Research pick: Say it right - "Objective evaluation of English teaching in colleges and universities based on textual analysis in multi-element perspective"

A new approach that might improve the evaluation of college-level English teaching by incorporating a broader range of data sources could address longstanding concerns about the subjectivity of traditional assessment methods. Conventional evaluations often rely predominantly on surveys and test scores, which provide limited insight into the complexities of teaching quality. The new approach, discussed in the International Journal of Reasoning-based Intelligent Systems, instead integrates diverse forms of data, multimodal elements, to produce a more objective appraisal.

Multimodal elements encompass different types of information beyond text, including images and audio recordings related to the teaching process and student feedback. The study’s main innovation lies in its use of an advanced computational framework that simultaneously analyses these varied data types, thereby capturing a more complete picture of the teaching environment.

A sophisticated algorithm, known as Cross-modal attention mechanism (CMAM), is used to identify meaningful relationships across modalities. For instance, it can link emotional expressions found in written comments with corresponding tones in audio or facial cues in images. This cross-referencing allows the system to interpret feedback in context, rather than treating each factor in isolation.

To bring information from the different modalities together, the system employs a gating mechanism. This regulates the contribution of each data source, ensuring that the most pertinent details have the most influence on the final evaluation. The combined data is then processed through a Transformer model, a machine-learning system that can understand complex patterns in language and context.

The model then performs sentiment analysis, which automatically detects the emotional tone behind the evaluations, whether positive, negative, or neutral. Unlike basic scoring, this method captures the subtleties of attitudes toward teaching quality, offering a richer understanding of student perspectives. A weighted naïve Bayes algorithm is then applied to come up with an overall evaluation score. Overall, this approach ensures the final assessment reflects not only what is said but also how it is expressed emotionally.

Chen, X. (2025) ‘Objective evaluation of English teaching in colleges and universities based on textual analysis in multi-element perspective’, Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 8, pp.11–20.