Pengaruh motivasi belajar dan self efficacy terhadap prestasi belajar siswa pada mata pelajaran Matematika kelas VII SMP Negeri I Rengel Tuban

Tujuan penelitian ini adalah ingin mengetahui apakah ada pengaruh antara motivasi belajar dan Self Efficacy terhadap prestasi belajar siswa pada mata pelajaran matematika, apakah ada pengaruh motivasi belajar terhadap prestasi belajar siswa pada mata pelajaran matematika dan apakah ada pengaruh self efficacy terhadap prestasi belajar siswa pada mata pelajaran matematika. Dengan pendekatan Kuantitatif-Correlation, metode penelitian ini akan diperoleh signifikansi pengaruh antara variabel yang diteliti. Sebagai subyek penelitian adalah siswa-siswi SMP Negeri 1 Rengel Tuban dengan tehnik pengambilan Simple Random Sampling. clitetapkan sebanyak 126 orang.sebagai sampel. Untuk membuktikan hipotesis penelitian digunakan regresi linier ganda. Hasil data matriks interkorelasi menunjukkan babwa variabel motivasi belajar (X 1) dengan variabel prestasi belajar siswa pada mata pelajaran matematika (Y) diperoleh harga korelasi r tabel sebesar = 0,255 dengan p: 0,799 yang berarti bahwa tidak ada pengaruh yang signifikan antara motivasi belajar dengan prestasi belajar siswa pada mata pelajaran matematika. Sedangkan variabel Self Efficacy (X2) dengan variabel prestasi belajar siswa pada mata pelajaran matematika (Y) diperoleh harga korelasi r tabel sebesar = 0,432 dengan p: 0,667 yang. berarti bahwa tidak ada pengaruh yang sangat signifikan antara Self Efficacy dengan prestasi belajar siswa pada mata pelajaran matematika. Dari hasil analisis regresi, Pada tabel Model Summary diperoleh hasil R Square (koefisien determinansi) sebesar 0.002 yang. berarti bahwa banya 0,2% variabel Prestasi Belajar Matematika dipengaruhi/dijelaskan oleh variabel motivasi belajar dan Self Efficacy. Sisanya sebesar 99.8% dipengaruhi oleh variabel lain. Pada tabel ANOVA dapat diperoleh F hitung 0.120, dengan tingkat signifikansi 0.887 > 0.05 berarti model regresi yang diperoleh nantinya tidak dapat digunakan untuk memprediksi Prestasi Belajar Matematika. Berdasarkan pada besarnya pengaruh variabel Motivasi belajar dan Self Efficacy terhadap nilai Prestasi Belajar Matematika menandaskan bahwa kedua variabel tersebut tidak cukup kuat untuk memprediksi prestasi belajar matematika siswa kelas VII SMP Negeri 1 Rengel Tuban. Untuk peneliti selanjutnya bisa melanjutkan dengan judul yang serupa, hal ini dikarenakan hasil dari beberapa penelitian sejenis masih bersifat kontroversial. Dan bisa juga untuk melanjutkan penelitian sejenis dengan mengungkap variabel lain sebagai variable prediktor (variabel bebas) yang mempengaruhi prestasi belajar pada siswa. Maka apabila peneliti bermaksud mengadakan replikasi terhadap penelitian ini hendaknya memperhatikan hal-hal tersebut untuk mencapai kesempurnaan

Genetic parameters for eight microsatellite loci in <i>Rhinopithecus brelichi</i>.

<p>N_a  =  no. of alleles; N_e  =  no. of effective alleles; H_o  =  observed heterozygosity; H_e  =  expected heterozygosity; F_is  =  fixation index; * = p<0.05.</p

List of microsatellite loci.

<p>T_a: annealing temperature.</p

Scenario explaining discrepancies between mtDNA and nDNA diversity in <i>R. brelichi</i>.

