Active Learning Multi-Strategies
Ibtehal Nafea*, Computer Science Department, College of Computer Science and Engineering, Taibah University, Saudi Arabia.
Manuscript received on September 20, 2020. | Revised Manuscript received on November 01, 2020. | Manuscript published on November 10, 2021. | PP: 87-94 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81191110120| DOI: 10.35940/ijitee.A8119.1110120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The competitive educational environment and globalization promote the adoption of multiple strategies that instill effective learning. Embracing different learning strategies helps keep the students focused and engaged. Not only this, but it also helps to evaluate the grey areas in their learning. The active learning improves student’s engagement and helps develop a conducive learning environment. Given this, the study investigates the effectiveness of active learning in computer science (CS) students. Primarily, it illustrates the active learning multi-strategies and how they positively contribute to the learning environment. A cross sectional design is used following a quantitative approach. The data is gathered from 74 students through a survey using a close-ended questionnaire. These students were enrolled in two different semesters, which was then statistically analyzed. Results showed that self-study improved the students learning outcomes by 77% for first semester students and by 78% for second-semester students. Effectiveness of peer review was 72% and 70%, while for clickers, it was 62% and 63%, respectively. The use of multi-strategies is instrumental in improving students learning. Such as, it helps increase learners’ knowledge, confidence, and constructive interaction in the classroom setting.
Keywords: Active Learning, Clickers, Education, Flipped Classroom, Programming Course, Role Play.
Scope of the Article: Machine Learning