Iraqi-E-learning-Emotion-Dataset
Description
This dataset presents an Iraqi E-Learning Emotion Dataset that captures the emotional experiences of students and academic staff toward e-learning in Iraqi universities. The data were collected in 2025 using a voluntary, structured online questionnaire distributed across public and private universities in Iraq. Participants were asked to describe their personal experiences, feelings, and challenges related to e-learning through open-ended free-text responses written in the Iraqi Arabic dialect. In addition to the textual responses, participants selected one emotion label that best represented their expressed experience from a predefined set of six categories: Joy, Trust, Anticipation, Anger, Anxiety, and Neutral. This design enables the dataset to support supervised emotion classification tasks while preserving the authenticity of self-reported emotional expression. The dataset consists of 1,182 annotated text entries stored in a UTF-8 encoded CSV file. A standardized preprocessing pipeline was applied to ensure data quality and consistency, including the removal of duplicate entries, URLs, non-text elements, emojis, foreign characters, and normalization of common Iraqi Arabic spelling variations. All preprocessing steps were implemented in Python and are fully documented to support reproducibility. This dataset addresses a notable gap in Arabic Natural Language Processing resources, as most existing Arabic emotion datasets are derived from social media and do not adequately represent educational contexts. By focusing on e-learning environments in higher education and on a low-resource Arabic dialect, the dataset provides context-specific emotional data that can be reused in research on Arabic emotion analysis, affective computing, educational data mining, learner engagement, and the development of emotion-aware intelligent tutoring systems. The dataset is openly available for research and educational purposes.
Files
Steps to reproduce
The data were collected through an online open-ended questionnaire and preprocessed using Python scripts for cleaning and normalization.
Institutions
- University of Information Technology and Communications