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

1
Moh. R. Dahman
Moh. R. Dahman
2
Semiha Dahman
Semiha Dahman
1 Istanbul University

Send Message

To: Author

GJHSS Volume 19 Issue G11

Article Fingerprint

ReserarchID

P5Z96

Evaluation of a Predictive Model for the decision of Lifelong Learners to Continue or Drop Out a German Course Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

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.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for 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): .

Download Citation

Issue Cover
GJHSS Volume 19 Issue G11
Pg. 17- 22
Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

Keywords
Classification
GJHSS-G Classification: FOR Code: 130309
Version of record

v1.2

Issue date

December 31, 2019

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 2470
Total Downloads: 1266
2026 Trends
Research Identity (RIN)
Related Research

Published Article

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.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

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

Moh. R. Dahman
Moh. R. Dahman Istanbul University
Semiha Dahman
Semiha Dahman

Research Journals