<![CDATA[This study aims to analyze the Influence of the Use of Team Games Tournament Models and Learning Disciplines on the Economic Learning Outcomes of Class X Students of SMAN 16 Padang City. Based on the analysis of data it is known that both classes of samples are usually distributed and homogeneous. The results of hypothesis testing through two-way ANOVA obtained the following results: 1) obtained a sig value of 0.011, this means that the sig value. less than the value a = 0.05 (sig. < ) then thus the H0 accepted Ha rejected, so it can be concluded that the learning outcomes of students taught using the teams games tournament method are higher than the classes taught using conventional learning methods (discovery learning). 2) the second hypothesis, obtained a sig value of 0.016, this means that the sig value. less than the value (a = 0.05 (sig.<) then thus the H accepted Ha rejected, So that it can be concluded that the learning outcomes of students taught using the teams games tournament method with higher learning discipline are higher than the classes taught using conventional learning methods (discovery learning) with learning discipline. 3) obtained a sig. value of 0.023, this means that the value of the sig. less than the value (a = 0.05 (sig.< ) then thus the Ho accepted Ha is rejected, so it can be concluded that students who have low learning discipline are taught with the teams games tournament model have high learning outcomes than the learning outcomes of students who have low learning discipline using a conventional learning model. 4) (discovery learning)) the third hypothesis is obtained sig level value = 0.401, this means that the value is a sig level value. = 0.843, this means that the value of the sig. greater than the value (α = 0.05 (sig. < α) then thus H0 rejected Ha accepted, so that there is no interaction of the learning model teams games tournament and learning discipline on student learning outcomesKeywords: Learning Outcomes, learning discipline, Model Teams Games Tournament, Discovery Learning]]>
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