A Framework for Liberal Learning in an Engineering College.
Author(s) -
Pradeep Waychal,
Anil Sahasrabudhe
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--19061
Subject(s) - liberal education , process (computing) , liberal arts education , engineering education , lifelong learning , scope (computer science) , active learning (machine learning) , computer science , mathematics education , industrialisation , artificial intelligence , engineering , engineering ethics , engineering management , higher education , political science , pedagogy , sociology , mathematics , law , programming language , operating system
This paper discusses experience of running a course in Liberal Learning for over 300 sophomore students of non circuit branches at a premier engineering college in India. The primary goals of the course were to introduce a lifelong learning process that allows students to extend their knowledge horizons beyond engineering, help them appreciate the interplay of engineering and other disciplines, and make them better learners. Liberal Learning has been in use in different forms in different civilizations. Aristotle had defined it as learning of a free man and emphasized the importance of the spirit in which the learning is pursued. In the last few centuries, industrialization re-defined educational agenda. It introduced industry oriented engineering courses that did not pay much attention to liberal learning. Recent trends show that liberal learning is regaining its importance. Some leading institutes like Princeton, Yale, and CMU run programs for engineers to help them gain a clear appreciation of technology and the socio-political forces that shape it. The Indian engineering education system has been slow in adopting this paradigm. We define liberal learning as ―self-learning in self-chosen liberal areas with self-defined scope‖. This covers a vast knowledge space. To ensure that students do not get lost in the space, we developed a guiding framework. This framework consists of process and data. The process has four distinct and slightly overlapping elements. They are define, harvest, synthesize and share. The data elements include student, area, faculty, sub-area, and cluster. Course assessment consisted of mid-term and end-term presentations which were evaluated by the peers and moderated by the faculty mentors. Results of self appraisals with respect to the learning attributes and the consequent development plans were also examined during the assessment.
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