Journal of Engineering Education Transformations

Journal of Engineering Education Transformations

Year: 2015, Volume: 28, Issue: Special Issue, Pages: 159-164

Original Article

Integrated Learning Framework towards Attaining Focused Outcomes

Abstract

With the changing demands and the needs of the industry, integrated learning becomes a major necessity today. Academicians need to play a vital role in developing such skills in students. To inculcate integrated learning into students we need to attain focused outcomes. In this paper, we propose an integrated learning framework towards attaining focused outcomes. We considered two major core courses - data mining and web technologies - in computer science domain at undergraduate level to develop integrated learning framework. The framework is a three stage process comprising of problem definition phase, knowledge discovery from database phase involving pre and post data processing, and finally validating results phase. In this paper, we discuss prototype of combining the courses data mining and web technologies to come out with an objective of "developing a web based data mining application" as a course project to cater the needs and to explore the technologies and concepts that could be glued together to develop rich internet applications. The experimental results show that the applications are full-fledged and addresses the Accreditation Board for Engineering and Technology (ABET) focused outcomes 'c' (An ability to design a computer based system, component or process to meet the desired needs within realistic constraints) and 'k' (An ability to use the techniques, skills, and tools necessary for computer engineering practice). The key focus was on integrated learning need so as to develop additional skills in students such as presentation skills, team-work, self-learning and analyzing the real world problems which in-turn improves the students' confidence level. This is reported in outcome assessment.

References

  • D. Li, "A Practice-Oriented Approach to Teaching Undergraduate Data Mining Course", 2011 American Society for Engineering Education (ASEE) Annual Conference, June 2011.
  • Gartner Predicts 2014 - http://www.gartner.com/technology/research/predicts/.
  • Han, J. and Kamber, M., Data Mining: Concepts and Techniques, Elsevier, 2006.
  • Knime - https://www.knime.org/.
  • Musa Jafar and Russell Anderson, "A Tools-Based Approach To Teaching Data Mining Methods", Journal of Information Technology Education: Innovations in Practice, Volume 9, 2010.
  • W3Schools - http://www.w3schools.com/.
  • ABET Accreditation - http://www.abet.org/accreditation/.
  • GATE - http://en.wikipedia.org/wiki/Graduate_Aptitude_Test_in_Engineering.
  • Weka - http://www.cs.waikato.ac.nz/ml/weka/.
  • Orange - http://orange.biolab.si/.
  • Rapid Miner - http://rapidminer.com/.

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