Better Educators of Science for Tomorrow (BEST) exposes teachers to modern molecular biology concepts by incorporating computational biology and bioinformatics into their high school curriculum. BEST also prepares teachers to introduce their students to emerging and exciting biomedical careers.
An annual professional development summer workshop trains high school teachers using the “teach-back” technique. This training equips single-subject teachers with the tools to address and teach multidisciplinary concepts in bioinformatics. This technique fortifies the teacher with all the necessary multidisciplinary content knowledge and also develops feedback and evaluation strategies as the lessons progress.
We invite secondary science, math and technology teachers to attend this workshop.
As Bioinformatics is an ever-evolving topic, a static textbook cannot be used for this course. All activities / lab practicum’s are conducted using interactive standard nationally used web-based activities. Teachers will need to bring an updated working laptop or similar device where they can use Word processing, Power Point, access the Internet and download materials. A tablet or cell phone will not fulfill this purpose.
All participant teachers should be able to use all or parts of the curriculum in their upper level high school Biology classes---we visit classes to collect feedback from students during the year. We extend in-classroom support to teachers.
Each trainee teacher is provided with the entire contents of the PSC developed Bioinformatics curriculum which includes daily lesson plans with standards, PowerPoint lecture and web links to all interactive activities. Sample midterm and final exams/ideas are also provided.
Some of the topics we focus on in the high school Bioinformatics curriculum include:
- Introduction to Bioinformatics
- Review of Molecular Biology - Techniques and Applications
- Understanding modern Biology as interdisciplinary
- Margaret Dayhoff’s contribution to Bioinformatics
- NCBI content and scope of BLAST
- Understanding Algorithms
- Evolutionary Relationships & Phylogenetic Trees
- Protein Folding
- Gene Annotation