BERT and Prerequisite Based Ontology for Predicting Learner’s Confusion in MOOCs Discussion Forums

Chanaa, Abdessamad; Faddouli, Nour-eddine El

doi:10.1007/978-3-030-52240-7_10

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…The basis of the intelligent tutoring system is to establish a learner model [AKS20, CLL*15, PPŘ*17] to evaluate the level of knowledge. In the education process, intelligent auxiliary methods can effectively help learners to perform efficient learning, such as text simplification [BDCM20], detection of their confused state [CEF20a], detection of their engagement [GMB*20], identification of their needs [CLP*20] and intelligent recommendation [CEF20b].…”

Section: Related Workmentioning

confidence: 99%

Accompany Children's Learning for You: An Intelligent Companion Learning System

Qian

Jiang

et al. 2023

Computer Graphics Forum

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Nowadays, parents attach importance to their children's primary education but often lack time and correct pedagogical principles to accompany their children's learning. Besides, existing learning systems cannot perceive children's emotional changes. They may also cause children's self‐control and cognitive problems due to smart devices such as mobile phones and tablets. To tackle these issues, we propose an intelligent companion learning system to accompany children in learning English words, namely the Intelligent Augmented Reality Educator (IARE). The IARE realizes the perception and feedback of children's engagement through the intelligent agent (IA) module, and presents the humanized interaction based on projective Augmented Reality (AR). Specifically, IA perceives the children's learning engagement change and spelling status in real‐time through our online lightweight temporal multiple instance attention module and character recognition module, based on which analyses the performance of the individual learning process and gives appropriate feedback and guidance. We allow children to interact with physical letters, thus avoiding the excessive interference of electronic devices. To test the efficacy of our system, we conduct a pilot study with 14 English learning children. The results show that our system can significantly improve children's intrinsic motivation and self‐efficacy.

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Section: Related Workmentioning

confidence: 99%

Accompany Children's Learning for You: An Intelligent Companion Learning System

Qian

Jiang

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

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“…Another study focused on solving the educational and non-educational text classification problem based on deep Gaussian Processes (DGPs) and Word2vec [14]. Also, Abdessamad Chanaa et al examined confusion state of learners by analysing MOOCs discussion forums using neural network classification algorithm and ontologies [15].…”

Section: Related Workmentioning

confidence: 99%

E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

Chanaa

Faddouli

2021

Int. J. Emerg. Technol. Learn.

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Massive Open Online Courses (MOOCs) have recently become a very motivating research field in education. Analyzing MOOCs discussion forums presents important issues since it can create challenges for understanding and appropriately identifying student sentiment behaviours. Using the high effectiveness of deep learning, this study aims to classify forum posts based on their sentiment polarity using two experiments. The first use the three known sentiment labels (positive/negative/neutral) and the second one employs sevens labels. The classification method implemented the Hierarchical Attention Network (HAN) algorithm; it combines the attention mechanism with a hierarchical network that simulates the same hierarchical structure of the document. The analysis of 29604 discussion posts from Stanford University affirms the effectiveness of our model. HAN achieved a classification accuracy of 70.3%, which surpassed the other prediction results using usual text classification models. These results are promising and have implications on the future development of automated sentiment analysis tool on e-learning discussion forum.

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Detecting Urgent Instructor Intervention Need in Learning Forums with a Domain Adaptation

Candido

Maia

2023

Lecture Notes in Networks and Systems

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BERT and Prerequisite Based Ontology for Predicting Learner’s Confusion in MOOCs Discussion Forums

Cited by 7 publications

References 5 publications

Accompany Children's Learning for You: An Intelligent Companion Learning System

Accompany Children's Learning for You: An Intelligent Companion Learning System

E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

Detecting Urgent Instructor Intervention Need in Learning Forums with a Domain Adaptation

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