Leading team:
- Dr. Moriah Ariely
- Prof. Anat Yarden
Project team:
- Dr. Noa Kedar
- Dr. Karin Halevi
- Dr. Gilat Brill
- Dr. Merav Siani
- Moran Farber
- Bilal Sawalha
Brief
PeTeL Biology is an online environment tailored for high school biology studies. The primary goal of the PeTeL environment is to customize biology learning materials to the comprehension level of each student in the subject being taught. The name given to the environment, PeTeL, is derived from the initials of the English words "Personalized Teaching and Learning". Accordingly, the PeTeL environment primarily focuses on diagnosing biology students' knowledge and skills, both at the class and individual student levels. The diagnosis is tailored to various learning styles and directs students to suitable learning materials (personalized teaching and learning). The knowledge diagnosis and student guidance are partly based on using artificial intelligence (AI) tools, such as providing feedback to students’ answers to open-ended questions in specific topics.
The PeTeL environment includes various activities that strongly emphasize an integration between high-level biological content and scientific skills. The content is interactive and editable, allowing personalization by the teachers for their class and/or group of students. The activities also integrate real-life cases, examples from daily life, and current biology and biology education research. Additionally, PeTeL includes a database of original questions and assessments, enabling the quick and simple creation of adaptive exams and assessments.
The environment enables every teacher to manage the classroom teaching sequence and learning materials, conduct ongoing formative assessments based on the real-time situation, and assess the status of learners in the class (performance, achievements, progress, etc.).
The content in the PeTeL environment is developed based on the professional and pedagogical knowledge of researchers in the Science Teaching Department at the Weizmann Institute of Science. The environment is accessible to all teachers at no cost.
Further reading:
- Ariely, M., Nazaretsky, T., & Alexandron, G. (2023). Machine learning and Hebrew NLP for automated assessment of open-ended questions in biology. International Journal of Artificial Intelligence in Education, 33, 1-34.
- Bar, C., & Yarden, A. (2023). Oh Deer … Practicing Scientific Inquiry and Data Literacy through an Authentic Gazelle Data Set. The American Biology Teacher, 85(5), 245-251.
- Siani, M., & Yarden, A. (2022). Introducing evolution of the human lactase gene using an online interactive activity. American Biology Teacher, 84(1), 16-21.