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Improving learning process in genetics classroom by using metacognitive strategy
Susantini Endang,Sumitro Sutiman Bambang,Corebima Aloysius Duran,Susilo Herawati 서울대학교 교육연구소 2018 Asia Pacific Education Review Vol.19 No.3
Strategies applied in this study consisted of a metacognitive strategy combined with cooperative learning (MSCL) and one without cooperative learning (MS). Both strategies used the self-understanding and evaluation sheet (SUES). The aims of this study were to investigate the effect of MSCL and MS on the quality of the learning process in genetics classroom. High- and low-ability students were also compared with regard to the effect of both strategies on their academic performance. Four learning process variables were examined: metacognitive skills, collaborative skills, genetics knowledge, and academic achievement. A quasi-experimental research design was used to compare the MSCL (n = 30) and MS (n = 30) groups in which each group consisted of low (n = 15)-ability and high (n = 15)-ability students. Results showed that MSCL group portrayed higher collaborative skills but lower metacognitive skills than MS group. However, both groups had no influences on other variables: genetics knowledge and academic achievements. In addition, high-ability students performed higher metacognitive skills, genetics knowledge, and academic achievements than low-ability students, whereas both of them showed relatively similar collaborative skills. As a suggestion, this study recommends that metacognitive strategy can be done in collaborative designs by using SUES as the authentic assessment.
A Review of Software for Predicting Gene Function
Swee Kuan Loha,Swee Thing Low,Mohd Saberi Mohamad,Safaai Deris,Shahreen Kasim,Choon Yee Wen,Zuwairie Ibrahim,Bambang Susilo,Yusuf Hendrawan,Agustin Krisna Wardani 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.2
A rich resource of information on functional genomics data can be applied to annotating the thousands of unknown gene functions that can be retrieved from most sequenced. High-throughput sequencing can lead to increased understanding of proteins and genes. We can infer networks of functional couplings from direct and indirect interactions. The development of gene function prediction is one of the major recent advances in the bioinformatics fields. These methods explore genomic context by major recent advances in the bioinformatics fields rather than by sequence alignment. This paper reviews software related to predicting gene function. Most of these programs are freely available online. The advantages and disadvantages of each program are stated clearly in order for the reader to understand them in a simple way. Web links to the software are provided as well.
Software for Detecting Gene-Gene Interactions in Genome Wide Association Studies
Ching Lee Koo,Mei Jing Liew,Mohd Saberi Mohamad,Abdul Hakim Mohamed Salleh,Safaai Deris,Zuwairie Ibrahim,Bambang Susilo,Yusuf Hendrawan,Agustin Krisna Wardani 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.4
Nowadays, genome-wide association studies (GWAS) have offered hundreds of thousands of single nucleotide polymorphism (SNPs). The studies of epistatic interactions of SNPs (denoted as gene-gene interactions or epitasis) are particularly important to unravel the genetic basis to complex multifactorial diseases. However, the greatest challenging and unsolved issue in GWAS is to discover epistatic interactions among large amount of SNPs data. Besides, traditional statistical approaches cannot solve such epistasis phenomenon due to possessing high dimensional data and the occurring of multiple polymorphisms. Hence, various kinds of promising software have been extensively investigated in order to solve these problems. This paper gives an overview on the software that had been used to detect gene-gene interactions that bring the effect on common and multifactorial diseases. Furthermore, sources, link, and functions description to the software are provided in this paper as well. Lastly, this paper presents the language implemented, system requirements, strengths, and weaknesses of software that had been widely used in detecting epistatic interactions in complex human diseases.