Improving English Foreign Language (EFL)Performance using Artificial Intelligence inVocational Education and Training (VET)

Abstract

Internationalisationis one of the strategies for improving the technical qualifications and employability of trainers in initial and continuing vocational educationand training. It is based on the full development of linguistic competence in a foreign language such as English, which is influenced by various factors, including affective factors. Currently, one resource for detecting poorperformance in English is artificial intelligence to the extent thatit can predict academic performance. This research aimsto predict performance in English as a foreign language based on affective variables such as willingness to communicate orally in English, self-efficacy and English language anxiety. The experimental result shows that the predictionmodel trained with a decision tree algorithm (J48) provides the best data for predicting performance in English in terms ofaccuracy = 0.74, precision = 0.70, recall = 0.678 and F-score = 0.68. Analysingthe influenceof the variables and eliminating the data for the affective variable willingness to communicate orally in English yields the best accuracy = 0.76. This finding has relevant practical implications for the early identification of underachievement in Englishand for personalisingeducational interventionsto improve learning and performance in English as a foreign language among vocational education and training students.