Acquisition of Arabic Students with Hybrid and Receptive Learning Models

This study aimed to analyze the ability to acquire Arabic as a second language for students of the Islamic Economics and Business Faculty at IAIN Ponorogo through hybrid and sensory learning activities. This research is qualitative with a case study type. The research population totaled 176 students, with a sample of 20 students. The sampling technique was done by non-probability sampling with the snowball sampling method because the samples were taken based on the needs of this study. The research data is in the form of pronunciation of vocabulary sounds, Arabic spelling of letters, verbs, and nouns, the ability to understand the material being listened to, and the ability to read Arabic texts for FEBI IAIN Ponorogo students in odd semesters of 2022/2023 academic year. The student reading source used as research data is the book "Al-'Arabiyyah fī Dirāsah al-Mu'āmalāt." A book taught in the course "Arabic for Economics." Observation, interviews, and documentation carry out data collection techniques. Data analysis techniques with data reduction, data presentation, concluding, and verification. The results of the study show that the acquisition of Arabic as a second language by students of the Islamic Faculty of Economics and Business at IAIN Ponorogo in attending "Arabic for Economics" courses tends to differ from one another. They are entering the pre-production language acquisition phase. That is, they have not mastered many foreign vocabulary. This is due to the educational background taken before they entered university. Another influencing factor is the environment in which students live

A random forest system combination approach for error detection in digital dictionaries

When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hybrid approach that combines rule-based, feature-based, and language model-based methods. We investigate combining methods and show that using random forests is a promising approach. We find that in isolation, unsupervised methods rival the performance of supervised methods. Random forests typically require training data so we investigate how we can apply random forests to combine individual base methods that are themselves unsupervised without requiring large amounts of training data. Experiments reveal empirically that a relatively small amount of data is sufficient and can potentially be further reduced through specific selection criteria.Comment: 9 pages, 7 figures, 10 tables; appeared in Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, April 201