scholarly journals Time advice and learning questions in computer simulations

2011 ◽  
Vol 27 (3) ◽  
Author(s):  
Günter Daniel Rey
Keyword(s):  
Computer Simulation ◽  
Regression Analysis ◽  
Factorial Design ◽  
Linear Model ◽  
Computer Simulations ◽  
Analysis Of Variance ◽  
Cognitive Theory ◽  
Corrective Feedback ◽  
General Linear Model ◽  
Design Time

<span>Students (N = 101) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without time advice) x 3 (with learning questions and corrective feedback, with learning questions without feedback or without learning questions) between subjects factorial design. Time spent with the simulation as well as retention and transfer tests were used as dependent measures. Neither the time advice presented immediately before students chose to finish the simulation nor the learning questions presented during the simulation significantly improves learners' retention or transfer performances. Students who were asked to employ more time on the computer simulation or who received learning questions with corrective feedback spent significantly more time with the simulation than did students for whom the time advice or the learning questions were absent. The results were discussed on the basis of the </span><em>cognitive theory of multimedia learning</em><span> and the </span><em>cognitive-affective theory of learning with media</em><span>, as well as in conjunction with adaptive computer simulations.</span>

scholarly journals Instructional advice, time advice and learning questions in computer simulations

2010 ◽  
Vol 26 (5) ◽  
Author(s):  
Günter Daniel Rey
Keyword(s):  
Computer Simulation ◽  
Regression Analysis ◽  
Factorial Design ◽  
Linear Model ◽  
Computer Simulations ◽  
Analysis Of Variance ◽  
Undergraduate Students ◽  
Scientific Discovery ◽  
General Linear Model ◽  
Design Time

<span>Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2 (with or without learning questions) between subjects factorial design. Time spent with the simulation as well as retention and transfer tests were used as dependent measures. Neither the instructional advice to examine the different parameters in a simulation systematically presented immediately before the simulation nor the learning questions (without feedback) presented during the simulation improves learners' retention or transfer performances. Students who were asked to employ more time on the computer simulation immediately before they want to finish it spent considerably more time with the simulation and performed better on retention, but not on transfer than did students for whom this request was absent. The results were discussed on the basis of the extended </span><em>Scientific Discovery as Dual Search</em><span> model and in conjunction with adaptive computer simulations.</span>

The relationships between performance traits and the bGH/Alu I and Pit-1/Hinf I polymorphisms in Holstein cows

2017 ◽  
Author(s):  
Memis Ozdemir ◽  
Mehmet Topal ◽  
Vecihi Aksakal
Keyword(s):  
Linear Model ◽  
Genetic Markers ◽  
Analysis Of Variance ◽  
Genetic Selection ◽  
General Linear Model ◽  
Holstein Cows ◽  
Production Traits ◽  
Milk Traits ◽  
Reproduction Traits ◽  
General Linear Model Procedure

Progress in genetic selection in livestock can be increased by marker asisted selection. The identification of favorable genetic markers is one of the most important stages in marker-asisted selection. In this study, it was aimed to determine the effects of the bGH/AluI and Pit-1/HinfI polymorphisms on the production traits of organic reared cows. Genotyping was performed on total 245 Holstein cows, n=181 for Pit-1 gene and n=186 for bGH gene. Milk yields and some reproduction traits analyzed by analysis of variance using the general linear model procedure, and 703 production records from cows were used to. The results showed that neither the Pit-1/Hinf I nor bGH/Alu I polymorphisms affect the tested milk traits (p>0.05).

Music Listening Preferences in Early Life

2009 ◽  
Vol 56 (4) ◽  
pp. 357-369 ◽  
Author(s):  
Beatriz Ilari ◽  
Megha Sundara
Keyword(s):  
Linear Model ◽  
Analysis Of Variance ◽  
Early Life ◽  
Age Groups ◽  
General Linear Model ◽  
Music Listening ◽  
General Linear Model Analysis ◽  
Gender And Age ◽  
Within Subjects ◽  
Instrumental Accompaniment

This study investigated infant listening preferences for two versions of an unfamiliar Chinese children's song: unaccompanied (i.e., voice only) and accompanied (i.e., voice and instrumental accompaniment). Three groups of 5-, 8- and 11-month-old infants were tested using the Headturn Preference Procedure. A general linear model analysis of variance was carried out with gender and age as the between-subjects variables and listening time to the two renditions (unaccompanied, accompanied) as the within-subjects variable. Results indicated a clear preference for the unaccompanied version of the song in all age groups.

scholarly journals Meningkatkan Kemampuan Repesentasi Matematis Siswa Melalui Strategi Solusi Pada Materi Pecahan di Kelas VII SMPN 1 Sungai Kunyit

