Online learning has experimented a notable increase in recent years. On the one hand, traditional face-to-face institutions are offering blended systems, supported by online platforms. On the other hand, online educational institutions receive each year a larger number of students that enroll in university courses. In such situation, with a high number of students and many activities to grade, the teacher experiments a work overload that makes it difficult to perform quality tasks. It is therefore urgent to support teachers with systems that alleviate the load of time-consuming tasks, so that teacher can focus on providing quality.
Natural Language Processing techniques have experimented a great change in the recent years. Since the arrival of transformer-based models in 2017, language models have become multilingual and now provide an unforeseen level of accuracy in different tasks. Translation techniques now offer human comparable results, text classification goes far beyond simple keyword matching and now is able to take complex semantics into account, to name a few improvements. The disruptive impact that ChatGPT has achieved in society is the reflect of the great accuracy of Natural Language Processing systems. Such development translates into innovative solutions for different fields, including education. Automatic Short-Answer Grading (ASAG) systems are an example, with many other possibilities of interest for current research. Pending challenges include feedback generation, fake text detection, explanation and traceability of classifications, exams generation, to name a few.
This workshop entitled “Advances on the use of Natural Language Processing techniques in Education” aims to explore new and innovative research on the application of artificial intelligence language techniques as support or automatization of the tasks required in the teaching/learning process, such as grading, exams generation or feedback generation. We welcome researchers and practitioners in the field to present their novel and innovative solutions.
Topics of interest include (but not limited to):
- Automated Short-Answer Grading (ASAG) systems and, in general, NLP for automatic answer grading.
- Automated Feedback Generation.
- Analysis of the impact in the learning process of Automated Grading Systems.
- Validation of answers including semantics, syntactics and readability.
- Detection techniques for fake texts and plagiarism.
- eXplainable Artificial Intelligence for the construction of automatic grading systems.
- Exams and assignments generation.
- Ethical implications and challenges.
- Luis de la Fuente Valentín, Universidad Internacional de La Rioja, Spain.
- Elena Verdú Pérez, Universidad Internacional de La Rioja, Spain.
- Giuseppe Fenza, University of Salerno, Italy
- Mariacristina Gallo, University of Salerno, Italy
- Antonio Jesús Fernández García, Universidad de Almería, Spain
- Isabel Segura Bedmar, Universidad Carlos III de Madrid, Spain.
- S.P. Raja, Vellore Institute of Technology, India
- Alfonso Ortega de la Puente, Universidad Internacional de La Rioja, Spain.
- Xiomara Patricia Blanco Valencia, Universidad Internacional de La Rioja, Spain.