Keldec

Kanika, .; Chakraverty, Shampa; Chakraborty, Pinaki; Agnihotri, Shikhar; Mohapatra, S.; Bansal, Prakriti

doi:10.1145/3342827.3342849

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…A different way to recommend items is by computing the similarities between items in the matrix. This approach is called item-based approach [12].…”

Section: Related Workmentioning

confidence: 99%

Educational Recommender System based on Learner’s Annotative Activity

Mazhoud

Kalboussi

Kacem

2021

Int. J. Emerg. Technol. Learn.

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In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.

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“…A different way to recommend items is by computing the similarities between items in the matrix. This approach is called item-based approach [12].…”

Section: Related Workmentioning

confidence: 99%

Educational Recommender System based on Learner’s Annotative Activity

Mazhoud

Kalboussi

Kacem

2021

Int. J. Emerg. Technol. Learn.

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“…The system, hence, evaluates semantic closeness of every website in IR with the image idea as well as the core teaching concepts. It uses semantic similarity methods using the Global Vectors for Word Representation (GloVe) algorithm [13]. The overall semantic similarity score represents how much similar the content of a web page is to a given image category in G cat and any core teaching concept in TC R .…”

Section: Vista—a Pedagogical Toolmentioning

confidence: 99%

“…The overall semantic similarity score represents how much similar the content of a web page is to a given image category in G cat and any core teaching concept in TC R . It is calculated using the similarity metrics proposed by Reference [13]. A list of websites sorted according to similarity can be seen in the appendix.…”

Section: Vista—a Pedagogical Toolmentioning

confidence: 99%

VISTA: A teaching aid to enhance contextual teaching

Kanika

Chakraverty

Chakraborty

et al. 2021

Comp Applic In Engineering

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In this study, an innovative tool for enabling contextual teaching based on visual inputs, named Visual Stimuli‐based Teaching Aid (VISTA), was developed. This system establishes the meaning and significance of concepts that are taught in the classroom within different environmental contexts. The idea of contextual teaching has been deliberated for more than a decade now. Nevertheless, using this in a classroom with limited time and resources has several challenges. VISTA leverages the ubiquitous mobile devices to boost conventional teaching methods with contextual teaching–learning. With VISTA, contextual teaching does not require any specialized device, expensive sensors, or simulated environment. To assess the applicability of VISTA in computer science education, we gathered the responses and experiences from 42 computer science teachers teaching in various universities of India. The responses garnered indicate that contextualization through VISTA is relevant as well as interesting. In total, 86.31% of the teachers found the contextual information provided by VISTA to be significantly related to both the captured images and the concepts presented in the class. These results encourage the use of VISTA as a pedagogical tool for contextual teaching.

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