2017 ◽  
Author(s):  
Resy Nirawati
Keyword(s):  
Factorial Design ◽  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Post Test

penelitian ini adalah mengeksplanasi kemampuan representasi siswa melalui strategi solusi. Bentuk penelitian ini adalah eksperimen, dengan menggunakan rancangan Factorial Design 3x3. Data yang diperoleh dari hasil belajar siswa berupa skor siswa setelah diajarkan menggunakan strategi solusi berupa garis bilangan, diagram dan tabel pembagian bilangan prima. Perbedaan skor tersebut diuji dengan Anova Dua Jalur menggunakan program SPSS for window versi 17.0. Dari hasil analisis data diperoleh skor rata-rata pre-test adalah 8,25 dan skor rata-rata post-test adalah 12,75 (rentangan skor 0 sampai dengan 16). Melalui uji normalitas data diketahui bahwa kedua data berdistribusi normal, dilanjutkan dengan uji pengaruh perlakuan diperoleh thitung (10,81) &gt; ttabel (2,05), maka Ho ditolak, atau terdapat perbedaan yang berarti (signifikan) antara hasil pre-test dan post-test. Jadi dapat disimpulkan terjadi peningkatan kemampuan siswa dalam menguasai materi membandingkan dan mengurutkan pecahan. Kemudian dilanjutkan lagi dengan Analisis General Linear Model-Univariate-Factor, dari tabel hasil Analisis General Linear Model-Univariate-Factor diperoleh bahwa nilai α = 0,05, Fhitung = 14,591 &gt; Ftabel = 3,156 maka Ho ditolak, atau terdapat perbedaan yang signifikan dari hasil belajar siswa pada materi membandingkan dan mengurutkan pecahan yang diajarkan dengan menggunakan strategi solusi berupa garis bilangan, diagram dan tabel pembagian bilangan prima.

scholarly journals Multivariate Analysis of Variance (MANOVA) untuk Memperkaya Hasil Penelitian Pendidikan

AKSIOMA ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Sutrisno Sutrisno ◽  
Dewi Wulandari
Keyword(s):  
Multivariate Analysis ◽  
Linear Model ◽  
Analysis Of Variance ◽  
General Linear Model ◽  
General Linear ◽  
Multivariate Analysis Of Variance ◽  
Wilks Lambda ◽  
Post Hoc ◽  
One Way Anova

MANOVA merupakan solusi teknik analisis data kuantitatif bagi peneliti di dunia pendidikan yang ingin mengamati hasil belajar peserta didik dalam rangka menerapkan prinsip kebulatan dalam Kurikulum 2013 (prinsip evaluasi hasil belajar meliputi aspek kognitif, afektif, dan psikomotor). MANOVA mampu mengungkapkan perbedaan yang tidak ditampilkan ANOVA secara terpisah, sehingga dapat meningkatkan kesempatan untuk menemukan perubahan sebagai akibat dari perlakuan yang berbeda dan interaksinya. Dengan demikian, temuan hasil penelitian akan semakin kaya dan sangat berguna bagi perkembangan ilmu pengetahuan. Terdapat dua model analisis variansi yaitu model overparameterized dan model rerata sel. Model rerata sel memberikan pendekatan sederhana dan tidak ambigu, yang dapat digunakan pada data seimbang atau data tidak seimbang. Model ini menggunakan kontras untuk menyatakan efek utama dan interaksi. Uji persyaratan MANOVA meliputi uji normalitas multivariat dengan uji Mardia dan uji homogenitas matriks kovariansi dengan uji Box’s M. Terdapat beberapa statistik uji MANOVA yaitu Wilks’ Lambda, Pillai, Lawley-Hotelling, dan Roy’s Largest Root. Ketika hipotesis nol MANOVA ditolak, maka dilanjutkan ANOVA pada setiap variabel terikat. Apabila hipotesis nol ANOVA ditolak dan variabel bebas memiliki lebih dari dua nilai, maka dilakukan uji post hoc dengan metode Scheffe’. Prosedur ini menjaga taraf kesalahan α. Uji komparasi rerata antar sel tidak dapat dilakukan secara langsung menggunakan General Linear Model (GLM) pada SPSS. Prosedur yang dapat dilakukan adalah memanipulasi data dengan merubah kondisi eksperimentasi menjadi nilai-nilai yang dianggap satu variabel bebas, sehingga dapat dianalisis dengan One-Way ANOVA atau GLM. Kesulitan analisis multivariat pada perhitungannya yang terlalu rumit, sudah terpecahkan dengan adanya software statistik yang semakin canggih.Kata kunci: MANOVA, analisis multivariat, memperkaya hasil, penelitian pendidikan

The Conceptual and Mathematical Unity of Analysis of Variance, Simple Correlation, and Multiple Regression

1984 ◽  
Vol 59 (3) ◽  
pp. 751-756 ◽  
Author(s):  
Michael Pirot ◽  
Stephen D. Lustig
Keyword(s):  
Linear Model ◽  
Multiple Regression ◽  
Total Variation ◽  
Analysis Of Variance ◽  
General Linear Model ◽  
Complex Data ◽  
Simple Correlation ◽  
Practical Matter ◽  
Linear Rule ◽  
Analyze Data

Analysis of variance, simple correlation, and multiple regression, though ordinarily construed as distinctly different statistical strategies, are demonstrated to be mathematically equivalent, insofar as each method arrives at the same variance accounted for. Conceptually, the three methods are only different procedures to divide the same total variation among a set of observations. As a practical matter, the methods differ in their suitability to analyze data formatted in a particular manner. Underlying these approaches and many of the statistics used in the behavioral sciences is the general linear model, which means that data are organized in a linear fashion. A linear rule is the most parsimonious reduction of complex data but at the same time tends to ignore information not predicted by the rule.

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