Bibliothek

@article{Bicen2018,
title = {Perceptions of Students for Gamification Approach: Kahoot as a Case Study},
author = {Huseyin Bicen and Senay Kocakoyun},
url = {http://online-journals.org/index.php/i-jet/article/view/7467
http://dx.doi.org/10.3991/ijet.v13i02.7467},
doi = {10.3991/ijet.v13i02.7467},
issn = {1863-0383},
year = {2018},
date = {2018-02-27},
urldate = {2018-05-16},
journal = {International Journal of Emerging Technologies in Learning (iJET)},
volume = {13},
number = {2},
publisher = {kassel university press GmbH},
abstract = {A novel learning experience that increases student motivation can be created in a learning environment that includes a gamification approach to assess competence. Student views on gamification were surveyed to determine the best application of this method, the environment necessary for its use, and the manner by which the application should proceed. The effect of a gamification approach on student achievement through intra-class competition was assessed using quantitative and qualitative methods. In this study, the Kahoot application was the preferred gamification method used. Participating students included 65 undergraduate students studying at the Department of Preschool Teaching. The findings showed that inclusion of a gamification method increased the interest of students in the class, and increased student ambitions for success. This method was also found to have a positive impact on student motivation. Furthermore, the results of this study indicate that the Kahoot application can be used effectively for gamification of lessons. In conclusion, the gamification method has an impact on students that renders them more ambitious and motivated to study.},
keywords = {achievement, competition, gamification, motivation, O, students},
pubstate = {published},
tppubtype = {article}
}

@unpublished{Kemper2018,
title = {Predicting Student Dropout: A Machine Learning Approach},
author = {Lorenz Kemper},
url = {https://www.researchgate.net/publication/322919234_Predicting_Student_Dropout_a_Machine_Learning_Approach},
year = {2018},
date = {2018-02-01},
urldate = {2018-08-22},
institution = {Karlsruhe Institute of Technology (KIT)},
abstract = {We perform two approaches of machine learning, logistic regression and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without need of collection. Therefore, we propose a methodical approach that may be put in practice with relative ease at other institutions. Using a Hellinger-Distance splitting approach we find decision trees to produce slightly better results. However, both methods yield high prediction accuracies of up to 95% after three semesters. A classification with more than 83% accuracy is already possible after the first semester. Within our analysis we show, that resampling techniques can improve the detection of at-risk students.},
keywords = {descision trees, dropout, higher education, logistic regression, machine learning, massive open online courses (MOOCs), O, prediction, students, Studienerfolg},
pubstate = {published},
tppubtype = {unpublished}
}