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Bioinformatics algorithms: course, teaching pedagogy and assessment

Bioinformatics algorithms: course, teaching pedagogy and assessment

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This chapter is a case study for presenting various modes of in-class lecture delivery, student-instructor interaction, and topic discussion. The aim of using numerous forms of teaching-learning pedagogy is for justifying and achieving the learning outcomes of the course. We have tried to incorporate and change strategies of having instructor-led training (ILT) materials to student-centric learning. It explores various learning styles and dimensions so that the course content may be delivered to its fullest. Adaptation of different types of learning styles is implemented to promote flexibility with the instructor and help the students perceive a topic in various flavours. The chapter also puts forth topic-wise teaching-learning pedagogy availed, students' motivations, as justified from their informal feedbacks, recommended actions that have a positive influence in topic delivery and understanding and usage in exploring the subject in relation to other domain studies. Blooms' cognitive level is also mentioned to give a concise idea of the topic depth that would be followed in this particular course delivery. The chapter also discusses the concept development and exploration, courserelated material design and development, and evaluation and analysis. The measurement framework is developed on the basis of the following criteria of intuitive capability levels, in-class response, topic understanding (based on student's informal and formal feedback), and marks-based evaluations. This inherently incorporates certain evaluation practices followed in this course. Having a high cohesion with bioinformatics, the course helps in offering computational solutions to sustainability-related issues. Further, based on NBA requirements, the course outcomes are also measured as per their given directives. Based on student's interactions, the course was found to be popular and useful to students. The computer science and engineering and information technology (CSE and IT) students could easily relate the understanding of data structures and algorithms captured in the interdisciplinary course, whereas the biotechnology students could relate their core knowledge in bioinformatics, genes, genetics, protein, and other domain knowledge to the various algorithms that can help in addressing solutions. The subject presents a win-win situation for students as they get to work in a domain with a vast dataset and that can have a huge impact on the human lifestyle and lifespan understanding.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 Course content: creation and access, course outcomes
  • 12.2.1 Access of course content
  • 12.2.2 Course outcomes
  • 12.2.3 Course content
  • 12.3 Strategies of lecture delivery
  • 12.4 Details of the topics discussed
  • 12.4.1 Topic 1: algorithms and complexity
  • 12.4.2 Topic 2: molecular biology
  • 12.4.3 Topic 3: exhaustive search-mapping, searching
  • 12.4.4 Topic 4: greedy algorithms
  • 12.4.5 Topic 5: dynamic programming algorithms
  • 12.4.6 Topic 6: divide-and-conquer algorithms
  • 12.4.7 Topic 7: graph algorithms
  • 12.4.8 Topic 8: combinatorial pattern matching
  • 12.4.9 Topic 9: clustering and trees
  • 12.4.10 Topic 10: applications
  • 12.5 In-class assessment approaches
  • 12.5.1 Self-assessment by students
  • 12.6 Discussion
  • 12.7 Conclusions and future scope
  • References

Inspec keywords: data structures; genetics; educational courses; biotechnology; cognition; computer aided instruction; bioinformatics; teaching; proteins

Other keywords: topic delivery; course-related material design; computer science and engineering; instructor-led training materials; protein; topic understanding; teaching pedagogy; topic depth; CSE; learning outcomes; course delivery; NBA requirements; student motivation; marks-based evaluations; in-class lecture delivery; course outcomes; cognitive level; topic discussion; data structures; bioinformatics; in-class response; student-centric learning; genetics; interdisciplinary course; biotechnology students; student-instructor interaction; concept development; topic-wise teaching-learning pedagogy; learning styles; information technology; sustainability-related issues

Subjects: Computer-aided instruction; Biology and medical computing

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