Implicit learner assessment based on semantic relevance of tags

Kardan, Ahmad A.; Sani, Mohammadreza Fani; Modaberi, Somayeh

doi:10.1016/j.chb.2015.10.027

Cited by 9 publications

(6 citation statements)

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“…Tags, as the identification of learner features, demonstrate the common features of a group of learners. Depending on learner features, the tags can be classified into basic tags and extended tags [15].…”

Section: Construction Of User Portrait On Online Learning Platformmentioning

confidence: 99%

An Early-Warning Model for Online Learners Based on User Portrait

Sun¹,

Chai²

2020

ISI

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In the age of the Internet, online learning is an important learning strategy. At present, a large number of data on learning behavior have been generated on various online education platforms. It is difficult to grasp the learning situation of the numerous learners of these platforms according to the massive data. User portrait offers a possible solution to the problem. This paper firstly classifies the portrait of online learners into three dimensions, and constructs the tag system of learner portrait based on the data fields of online learning platform. Then, the learning behavior data of online learners were analyzed in details. Online learners were divided into multiple groups through data mining, and the learner portrait was generated. From the five dimensions of learner portrait, the learning situation was analyzed to master the learning information of learners. Based on the analysis results, the four-dimensional early-warning of learning situation was realized through sequence analysis and association rule mining. The research results provide a good reference for the improvement of online learning.

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Section: Construction Of User Portrait On Online Learning Platformmentioning

confidence: 99%

An Early-Warning Model for Online Learners Based on User Portrait

Sun¹,

Chai²

2020

ISI

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“…Social tagging has also received attention in the field of Technology-enhanced Learning (TeL) mainly due to the emergence of Open Educational Resources (OERs) initiatives worldwide, which have focused on supporting the process of organizing, classifying and storing digital educational resources in the form of Learning Objects (LOs) and their educational metadata in web-based repositories referred to as Learning Object Repositories (LORs) (Ehlers, 2011). Furthermore, social tagging in TeL has recently been also considered for other purposes, such as supporting student assessment (e.g., Kardan et al, 2016) or supporting the provision of personalized learning objects and pathways to students (e.g., Cao et al, 2015). However, these approaches are still not widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Studying tag vocabulary evolution of social tagging systems in learning object repositories

Zervas

Sampson

Pelliccione

2016

Smart Learn. Environ.

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In the field of Technology-enhanced Learning (TeL), social tagging has been applied to Learning Object Repositories (LORs) mainly as a means:(a) to offer an alternative way of classifying the Learning Objects (LOs) based on the tag vocabulary created by the end-users of the LOs, and (b) to facilitate the enhancement of LOs' descriptions via collaborative tagging. However, in order to be able to understand how a social tagging system performs and whether it can deliver the aforementioned goals, it is important to be able to investigate the evolution of the tag vocabulary, which constitutes the core component of a social tagging system. Within this context, research has focused on different facets of social tagging systems such as the growth of the tag vocabulary, the frequency and reuse of tags, as well as the stability of the tag vocabulary but there are only sporadic studies for investigating these issues in the field of LORs. This paper aims to contribute in studying how social tagging systems perform in the context of LORs by investigating the evolution of the tag vocabulary in OpenScienceResources Repository, a science education domain specific repository with a rich dataset operating in Europe for 5 years.

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“…The idea of machine-reasonable reports does not infer some mystical manmade brainpower which permits machines to grasp human mumblings (Agus, Penna, Peró-Cebollero, & Guàrdia-Olmos, 2016). It just demonstrates a machine's capacity to take care of an all-around characterized issue by performing very much characterized operations on existing all around characterized information (Kardan et al, 2016). Rather than requesting that machines comprehend individuals' dialect; it includes requesting that individuals attempt (Agus et al, 2016).…”

Section: Unorganized Web Data and Understandability Issuesmentioning

confidence: 99%

“…Such assessments can support educational system in many ways. E-assessment is considered a huge change in the field of educational assessment when considering traditional assessment methods (Kardan, Sani, & Modaberi, 2016). At large scale enabling devices and hardware for conducting electronic examination by huge number of students seems hectic due to its security threats towards educational dishonesty or plagiarism issues.…”

Section: E-assessmentmentioning

confidence: 99%

“…A technique for creating feedback using Semantic Web based approach for generating question oriented test assessment (del Mar Sánchez-Vera, Fernández-Breis, Castellanos-Nieves, Frutos-Morales, & Prendes-Espinosa, 2012). Other assessments methods recently introduced include personalized self-assessment on feedback (Belcadhi, 2016), implicit leaners assessment on the bases of relevance (Kardan et al, 2016), and automated distributed knowledge assessment (Stantchev et al, 2015), these are built-up on Semantic Web technologies. Moreover, work on Heterogenous data appearing in for assessment is still lacks to its full extents (Musen, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

E-Assessment Data Compatibility Resolution Methodology with Bidirectional Data Transformation

Malik

Ahmad

2017

EURASIA J MATH SCI T

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Electronic Assessment (E-Assessment) also known as computer aided assessment for the purposes involving diagnostic, formative or summative examining using data analysis. Digital assessments come commonly from social, academic, and adaptive learning in machine readable forms to deliver the machine scoring function. To achieve real-time and smart e-assessment, data modeling needs dramatic improvements at the level of representation which will improve examinees to gain prompt response instantly after attempting exams. Whereas, computer based inference to gain intelligence in assessing results through computations is becoming a useful feature in todays' testing systems. Induction of rule base linked data is desired to be reformed from the old tradition data model found either in spread sheet or relational database used for data storage. These data forms are essential to be converted into semantical annotated form to support Artificial Intelligence. This can be done with the use of Semantic Web data model Resource Description Framework (RDF) built-up using hierarchal and linked data representation. Updating assessment source data later for results is one of the hardest problem of all viabilities in traditional and semantically augmented systems when combined for evaluating. This study purposes a methodology of bidirectional data transformation back and forth from Relational Database (RDB) and RDF. A case study representing qualitative analysis of transforming student's results information into RDF store reforming data as ready to be analyzed. At the end of this study outcomes show how data updating becomes feasible by following proposed data transformation procedure.

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Implicit learner assessment based on semantic relevance of tags

Cited by 9 publications

References 10 publications

An Early-Warning Model for Online Learners Based on User Portrait

An Early-Warning Model for Online Learners Based on User Portrait

Studying tag vocabulary evolution of social tagging systems in learning object repositories

E-Assessment Data Compatibility Resolution Methodology with Bidirectional Data Transformation

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