Evaluation of a Predictive Model for the decision of Lifelong Learners to Continue or Drop Out a German Course

Moh. R. Dahman; Semiha Dahman

Authors

Moh. R. Dahman
Semiha Dahman

Keywords:

prediction model; german language; L2 acquisition

Abstract

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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How to Cite

Evaluation of a Predictive Model for the decision of Lifelong Learners to Continue or Drop Out a German Course. (2019). Global Journal of Human-Social Science, 19(G11), 17-22. https://socialscienceresearch.org/index.php/GJHSS/article/view/3068

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