Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The necessary motivation, attitude, and anxiety along with other independent variables, like the placement test scores, influence learners’ adequate progress and successful performance in language classrooms. A machine learning model was designed by Dahman and Dag (2019) to predict adult learners’ decision to continue or drop out ESOL courses based on the input variables (motivation, attitude, anxiety, and placement test scores). This study investigated the accuracy of this model in a different setup, the decision of lifelong learners to continue or drop out a German Language course. 100 German learners “A2” level have participated in the study, the result showed that the model predicted 95.4%. accuracy for the continuation, and 83.3% accuracy for the dropouts, with an overall accuracy of 94%. The implication of the result and future recommendations are discussed.
Moh. R. Dahman. 2019. \u201cEvaluation of a Predictive Model for the decision of Lifelong Learners to Continue or Drop Out a German Course\u201d. Global Journal of Human-Social Science - G: Linguistics & Education GJHSS-G Volume 19 (GJHSS Volume 19 Issue G11): .
Crossref Journal DOI 10.17406/GJHSS
Print ISSN 0975-587X
e-ISSN 2249-460X
The methods for personal identification and authentication are no exception.
Total Score: 102
Country: Turkey
Subject: Global Journal of Human-Social Science - G: Linguistics & Education
Authors: Moh. R. Dahman, Semiha Dahman (PhD/Dr. count: 0)
View Count (all-time): 120
Total Views (Real + Logic): 2470
Total Downloads (simulated): 1266
Publish Date: 2019 12, Tue
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Neural Networks and Rules-based Systems used to Find Rational and
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The necessary motivation, attitude, and anxiety along with other independent variables, like the placement test scores, influence learners’ adequate progress and successful performance in language classrooms. A machine learning model was designed by Dahman and Dag (2019) to predict adult learners’ decision to continue or drop out ESOL courses based on the input variables (motivation, attitude, anxiety, and placement test scores). This study investigated the accuracy of this model in a different setup, the decision of lifelong learners to continue or drop out a German Language course. 100 German learners “A2” level have participated in the study, the result showed that the model predicted 95.4%. accuracy for the continuation, and 83.3% accuracy for the dropouts, with an overall accuracy of 94%. The implication of the result and future recommendations are discussed.
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