A New Proposal to Teaching: The Beehive Interactive Learning Model in a Statistics Course
AbstractThe purpose of the study is to present the Beehive Interactive Learning Model (BILM) and to provide an example of its application in an undergraduate statistics course. The model is developed by the researcher, who is a professor in Educational Measurement and Evaluation area in Turkey with over 15 years of teaching experience. The model is based on four main components; content, instruction, assessment, and motivational beliefs. The core principal of the model is to stimulate students’ desire for learning. Both the instructor and the students are in the center of the model, placed in the beehives, referring to the hard work and constant interaction among students. The graphical representation of the model is presented in the paper. The model has been improved over a period of 10 years by adding new components and omitting some. The model requires the instructor to encourage active learning through class projects and performance assignments. It heavily relies on technology and the Internet; course portal provides online documents, an opportunity to download and submit assignments online, and to take online self-regulated quizzes with instant feedback. There are lab sessions requiring students to demonstrate their analyzing skills in SPSS. The model aims to pack the knowledge in a way that awakens everybody's desire to learn, satisfies the learner through successful hands-on applications and finally develops a sense of success, hopefully yielding positive attitudes toward the content and learning. The model is in the process of development and any suggestions are welcomed from the researchers around the world.
Oct 6, 2017
How to Cite
BASOL, Gulsah. A New Proposal to Teaching: The Beehive Interactive Learning Model in a Statistics Course. European Journal of Multidisciplinary Studies, [S.l.], v. 2, n. 6, p. 107-114, oct. 2017. ISSN 2414-8385. Available at: <http://journals.euser.org/index.php/ejms/article/view/2630>. Date accessed: 24 feb. 2020. doi: http://dx.doi.org/10.26417/ejms.v6i1.p107-114.
CC Attribution 4.0 International (CC BY4.0)