Semantic Annotation of Resources of Distance Learning Based Intelligent Agents

Abderrahman, Chekry; Aziz, Oriche; Mohamed, Khaldi

doi:10.3991/ijep.v4i1.2845

Cited by 3 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these research limitations, we will be exploring in future works the hybridization of ML techniques with the ontology-based recommendation approach as suggested by [34] to upgrade the variety of learner features used for recommendation in adherence to the MOOC recommendation criteria. In this sense, educational resources management systems use ontologies to share information and annotate semantically these resources [35]. The semantic annotations will enable matching MOOC content knowledge with the sought knowledge by a target learner.…”

Section: 4mentioning

confidence: 99%

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

2022

View full text Add to dashboard Cite

The phenomenon of high dropout rates has been the concern of MOOC providers and educators since the emergence of this disruptive technology in online learning. This led to the focus on learner motivation studies from different aspects: demotivation signs detection, learning path personalization, course recommendation, etc. Our paper aims to predict learner motivation for MOOCs to select the right MOOC for the right learner. So, we predict the motivation in an educational data mining approach by extracting and preprocessing learners' navigation traces on a MOOC platform and building a machine learning model that predicts accurately a given learner motivation for a MOOC. The comparison of the performance of four supervised learning algorithms resulted in the selection of the random forest classifier as a modeling technique for motivation prediction. Afterward, the Machine Learning-based recommendation function was tested for learners of the MOOC platform dataset to recommend the Top-10 MOOCs suitable for the target learner. Finally, further research on learner characteristics considered in recommender systems could enlarge the recommendation scope of MOOCs and maintain learner motivation.

show abstract

Section: 4mentioning

confidence: 99%

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

2022

View full text Add to dashboard Cite

show abstract

“…Abderrahman et al [19] proposed a semantic annotation system for information extraction from dispersed online educational resources such as electronic documents. This information is stored in a warehouse that consists of two parts: database and descriptors.…”

Section: Classifiersmentioning

confidence: 99%

Text Mining: Design of Interactive Search Engine Based Regular Expressions of Online Automobile Advertisements

Jalal

2020

Int. J. Eng. Ped.

View full text Add to dashboard Cite

Technology world has greatly evolved over the past decades, which led to inflated data volume. This progress of technology in the digital form generated scattered texts across millions of web pages. Unstructured texts contain a vast amount of textual data. Discover of useful and interesting relations from unstructured texts requires more processing by computers. Therefore, text mining and information extraction have become an exciting research field to get structured and valuable information. This paper focuses on text pre-processing of automotive advertisements domains to configure a structured database. The structured database was created by extract the information over unstructured automotive advertisements, which is an area of natural language processing. Information extraction deals with finding factual information in text using learning regular expressions. We manually craft rule-based specific approaches to extract structured information from unstructured web pages. Structured information will be provided by user-friendly search engine designed for topic-specific knowledge. Consequently, this information that extracted from these advertisements uses to perform a structured search over certain interesting attributes. Thus, the tuples are assigned a probability and indexed to support the efficiency of extraction and exploration via user queries.

show abstract

“…When the activity is carried out with students, they then read a text given by the teacher in the classroom and, on it, write notes on certain parts of the text [21]. The annotations can be free text or they can also refer to concepts that appear in the text and that are defined in an ontology provided by the teacher [1]. The activity ends by giving the teacher a work where a discussion of the annotated text is developed using the annotations made.…”

Section: Introductionmentioning

confidence: 99%

Use of Critical Annotation and Interactive Fiction for the Creation of Digital Educational Content

Diez-Sanmartín

Gayoso-Cabada

Cabezuelo

2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Interactive fiction and critical annotation are two very important activities in the field of philology teaching. The first activity aims to turn the reader into an ac-tive element in reading electronic books. For this, different gadgets and tech-niques are used that force the reader to have to make decisions in the reading process. With respect of the annotation, it is an activity that aims to enrich liter-ary texts with meta-information that allows explaining or extending the infor-mation that appears in the contents of a book. Both activities are fundamental in the training of philology students. In general, there are few specific electronic publishing tools for these activities, and those that exist have a professional ori-entation. In this article, two tools are described that have implemented the func-tionality necessary to carry out these activities but with an academic orientation. In this way, students can learn both activities in a friendly, intuitive environ-ment designed for education.

show abstract

Semantic Annotation of Resources of Distance Learning Based Intelligent Agents

Cited by 3 publications

References 2 publications

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

Text Mining: Design of Interactive Search Engine Based Regular Expressions of Online Automobile Advertisements

Use of Critical Annotation and Interactive Fiction for the Creation of Digital Educational Content

Contact Info

Product

Resources

About