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

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

Moh. R. Dahman

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Semiha Dahman

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Istanbul University

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

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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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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

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).

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GJHSS Volume 19 Issue G11

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Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

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GJHSS-G Classification FOR Code: 130309

Submission ReceivedDecember 10, 2018
Peer Review Double Blind
Handling Editor
Accepted January 5, 2019
Published January 15, 2019

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v1.2

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December 31, 2019

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