Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example

A Case Study of Using Edmodo to Enhance Computer Science Learning for Engineering Students

Edmodo is becoming increasingly used in higher education. It helps teachers to easily share learning content with students, and communicate with them better. Several studies demonstrate its effectiveness in improving students’ results and satisfaction with the learning process. In this paper, we describe our experience using Edmodo for courses in computer sciences designed for engineering students. We tested Edmodo in three courses delivered in a blended learning mode: the assembly language programming, the operating systems, and the PHP language programming. The learning scenario adopted for these courses was already presented in our previous work on the pedagogy of integration. Results show that the use of Edmodo within the pedagogy of integration enhances both learning and teaching experiences

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

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

Fine-Tuned BERT Model for Large Scale and Cognitive Classification of MOOCs

The quality assurance of MOOCs focuses on improving their pedagogical quality. However, the tools that allow reflection on and assistance regarding the pedagogical aspects of MOOCs are limited. The pedagogical classification of MOOCs is a difficult task, given the variability of MOOCs' content, structure, and designs. Pedagogical researchers have adopted several approaches to examine these variations and identify the pedagogical models of MOOCs, but these approaches are manual and operate on a small scale. Furthermore, MOOCs do not contain any metadata on their pedagogical aspects. Our objective in this research work was the automatic and large-scale classification of MOOCs based on their learning objectives and Bloom’s taxonomy. However, the main challenge of our work was the lack of annotated data. We created a dataset of 2,394 learning objectives. Due to the limited size of our dataset, we adopted transfer learning via bidirectional encoder representations from Transformers (BERT). The contributions of our approach are twofold. First, we automated the pedagogical annotation of MOOCs on a large scale and based on the cognitive levels of Bloom’s taxonomy. Second, we fine-tuned BERT via different architectures. In addition to applying a simple softmax classifier, we chose prevalent neural networks long short-term memory (LSTM) and Bi-directional long short-term memory (Bi-LSTM). The results of our experiments showed, on the one hand, that choosing a more complex classifier does not boost the performance of classification. On the other hand, using a model based on dense layers upon BERT in combination with dropout and the rectified linear unit (ReLU) activation function enabled us to reach the highest accuracy value

Intelligent System Using Deep Learning for Answering Learner Questions in a MOOC

Despite the great success of Massive Open Online Courses (MOOCs), the success rate of learners remains very low. Therefore, a lot of research has been done to understand and solve this abandonment problem. This work is part of the same effort which aims to improve learning in MOOCs. We offer an intelligent system capable of assisting the learner by providing answers to all his questions on the subjects covered in the MOOC. The architecture of our system is based on new advances in artificial intelligence, in particular the applications of deep learning in the field of natural language processing (NLP). The results obtained are quite interesting and demonstrate the relevance of our solution

MTBERT-Attention: An Explainable BERT Model based on Multi-Task Learning for Cognitive Text Classification

In recent years, there has been a lot of focus on Bloom’s taxonomy-based classification of E-Learning materials. Researchers employ different methods and features. In our previous works, we have dealt with this problem via different techniques and algorithms. we started by boosting traditional machine learning algorithms then we moved on to deep learning algorithms by proposing a bidirectional GRU (BI-GRU) model combined with word2vec, finally we also proposed a fine-tuned model of bidirectional encoder representations from Transformers (BERT). The limitations of our model’s performance as well as the lack of an annotated dataset lead us to explore a novel approach to the cognitive classification of text. First, we propose MTBERT-Attention, a unique and explainable model based on multi-task learning (MTL), BERT, and the co-attention mechanism. MTL enhances our primary task’s generalization capacity and permits data augmentation. The usage of BERT as a stack for knowledge transfer between activities has been improved. The co-attention mechanism put particular emphasis on the significant aspects of the learning objective. We propose an explainability framework based on the attention mechanism. Lastly, we carry out in-depth tests to assess the viability and efficiency of the suggested model and the explainability Framework. Our proposed model outperforms the baseline models for loss, F1-score, and accuracy. It attained an overall classification accuracy of 97.71% with the test set and effectively classifies learning objectives that use unclear action verbs from Bloom’s taxonomy. On the other hand, to prove the performance of our explainability Framework, we conduct a qualitative and quantitative study on the quality of the explanations as well as on the computational cost. We adopt Local Interpretable Model-agnostic Explanations (LIME) as a baseline for comparison. Our experiments show that our proposed approach for explainability outperforms the LIME explainer in terms of fidelity and computational cost

Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

A learning environment generates massive knowledge by means of the services provided in MOOCs. Such knowledge is produced via learning actor interactions. This result is a motivation for researchers to put forward solutions for big data usage, depending on learning analytics techniques as well as the big data techniques relating to the educational field. In this context, the present article unfolds a uniform model to facilitate the exploitation of the experiences produced by the interactions of the pedagogical actors. The aim of proposing the said model is to make a unified analysis of the massive data generated by learning actors. This model suggests making an initial pre-processing of the massive data produced in an e-learning system, and it’s subsequently intends to produce machine learning, defined by rules of measures of actors knowledge relevance. All the processing stages of this model will be introduced in an algorithm that results in the production of learning actor knowledge tree

Artificial Intelligent in Education

The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges

Artificial Intelligent in Education

The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges