Quranic education and technology : reinforcement learning system for non-native Arabic children

We built a simulator based on reinforcement learning to improve teaching experience in Quranic and Islamic education for non-native Arabic speakers to evaluate their strength and weaknesses and allow the system to help improving the child in one hand, and provide an accurate actual report for each child on the other hand

A Correlational Study on the Students’ Quranic Memorization and Their English Vocabulary Retention

This study aims to know whether there is a correlation between students’ Quranic memorization and their English vocabulary retention. This study chose 28 of seventh graders at Islamic Junior High School Az-Zahra Lampung in 2018/2019 academic year as the sample by means of cluster random sampling technique. This study applied correlational research design. The documented data of Quranic memorization of learners and the English vocabulary preservation were collected from the test and then analyzed for hypothetical testing employing Pearson's Product Moment equation. The outcome of the hypothetical test shows that Sig. (value) was generated by the value of significance = 0.000 < α = 0.05. It means the acceptance of Ha and the rejection of H0. Furthermore, based on the r value interpretation table, it is known that the observed r is a high correlation as the value of observed r is 0.622 in the 0.600 – 0.800 level. It can therefore be inferred that there is a significant correlation between the Quranic memorization of learners and the acquisition of English vocabulary

Memorization and discussion methods effect on achievement and communication skills

This study aimed to determine learning outcomes and discuss the effect of the combination of memorization and small group discussion methods on students’ achievement and communication skills. A quantitative method was applied in this study with a quasi-experimental design. The subject of this study is 60 students in Islamic Education, Faculty of Education and Teacher Training (FTIK) at the Pontianak State Institute for Islamic Studies, divided into a control and an experimental group. Data were collected through pre-test and post-test from the control and experimental groups and then analyzed using the independent samples t-test using statistical software JASP version 0.16.4. The findings revealed that there are differences between outcome learning and communication skills between the control group which only uses the small group discussion method and the experimental group which employs a combination of memorization and small group discussion methods in the course of developing Islamic Education material. The findings of this study confirm that combining the two methods, memorization, and small group discussion, is highly effective in improving learning achievement and communication skills at the student level. Further research is suggested to explore and determine other factors affecting learning outcomes and methods

Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

Voice signal processing has been proposed to improve effectiveness and facilitate the public, such as Smart Home. This study aims a smart home simulation model to move doors, TVs, and lights from voice instructions. Sound signals are processed using Mel-frequency Cepstrum Coefficients (MFCC) to perform feature extraction. Then, the voice is recognized by the speaker using a hierarchy Support Vector Machine (SVM). So that unregistered speakers are not processed or are declared not having access rights. For the process of recognizing spoken words such as "Open the Door”,"Close the Door","Turn on the TV","Turn off the TV","Turn on the Lights" and "Turn Offthe Lights" are done using Backpropagation. The results showed that hierarchy SVM provided an accuracy of 71% compared to the single SVM of 45%

Statistical Parsing by Machine Learning from a Classical Arabic Treebank

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year