Machine Leaning Based Urdu Language Tutor for Primary School Students

Khalil, Fatima; Sardar, Farva; Gull, Mehreen; Aslam, Muhammad; Ahmad, Nafees; Martinez-Enriquez, A. M.

doi:10.1007/978-3-030-60884-2_15

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reported average accuracy of the system is 92.8%. The only reported complete educational system for Urdu handwriting, 'Urdu Mu'allam' [51], teaches Urdu reading, writing, and listening. For handwriting, the system only evaluates the shape of the writing sample.…”

Section: A Handwriting Evaluation In Literaturementioning

confidence: 99%

Automating the Evaluation of Urdu Handwriting for Novice Writers With Localized Feedback

Khalid,

Aslam

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Handwriting is one of the basic skills crucial in communication and early childhood development. It plays a major role in the psychomotor and cognitive growth of children. Therefore, it is required to devise interesting and modern ways to teach this skill. A handwriting tutoring system designed to teach Urdu transcription engagingly and efficiently can help to solve this problem. Essential to these teaching systems is the quality evaluation module that analyzes and evaluates the quality of transcription based on legibility and writing process and provides feedback to the writer. This feedback can not only help the young students to improve and identify their mistakes but can also help to repair the handwriting skills of any individual wanting to improve their transcription abilities. The existing handwriting evaluation methods are either designed for Arabic writing style, or those designed for Urdu need more comprehensive feedback on the quality of handwriting. Therefore, it is the need of time to develop a technology-assisted educational system for teaching Urdu handwriting that evaluates and provides intelligent feedback to the learners. In this work, we have proposed an innovative model for evaluating different aspects of Urdu handwriting and providing qualitative and quantitative feedback. The system takes on real-time writing samples, and its transcription profiler module then individually evaluates six aspects of handwriting, including legibility, shape formation, the direction of writing, proportion with the baseline, count, and order of strokes. Based on the evaluation, the feedback generator module provides overall feedback and detects mistakes made while writing. A machine learning-based hybrid architecture is constructed that combines spatio-temporal and structural features to do a quality analysis of each aspect of the transcript. The contribution of this work is twofold: first, to develop a standard for evaluating the quality of Urdu handwriting or any cursive writing script, and second, to create an architecture that relies on machine learning-based models to provide detailed and intelligent feedback while learning handwriting. The system's performance is verified by comparing it with the test data evaluated by real teachers. It achieved an average of 97% accuracy among all character classes in all handwriting aspects. The results show that the system is effective in identifying and correcting mistakes, thus helping individuals seeking to improve their handwriting skills.

show abstract

Section: A Handwriting Evaluation In Literaturementioning

confidence: 99%

Automating the Evaluation of Urdu Handwriting for Novice Writers With Localized Feedback

Khalid,

Aslam

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consequently, special attention is being paid to such types of online education technology as chatbots, which are not just a part of online learning: this is computer-assisted learning equally. − A chatbot is an algorithm-powered program with questions and answers which come to the user in the form of text messages. This allows the bot to communicate virtually with users around the clock.…”

mentioning

confidence: 99%

“…Moreover, the issue of homework effectiveness during online education has been addressed. 11−14 There have already been articles about how challenging it was for teachers to work because of COVID- 19, and what measures they took in order to enhance students' learning 15,16 and prepare them for tests and exams. 17,18 At the students' end, not only is it difficult to learn during the pandemic, but there are special challenges to properly prepare for the exam online to pass it successfully.…”

mentioning

confidence: 99%

Chemist Bot as a Helpful Personal Online Training Tool for the Final Chemistry Examination

et al. 2021

View full text Add to dashboard Cite

The global pandemic complicated both learning chemistry and, importantly, preparation for examinations. One of the problems which students face is the lack of teachers’ attention, whereas the implementation of such distance learning technology as a chatbot represents one possible solution. The Chemist Bot was created specifically to help Russian students prepare for the final chemistry examination. It provides various activities, including the access to theory, the opportunity to complete the real exam tasks, and educational games. Over a period of three months, 465 students took part in the Chemist Bot activities, and 52 of them shared their real examination results. It was revealed that both the completion of exam tasks and participation in extracurricular activities are helpful for training and preparation. In the present experiment, it has been shown that 13 of the 15 students who wrote the full United State Exam (USE) sample in the bot and scored at least 53 out of 66 points in the Chemist Bot then scored over 80 points on the real USE (which is 80% of the maximum USE score and is considered excellent by FIPM – Federal Institute of Pedagogical Measurements, Russian Federation). Currently, the Chemist Bot is involved in preparation of new participants: those students who are going to pass the exam in the summer of 2022.

show abstract

Developing mobile language learning applications: a systematic literature review

Kumar

Goundar

2022

Educ Inf Technol

View full text Add to dashboard Cite

Machine Leaning Based Urdu Language Tutor for Primary School Students

Cited by 3 publications

References 8 publications

Automating the Evaluation of Urdu Handwriting for Novice Writers With Localized Feedback

Automating the Evaluation of Urdu Handwriting for Novice Writers With Localized Feedback

Chemist Bot as a Helpful Personal Online Training Tool for the Final Chemistry Examination

Developing mobile language learning applications: a systematic literature review

Contact Info

Product

Resources

About