A study of English learning attitudes and perceptions among senior high school students in Taiwan

This three-phase, sequential mixed methods study explores two aspects of communicative language teaching in Taiwanese senior high schools. Firstly, it examines the extent to which the communicative approach is implemented in the English classroom in Taiwan and secondly it investigates the attitudes of senior high school students towards their learning of English at school. This research study employed the dominant-less dominant mixed method design, with a combination of a dominant qualitative approach and a less-dominant qualitative data aggregation procedure. Results from the focus group interviews are mostly consistent with those of the classroom observations. The findings revealed that the traditional approach, which focuses on the teaching of vocabulary, grammar and the explanation of the textbook contents, still prevailed in the English classroom. Teachers’ classroom practices reflected students’ current learning purpose, which is to achieve good exam results, as revealed in the focus group interviews. The questionnaire survey found that despite their pressing need to “pass exams”, the majority of students had positive attitudes communicative activities in class, believing that the best way of learning English is to be able to use it in real situations outside of the classroom. Nevertheless, students exhibited contradictory attitudes in that they showed inhibitions about speaking or participating actively in class, even though they had the belief that English is best learned through speaking. Finally, the data analysis revealed that some variables, such as “gender” and “major”, played important roles in influencing learner attitudes towards English learning at school. On the other hand, there was little relationship between the variables “programme” and “mother tongue” and learner attitudes in this study

A study of English learning attitudes and perceptions among senior high school students in Taiwan

This three-phase, sequential mixed methods study explores two aspects of communicative language teaching in Taiwanese senior high schools. Firstly, it examines the extent to which the communicative approach is implemented in the English classroom in Taiwan and secondly it investigates the attitudes of senior high school students towards their learning of English at school. This research study employed the dominant-less dominant mixed method design, with a combination of a dominant qualitative approach and a less-dominant qualitative data aggregation procedure. Results from the focus group interviews are mostly consistent with those of the classroom observations. The findings revealed that the traditional approach, which focuses on the teaching of vocabulary, grammar and the explanation of the textbook contents, still prevailed in the English classroom. Teachers’ classroom practices reflected students’ current learning purpose, which is to achieve good exam results, as revealed in the focus group interviews. The questionnaire survey found that despite their pressing need to “pass exams”, the majority of students had positive attitudes communicative activities in class, believing that the best way of learning English is to be able to use it in real situations outside of the classroom. Nevertheless, students exhibited contradictory attitudes in that they showed inhibitions about speaking or participating actively in class, even though they had the belief that English is best learned through speaking. Finally, the data analysis revealed that some variables, such as “gender” and “major”, played important roles in influencing learner attitudes towards English learning at school. On the other hand, there was little relationship between the variables “programme” and “mother tongue” and learner attitudes in this study.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

<p>Abstract</p> <p>Background</p> <p>The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.</p> <p>Results and discussion</p> <p>To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.</p> <p>Conclusion</p> <p>Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.</p