Similar to my final design project TELE for teaching Periodicity but relevant to this post, interactives through Module B: Chemland Suite visualize periodic trends as 3D bar graphs on periodic table outline:
Pictorial representations are more approachable than raw numbers from the data booklet, intuitively comparing values between elements on different periods and groups. It however does nothing to explain fundamental concepts like why ionization energy increases across periods, resulting in similar traditional instruction problems to memorize trends without understanding. Based on my final project, Excel can be used to zoom in on specific trends, having students work through observations at their own pace, noting discrepant events through guided inquiry (Driver et al., 1994). Simulations embed just in time prompts, minimizing unnecessary details and pressing variables freely. Otherwise pure discovery can be frequently overwhelming where learners cannot reflect upon their own learning.
- Define ionization energy, electron affinity, electronegativity and melting point, having students explore trends visually using Chemland.
- Plot Excel graphs to examine patterns across periods and down groups. Does the data fit with expectations?
- Have students modify conclusions posing anomalies and discrepant events. Learners can think-pair-share to explain personal ideas convincing others of reasoning.
- Discuss rationales graphing IE, EA, EN and MP against radii directly to examine whether Coulomb’s Law theory applies.
- Have group debrief getting students to justify periodic trends, focusing on reasons more than stating from memory.
Simulations can be used to supplement lectures and verify empirical data from lab work, reviewing concepts to fill in missing information. Inquiry through agency models scientific reasoning, actively constructing knowledge. Conceptual models communicate invisible ideas, developing explanations to make sense of the natural world. Although textual representations enable higher precision and control, concrete environments help students use knowledge rather than simply memorize. With flexible private theories, learners develop original hypotheses formalizing ideas towards knowledge, enabling self-directed study with sufficient freedom testing alternative iterations (Xiang and Passmore, 2014). Modelling involves analyzing, synthesizing, debugging and explaining, progressing through multiple cycles of construction, quantification, interpretation and revision.
Driver, R., Asoko, H., Leach, J., Mortimer, E., & Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(7), 5-12.
Xiang, L., & Passmore, C. (2014). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, doi:10.1007/s10956-014-9534-4