Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning

Mansur, Andi Besse Firdausiah; Yusof, Norazah

doi:10.1016/j.compedu.2012.11.011

Cited by 35 publications

(5 citation statements)

References 10 publications

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“…Studies in this category focus on evaluating or developing community detection method that is convenient for online learning data and efficient in classifying learners' behaviors and patterns of interactions gathered from online learning systems. Firdausiah Mansur and Yusof (2013) proposed a learners' clustering model that combines ontology, clustering techniques and the meaningful learning concept and attributes to categorize learners in Moodle E‐learning system into three meaningful clusters: active, constructive, and intentional. Adraoui et al (2019) proposed a new algorithm to detect and assess learning communities in social learning environments.…”

Section: Resultsmentioning

confidence: 99%

Detecting communities using social network analysis in online learning environments: Systematic literature review

Yassine¹,

Kadry²,

Sicilia³

2021

WIREs Data Min & Knowl

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Uncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities. This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

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Section: Resultsmentioning

confidence: 99%

Detecting communities using social network analysis in online learning environments: Systematic literature review

Yassine¹,

Kadry²,

Sicilia³

2021

WIREs Data Min & Knowl

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“…The Analysis of Social Networks (ASN) is one of the main prominent approaches that classify and analyze the user's behavior (Mansur & Yusof, 2013) at many stages and levels of abstraction especially they are relying on graph specification where nodes and edges represent the users and their relations respectively. In literature, many initiatives are dedicated to study ASN quantitatively and qualitatively.…”

Section: Related Workmentioning

confidence: 99%

Recommendations-based on semantic analysis of social networks in learning environments

Khaled¹,

Ouchani

Chohra

2019

Computers in Human Behavior

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“…[21] explores the possibility of using a hierarchical clustering and analytical procedure to diagnosis individual and class learning and misconceptions; Ref. [22] proposed the ontology and the clustering techniques for classifying the behavior of the students which is based on the level of meaningful learning characteristics from their log activities in Moodle e-learning system; Ref. [23] use the frequency of access and the duration of sessions to establish several categories of learners by cluster analysis, which depict the differences among the cohort in terms of participation; or Ref.…”

Section: Background and Motivationmentioning

confidence: 99%

A Cluster-based Personalized Item Recommended Approach on the Educational Assessment System

Chang²,

Chiu³

et al. 2015

Int. J. Emerg. Technol. Learn.

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Abstract-Personalized item recommendation enables the educational assessment system to make deliberate efforts to perform appropriate assessment strategies that !t the needs, purposes, preferences, and interests of individual teachers. This study presents a dynamically personalized itemrecommendation approach that is based on clustering inserve teachers with assessment compiling interest and preference characteristics to recommend available, best-fit candidate items to support teachers to construct their classroom assessment. A two-round assessment constructing activity was being adopted to collect and extract these teacher' assessment knowledge (item selected preference behaviors), and through the designed item-recommendation mechanism to facilitate IKMAAS [1] to recommend proper items to meet different individual in-serve teachers. To evaluate the effectiveness and usability for the cluster-based personalized item-recommendation, the assessment system log analysis and the questionnaire collected from participating teachers' perceptions were being used. The results showed the proposed item-recommendation approach based on clustered teachers' assessment knowledge can effectively improve their educational assessment construction.Index Terms-architectures for educational technology system; authoring tools and methods; elementary education; evaluation methodologies; human-computer interface I. BACKGROUND AND MOTIVATIONRecommender Systems and related recommender technology applications can provide personalized information services by adopting knowledge discovery (such as Bayesian networks, decision trees etc.) [2] or data mining techniques from the actions and attributes of users (personality factors, behavioral factors, etc.) [3] are now considered to be the most promising way wherein many domains ranging from electronic commerce to personalized service and knowledge management, they not only enable to present specific objects in accordance with the different users' interest or need on the basis of his or her known preferences or reference to those of other users with similar characteristics [4], but also could efficiently filter out the overload of information or provide favorable information when the users need to make decisions [5]. Likewise, the applications of recommendation have gradually appeared in the area of e-learning and e-education, it seems to be as "right-hand man of learners and instructors" which recommend the best-fit educational resources that directly meet the learning needs of different learners, or help the individual instructors making better teaching decisions

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Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning

Cited by 35 publications

References 10 publications

Detecting communities using social network analysis in online learning environments: Systematic literature review

Detecting communities using social network analysis in online learning environments: Systematic literature review

Recommendations-based on semantic analysis of social networks in learning environments

A Cluster-based Personalized Item Recommended Approach on the Educational Assessment System

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