Context Based Learning in Basic Thermodynamics through Day to Day Events
K V Muralidhar Sharma1, S.G.Gopala Krishna2, N.Kapilan3
1K V Muralidhar Sharma, Dept. of mechanical Engg., Jyothy Institute of Technology, Bengaluru, Karnataka.
2Dr.S.G.Gopala Krishna, Professor & Director, Nagarjuna Group of Institutions, Chickballapur.
3Dr. N.Kapilan, Professor and Head, Dept. of M.E., Nagarjuna College of Engg & Technology, Chickballapur.

Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12052-12055 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5605098319/2019©BEIESP | DOI: 10.35940/ijrte.C6505.118419

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Abstract: Very few students accept that learning basic thermodynamics is really inspirational. Unfortunately the reality is exactly the other way. Even the students after certain years of learning and working experience accept this. Acceptance of the subject’s high difficulty level is also a widely acknowledged by the researchers across the globe. Researchers are continuously engaged in finding the root cause of the problem as well possible different solution. This paper is the part of an ongoing research work in designing a Learning system. As part of this work a number of day-to-day events which can be effectively utilized in driving home different thermodynamic concepts are identified. Two scenarios out of these shortlisted is dealt here, namely (1) A traveler scenario with an over pressurized car (2) A hot air blow scenario. These two Contexts have been illustrated to drive home the concepts of basic thermodynamics. A well-established CBL model is used for the experimentation purpose. First year engineering undergraduate students of VTU are selected for the quasi-experimental purpose. The experiment has proved that the context based learning has given an improvement in the learning gain.
Keywords: Thermodynamics, Scenario approach, CBL- Context Based Learning, Learning difficulties.
Scope of the Article: Artificial Intelligence and Machine Learning.