Recommender System for e-Learning based on Personal Learning Style

Qomariyah, Nunung Nurul; Fajar, Ahmad Nurul

doi:10.1109/isriti48646.2019.9034568

Cited by 23 publications

(11 citation statements)

References 10 publications

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“…Qomariyah et al [18] have implemented a recommendation framework model to help students find the right content during their research and keep them inspired. By creating more acceptable material content, the students would find it beneficial and the teachers as they want to understand their students' learning style.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

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The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/ learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student's fraternity as it is during this COVID-19 pandemic period.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

View full text Add to dashboard Cite

show abstract

“…Qomariyah and Fajar [22] proposed the design and implementation of an e-learning recommender system based on a logic approach called active pairwise relation learner (APARELL). eir proposed e-learning recommender system helped students to select the best materials according to their preferences.…”

Section: Recommender Systems In the Domain Of Educationmentioning

confidence: 99%

“…eir proposed e-learning recommender system helped students to select the best materials according to their preferences. e authors in [22] justified the big potential to implement a personalized recommender system in e-learning based on the student's learning styles using an ontology of material content based on different learning styles. Similar to [22], Li et al [20] proposed a personalized semantic recommendation system (PSRS) for E-Learning.…”

Section: Recommender Systems In the Domain Of Educationmentioning

confidence: 99%

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

Asabere

2022

Mobile Information Systems

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Globally, the selection of tertiary programmes for higher education in a university by prospective applicants is a daunting task. Different universities offer a wide range of programmes using different education delivery modes for teaching and learning. This creates information overload in the context of tertiary programmes. To tackle the information overload problem of tertiary programmes in the context of higher education institutions (HEIs), this paper, therefore, proposes a novel recommendation model called Collaborative and Social-Personality Aware Recommendation of Programmes (CoSPARP) for tertiary programme selection. CoSPARP utilizes a hybrid filtering system that incorporates the computation of similarities relating to the CF, personality traits, and the tie strength of users (prospective applicants) to generate effective programme recommendations for a tertiary programme applicant (TPA). The proposed CoSPARP recommendation method employs the above recommendation entities to create profiles of the TPAs as a basis of profile similarity for tertiary programme recommendations. Results of benchmarking experiments showed that CoSPARP overcomes cold-start due to the proposed (innovative) hybridization process. Additionally, using a relevant real-world dataset and suitable evaluation metrics such as precision, recall, and F-measure, CoSPARP produces more favourable outcomes in comparison to other state-of-the-art methods.

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“…A user may also feels difficult to give a slightly lower preference to some items, because the rating scale does not normally have a half score. Therefore, there is another approach, called pairwise elicitation, which has been introduced by the researchers in this domain which shows pair choices to the users, such as in [6,7,8,9,10,11,12,13]. By using this approach, a user will be shown with a series of pair options and a preference can be expressed by selecting only one of the most preferred item between the two items (a pair) at a time.…”

Section: Introductionmentioning

confidence: 99%

A Genetic-Based Pairwise trip Planner Recommender System

Qomariyah

Kazakov

2021

Preprint

Self Cite

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The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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Recommender System for e-Learning based on Personal Learning Style

Cited by 23 publications

References 10 publications

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

A Genetic-Based Pairwise trip Planner Recommender System

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