Affection-enhanced Personalized Question Recommendation in Online Learning DOI Open Access

Mingzi Chen,

Xin Wei, Xuguang Zhang

и другие.

KSII Transactions on Internet and Information Systems, Год журнала: 2023, Номер 17(12)

Опубликована: Дек. 31, 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting question practice.Surrounded by abundant learning resources, some students struggle select proper questions.Personalized recommendation is crucial supporting in choosing questions improve their performance.However, traditional methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well.The CDM-based ignores requirements similarities, resulting inaccuracies recommendation.Even CF examines student it disregards knowledge proficiency struggles when generating appropriate difficulty.To solve these issues, we first design an enhanced process that integrates affection into CDM employing non-compensatory bidimensional item response (NCB-IRM) enhance representation individual personality.Subsequently, propose affection-enhanced personalized (AE-PQR) method learning.It introduces NCB-IRM CF, considering both common characteristics responses maintain rationality accuracy recommendation.Experimental results show our proposed improves diagnosed cognition appropriateness recommended questions.

Язык: Английский

An Automated Recommendation System for Crowdsourcing Data Using Improved Heuristic‐Aided Residual Long Short‐Term Memory DOI Open Access

K. Dhinakaran,

R. Nedunchelian

Computational Intelligence, Год журнала: 2025, Номер 41(1)

Опубликована: Янв. 6, 2025

ABSTRACT In recent years, crowdsourcing has developed into a business production paradigm and distributed problem‐solving platform. However, the conventional machine learning models failed to assist both requesters workers in finding proper jobs that affect better quality outputs. The traditional large‐scale systems typically involve lot of microtasks, it requires more time for crowdworker search work on this Thus, task suggestion methods are useful. Yet, approaches do not consider cold‐start issue. To tackle these issues, paper, new recommendation system data is implemented utilizing deep learning. Initially, from standard online sources, crowdsourced accumulated. novelty model propose an adaptive residual long short‐term memory (ARes‐LSTM) learns task's latent factor via features rather than ID. Here, network's parameters optimized by fitness‐based drawer algorithm (F‐DA) improve efficacy rates. Further, suggested ARes‐LSTM adopted detect user's preference score based historical behaviors. According behavior records users features, provides personalized recommendations rectifies issue cold‐start. From outcomes, accuracy rate 91.42857. Consequently, techniques such as AOA, TSA, BBRO, DA attained 84.07, 85.42, 87.07, 90.07. Finally, simulation conducted with various efficiency metrics show supremacy designed system. proved chooses intended tasks individual preferences can help enlarge number chances engage efforts across broad range platforms.

Язык: Английский

Процитировано

0
Unlocking Cultural Heritage: The Gamified Digitisation Project of SMA-UniGe DOI
Lara La Tessa, Mauro Coccoli, Stefano Schiaparelli

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Gamification as a panacea to workplace cyberloafing: an application of self-determination and social bonding theories DOI

K.S. Nivedhitha,

Gayathri Giri, Palvi Pasricha

и другие.

Internet Research, Год журнала: 2024, Номер unknown

Опубликована: Июль 16, 2024

Purpose Gamification has been constantly demonstrated as an effective mechanism for employee engagement. However, little is known about how gamification reduces cyberloafing and the by which it affects in workplace. This study draws inspiration from self-determination social bonding theories to explain game dynamics, namely, personalised challenges, interactivity progression status, enhance tacit knowledge sharing behaviour, which, turn, cyberloafing. In addition, also examines negative moderating effect of fear failure on positive relationship between dynamics sharing. Design/methodology/approach Using a sample 250 employees information technology organisations, employed 3-wave examine conditional indirect effects. Findings The results ascertain that plays central role Further, positively influenced sharing, turn reduced Especially, status greatly behaviour when was low. Originality/value one initial studies suggest progressive tool reduce workplace behaviours. It utilises problematisation approach analyse criticise in-house assumptions regarding prevention measures. proposes conceptual model explaining link through alternate assumptions.

Язык: Английский

Процитировано

2
Adaptive-Propagating Heterophilous Graph Convolutional Network DOI
Yang Huang,

Yueyang Pi,

Yiqing Shi

и другие.

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

0
Gamifying Cultural Heritage: The Digitization Journey of Genoa University Museum System (SMA-UniGe) DOI Open Access
Lara La Tessa, Stefano Schiaparelli, Mauro Coccoli

и другие.

Опубликована: Апрель 10, 2024

The extensive collection of paper documents and books stored in the archives universities worldwide is a hidden cultural heritage that frequently inaccessible. To overcome this problem, University Genoa, Italy, seeks to collect, store, digitize wide variety items, encompassing books, manuscripts, archival materials, related museum artifacts, which together form great importance historical significance. make such accessible both humans machines, images videos must be provided with alternate descriptions, metadata, speech-to-text transcriptions while ancient texts, for OCR techniques are often not effective, accompanied by word-for-word transcripts. This work presents design transcription system “University Museum System” at Italy (SMA-UniGe), including user interface elements users’ engagement techniques. goal create an digital can enjoyed all, facilitated community volunteers who eager dedicate their time, have experience, socialize, interact on proposed system. exploits gamification theory transform typically monotonous task into captivating experience. activity line so-called third mission, i.e., public aims generating knowledge outside academic environment benefit social, cultural, economic development.

Язык: Английский

Процитировано

0
Affection-enhanced Personalized Question Recommendation in Online Learning DOI Open Access

Mingzi Chen,

Xin Wei, Xuguang Zhang

и другие.

KSII Transactions on Internet and Information Systems, Год журнала: 2023, Номер 17(12)

Опубликована: Дек. 31, 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting question practice.Surrounded by abundant learning resources, some students struggle select proper questions.Personalized recommendation is crucial supporting in choosing questions improve their performance.However, traditional methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well.The CDM-based ignores requirements similarities, resulting inaccuracies recommendation.Even CF examines student it disregards knowledge proficiency struggles when generating appropriate difficulty.To solve these issues, we first design an enhanced process that integrates affection into CDM employing non-compensatory bidimensional item response (NCB-IRM) enhance representation individual personality.Subsequently, propose affection-enhanced personalized (AE-PQR) method learning.It introduces NCB-IRM CF, considering both common characteristics responses maintain rationality accuracy recommendation.Experimental results show our proposed improves diagnosed cognition appropriateness recommended questions.

Язык: Английский

Процитировано

0