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Machine learning based analysis of learner-centric teaching of punjabi grammar with multimedia tools in rural indian environment

The advent of multimedia and its reach to everyone has made a massive change in life. Multimedia content enhances the learning trend and is playing a pivotal role in making the teaching and learning process more learners centric. In the present manuscript, authors use machine learning methods to fin...

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Bibliographic Details
Published in:Multimedia tools and applications 2022-11, Vol.81 (28), p.40775-40792
Main Authors: Kumar, Vinay, Dhingra, Gittaly, Saxena, Nitin, Malhotra, Reetu
Format: Article
Language:English
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Summary:The advent of multimedia and its reach to everyone has made a massive change in life. Multimedia content enhances the learning trend and is playing a pivotal role in making the teaching and learning process more learners centric. In the present manuscript, authors use machine learning methods to find the impact of multimedia led teaching in government schools of Punjab, India. They promote the use of computer-based teaching in Punjabi for teaching the syllabus in schools. The secondary class students of Patiala and Mohali government schools affiliated to Punjab School Education Board are participants of this study. The students are divided in two groups. Twenty two topics of Punjabi grammar syllabus are taught to two student groups separately using different instructional strategies (multimedia presentations and traditional lectures). Achievement test, before and after the teaching, are conducted for all participants. The results support the hypothesis that multimedia does make a difference in the overall learning of the students. The descriptive statistics of achievement score shows the improvement in marks obtained by students after technology driven teaching. The average marks scored by students taught through multimedia system is 52.24% more than taught through traditional method. Nine machine learning models are used to find effectiveness of multimedia-based learning. Results shows that AdaBoost performs well with an accuracy of 99.8% respective to others based on student learning strategies.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-12898-w