Models of Digital Educational Resources Indexing and Dynamic User Proﬁle Evolution

Slimani, Hamid; Faddouli, Nour-eddine El; Bennani, Samir; Amrous, Naila

doi:10.3991/ijet.v11i01.4932

Cited by 5 publications

(2 citation statements)

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“…A model based on traces of the objects, which comes from xapi and XMS-Caliper, can meet the following needs: Collection of data processing traces, model and reuse of the indicator. Such a model is obtained by enrichment based on the design methodology of a trace-based system (SBT) [17].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments

Batchakui

Djotio

Moukouop

et al. 2021

Int. J. Emerg. Technol. Learn.

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This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.

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Section: Introductionmentioning

confidence: 99%

Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments

Batchakui

Djotio

Moukouop

et al. 2021

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

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“…For example, the author in [19] proposed a classification model for unstructured documents via combining the Naïve Bayes classifier with the predictive analysis for learning systems. Also, the author in [31] proposed a model of digital educational resources indexing and dynamic user profile evolution. The aim of the model is to develop user profiles.…”

mentioning

confidence: 99%

Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

Hadioui¹,

Faddouli²,

Touimi³

et al. 2017

Int. J. Emerg. Technol. Learn.

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Abstract-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.Keywords-learning analytics, operational data, machine learning, big data analysis, knowledge management IntroductionCurrently, the field of education is flourishing rapidly throughout the world, due to the changes that have been occurring in this area with the implementation of Massive Open Online Courses MOOCs [1]. Great many research projects have been funded in order to draw the attention of researchers in this field to work on such massive data, conducting in-depth studies of MOOCs (COURSERA, OPEN ODX, etc.).MOOCs generate big data in the form of activity traces. Such data are of three various types, namely: structured, semi-structured and unstructured data. In [2], the author has conducted an in-depth study on the types of data generated by the interactions of educational actors in online learning systems. The structured data are those found in the databases; the semi-structured are those found in the XML and JSON files, whereas the unstructured are those found in documents, video recordings, audio, etc.iJET -Vol. 12, No. 11, 2017 151Big data analysis [3] represents the combination of big data techniques with learning analysis. This combination enables to envision integrating Learning analytics (LA) algorithms with learning systems based on big data. Learning analytics (LA) represent a set of algorithms useful for the analysis and pre-processing of the massive data originally generated in the MOOCs. Indeed, we find two approaches: one supervised and another unsupervised [4,5]. On the other side, big data represent the tendency of actors to store massive data of different natures and to process them in parallel in tune with an architecture [6] built on three key elements: HDSF, MapReduce and YARN. Research problematicThe massive data generated by the services which are offered within the MOOCs systems are structured, semi-structured and unstructured. Given such fact, prerequisite is to make an in-depth analysis focusing on all the massive data dimensions. To this end, the author in [7] identifies three dimensio...

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