

Mutations of RNA binding motif protein 20 (RBM20) have been recently reported to cause Human dilated cardiomyopathy (DCM) (Brauch et al., 2009, Li et al., 2010). DCM is the major cause of heart failure and mortality around the world (Jefferies and Towbin, 2010). Overall, 25\u201350% of DCM cases are familiar and causative mutations which have been described in more than 50 genes encoding mostly for structural components of cardiomyocytes. RBM20 belongs to the family of the SR and SR-related RNA binding proteins which assemble in the spliceosome taking part in the splicing of pre-mRNA. RBM20 is mainly expressed in striated muscle, with the highest levels in the heart (Guo et al., 2012). Due to its involvement in DCM, RBM20 was studied a lot to unveil its mechanism of action and its RNA targets (Guo et al., 2012, Li et al., 2013). Guo and colleagues reported a set of 31 genes showing a RBM20 dependent splicing from a whole transcriptome analysis in rats and humans (Guo et al., 2012). More recently, Maatz and colleagues reported an additional set of 18 rat genes and observed that RNA sequences recognized by RBM20 are likely to be located in the 400 nucleotides flanking the exons whose alternative splicing is regulated by RBM20 (Maatz et al., 2014). However, both the suggested RNA sequence which is recognized by RBM20 and its over-representation over the flanking regions of affected exons remain poor predictors to target genes presenting splicing events regulated by RBM20. The aim of this work was, thus, to characterize, through a bioinformatic approach, the sequence motifs of the exons whose alternative splicing was affected by RBM20, in order to ameliorate the prediction of the genes (exons) affected by RBM20. A differential expression analysis was performed to select the dataset of RBM20 affected exons; a further dataset was retrieved from literature data (Maatz et al., 2014). A Support Vector Machine (SVM) approach evaluating more kinds of genetic elements binding in the flanking regions of our target exons was used. A SVM method was chose to classify RBM20 affected and not affected exons, but other machine learning algorithms could have been used as well; however, SVM is among the most commonly used ones. From the analyses, our model resulted to well discriminate RBM20 affected from not affected exons. From a biological and functional point of view, this approach helps us to target novel candidate genes associated to diseases depending on a dysregulation of RBM20. This study provided additional information about RBM20 regulation of target exons, based not only on the RNA binding site, but also on other genetic elements associated to the binding site. Furthermore, we proposed the first model based on a SVM algorithm for the classification of RBM20 affected and not affected exons