12th grade

 

12th grade

Activities:
  • Studying Darwin's theory of evolution mechanisms with comic illustrations
  • Studying evidence of evolution with infographic illustrations
  • Studying directions of selection with animated illustrations
  • Interactive Assignment 1: Sorting photos of living organisms by the type of selection that led to their emergence
  • Interactive Assignment 2: Determining the direction of selection in the situations described in the text
  • Interactive Assignment 3: Ranking the reliability of evidence for evolution
  • Interactive Assignment 4: Identifying and sorting organs into analogous, homologous, and vestigial categories
  • Assignments 5 and 7: Modeling evolutionary processes using examples of bacteria and humanity
  • Assignment 6: Discussion on controversial questions of the theory of evolution
 
Activities:
  • Break down complex terms like chemoorganoheterotrophy and photolithoautotrophy into clear, understandable ideas
  • Compare cellular respiration, photosynthesis, and fermentation at the molecular level
  • Explore the differences between oxygenic and anoxygenic photosynthesis
  • Examine both aerobic and anaerobic respiration pathways
  • Discover unique prokaryotic energy strategies, such as chemolithoautotrophy and methanogenesis

Activities:
  • Measures of Central Tendency — Understand how to calculate and interpret the mean using practical data examples

  • Student’s t-Test — Learn to compare two sample means, test hypotheses, and interpret results with real data sets

  • Chi-square test — Explore how to analyze categorical data for significant differences, even with unequal sample sizes

  • Pearson and Spearman correlation coefficients — Discover how to measure relationships between variables, including when data are not normally distributed

  • Phi coefficient — Analyze associations in binary (yes/no) outcomes through fun behavioral experiments

  • Cluster analysis — Group and compare complex biological data using distance measures and dendrograms, learning how to identify patterns in multidimensional datasets