<p>In historical times, <i>R. brelichi</i> was distributed over a larger area than today comprising several subpopulations or demes (A–G) with respective different mtDNA haplogroup assemblages. Due to male migration between these demes, nDNA was transferred between them and equalized nDNA diversity among demes, but not so for mtDNA. After partial habitat and population loss in this example only one deme survived (E; dashed circles), containing just a subset of the original mtDNA haplotypes but almost all nDNA diversity. Thus, mtDNA diversity was strongly reduced while nDNA diversity remained comparatively high.</p

Geographical position of FNNR (marked by a black dot) in Guizhou Province, China (A) and a sketch of FNNR (B).

<p>Collection of samples for genetic analyses was carried out in the gray region around the Yangaoping field station.</p

Allele frequency distribution for eight microsatellite loci in <i>Rhinopithecus brelichi</i> (n = 141 individuals).

<p>Bars represent the proportion of alleles found in each allele frequency class. The distribution is L-shaped, as expected for a stable population under mutation-drift equilibrium, thus not indicating a recent bottleneck.</p

Expected (H_e) and observed (H_o) heterozygosity across six loci for <i>Rhinopithecus brelichi</i> and <i>R. bieti</i> (data for <i>R. bieti</i> from Liu et al. [46]).

<p>Expected (H_e) and observed (H_o) heterozygosity across six loci for <i>Rhinopithecus brelichi</i> and <i>R. bieti</i> (data for <i>R. bieti</i> from Liu et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073647#pone.0073647-Liu1" target="_blank">[46]</a>).</p

Ethical Statement;Material and methods;Suppl Tables;Suppl Figure from Inverted intergeneric introgression between critically endangered kipunjis and yellow baboons in two disjunct populations

Additional information on Ethical Statement;Additional information on Material and methods. Sample Collection; Laboratory methods; Phylogenetic analysis; References;Suppl. Table 1 List of samples. ID, clade, geographic provenance and accession numbers; Suppl. Table 2. Estimated divergence ages of mtDNA lineages in million years (Ma); Suppl. Table 3. Results from the Kishino-Hasegawa (KH) and Shimodaira-Hasegawa (SH) tests;Suppl Fig 1 is showing an uncollapsed chronogram showing phylogenetic relationships and divergence times among various baboon and kipunji mtDNA lineage

Bayesian Skyline Plot (BSP) displaying changes in female effective population size (N_ef) through time in <i>R. brelichi</i>.

<p>Calculations are based on 603 bp of the mitochondrial HVI region. Shown are the median (black) and the 95% highest posterior probability density (dashed lines) around the estimate. The arrow indicates the start of a reduction in N_ef.</p

A Mitogenomic Phylogeny of Living Primates

<div><p>Primates, the mammalian order including our own species, comprise 480 species in 78 genera. Thus, they represent the third largest of the 18 orders of eutherian mammals. Although recent phylogenetic studies on primates are increasingly built on molecular datasets, most of these studies have focused on taxonomic subgroups within the order. Complete mitochondrial (mt) genomes have proven to be extremely useful in deciphering within-order relationships even up to deep nodes. Using 454 sequencing, we sequenced 32 new complete mt genomes adding 20 previously not represented genera to the phylogenetic reconstruction of the primate tree. With 13 new sequences, the number of complete mt genomes within the parvorder Platyrrhini was widely extended, resulting in a largely resolved branching pattern among New World monkey families. We added 10 new Strepsirrhini mt genomes to the 15 previously available ones, thus almost doubling the number of mt genomes within this clade. Our data allow precise date estimates of all nodes and offer new insights into primate evolution. One major result is a relatively young date for the most recent common ancestor of all living primates which was estimated to 66-69 million years ago, suggesting that the divergence of extant primates started close to the K/T-boundary. Although some relationships remain unclear, the large number of mt genomes used allowed us to reconstruct a robust primate phylogeny which is largely in agreement with previous publications. Finally, we show that mt genomes are a useful tool for resolving primate phylogenetic relationships on various taxonomic levels.</p></div