A Probabilistic Inductive Model for Translation Error Analysis among EFL Learners through Al-Sadr’s Induction Theory - Hussien Jawad Abdulridha; Hussien Jawad Abdulridha; Anwar Al-Zahraa Firas (Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan, 2026)
This quasi-experimental study investigated the prediction of recurrent translation errors among EFL learners using a Bayesian probabilistic model within the framework of Sayyid Muhammad Baqir Al-Sadr’s theory of probabilistic induction. 65 university students participated in the study, completing two translation tests to examine the recurrence of ten types of translation errors under similar linguistic conditions. The results of Test 1 showed that grammatical errors were the most frequent (31.8%), spelling errors (28.8%) and semantic errors (19.4%). Similar trends were also evident in Test 2; however, the frequency of semantic errors decreased slightly (from 19.4% to 16.3%) compared with Test 1, while contextual errors (from 8.0% to 11.6%) and terminology errors (from 2.4% to 6.4%) each showed a moderate increase. Idiomatic, cultural and omission/addition error frequencies remained low. The Bayesian model performed reasonably well as a predictive tool. It was able to demonstrate high levels of predictive consistency by providing close correspondence between the predicted probability of an error occurring and its observed frequency on the second translation task. The findings suggest that recurrent translation errors can be interpreted as predictable probabilistic patterns rather than isolated learner deviations. The study concludes that integrating Bayesian reasoning with Al-Sadr’s probabilistic induction theory may help instructors predict learner difficulties and design more effective pedagogical interventions in teaching.