Author(s)

Shaofeng Pang ¹, Yujing Zhang ², Linwen Zhang ¹, Qiong Su ¹, Xiangfei Zhao ¹

Affiliation(s)

¹ Chemical Engineering Institute, Northwest Minzu University, Lanzhou, China
² College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, China

Corresponding Author

Shaofeng Pang

ABSTRACT

Spectroscopic analysis has been considered a challenging course due to its heavy content, high memorization requirement, and extensive repetition. Recent educational studies indicate, however, that the difficulty does not lie solely in the quantity of information but also in the failure of most students to develop an adequate cognitive schema with spectra. For novices, spectra are often viewed as peak, numerical, and tabular collections; for experienced learners, spectra operate as a multiple representation system that needs to be deciphered based on molecular configuration, bonding environment, experimental setup, discipline-specific vocabulary, and argumentation in science. Educational studies have found that the learning of infrared and nuclear magnetic resonance spectroscopies can be hindered by local cue dependency, dysfunctional chemical reasoning, heuristic manipulation, faulty conceptual model, and fragmented reading strategy. Meanwhile, more competent learners are characterized by spectral comparison, multivariate reasoning, and iterative data interpretation. The development of visualization software, interactive digital tools, real-world data set, spectral puzzle, group work activity, gamification exercise, concept diagnostic test, and pedagogical content knowledge of instructors have provided new perspectives for spectroscopic analysis instruction. Based on this research base, the current study proposes that spectroscopic analysis should not merely be taught through a series of analytical methods and practices but as integrated instruction in representational transformation, evidence chain construction, and academic communication, with particular attention paid to visualization scaffolding, authentic assignment, formative evaluation, instructor knowledge, and equity in education.

KEYWORDS

Spectroscopic Analysis Instruction, Representational Transformation, Spectral Evidence-based Reasoning, Authentic Learning Tasks

CITE THIS PAPER

Shaofeng Pang, Yujing Zhang, Linwen Zhang, Qiong Su, Xiangfei Zhao. Representational Transformation and Evidence Chain Construction in Spectroscopic Analysis Instruction. Curriculum and Teaching Methodology (2026). Vol. 9, No.2, 55-62. DOI: http://dx.doi.org/10.23977/curtm.2026.090207.

REFERENCES

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