This paper examines how computer information technology can be incorporated into teaching evaluation in higher education, with particular attention to practical approaches and innovative strategies. Utilizing methods such as causal inference analysis, propensity score matching (PSM), Bayesian analysis, and cluster analysis, The study investigates how technology-enhanced evaluation methods influence students learning outcomes, emphasizing measurable effects and practical implications. The findings reveal that the use of technology notably improves learning effectiveness by delivering individualized feedback, boosting student engagement, and easing cognitive demands. Moreover, the results underscore the value of customized strategies to address varied learner profiles. This study offers concrete evidence in favor of integrating advanced technologies into educational evaluation, laying the groundwork for refining and expanding more inclusive and impactful teaching approaches.