The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

Hasan, Muhammad Asif; Noor, Nurul Fazmidar Mohd; Rahman, Saadah Abdul; Rahman, Mohammad Mustaneer

doi:10.1109/access.2020.3036990

Cited by 38 publications

(30 citation statements)

References 137 publications

(155 reference statements)

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“…1081 As noted above, an intelligent tutoring system is a computer program that tries to mimic a human teacher to provide personalized learning to students. 1082 Recently, ITSs such as ASSISTments, 1083 iTalk2Learn, 1084 and Aida Calculus 1085 , have gained attention. 1086 Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students, and improving students' academic performance.…”

Section: Aied Continues To Developmentioning

confidence: 99%

A Unified Framework of Five Principles for AI in Society

Floridi¹,

Cowls²

2019

Harvard Data Science Review

349

214

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Section: Aied Continues To Developmentioning

confidence: 99%

A Unified Framework of Five Principles for AI in Society

Floridi¹,

Cowls²

2019

Harvard Data Science Review

349

214

View full text Add to dashboard Cite

show abstract

“…Considering a direct correlation between emotions and learning [ 40 ] recently, ITS have also started focusing on emotional state of students while learning to offer a more contextualized learning experience [ 24 ].…”

Section: Latest Researchmentioning

confidence: 99%

Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

2021

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In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

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“…The researchers in Affective Computing, an area of science formed by researchers of Computing and Emotions, have explored social media as another lens to model and evaluate emotional behaviors. In Affective Computing, several state-of-the-art technologies and methodologies for various contexts have been developed to (i) understand and model human behavior, predict emotions and mental disorders, as in [7]- [11]; or (ii) consider emotional aspects in the design of intelligent systems [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

et al. 2021

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The way users interact on social media can indicate their well-being. When depressed, people's feelings tend to be more evident, affecting how users interact and demonstrate their feelings on social media. This paper presents a new approach for the temporal assessment of emotional behavior and interaction among depressed users on social networks. We start by modeling user interactions using complex networks, grouping users through time using the Clauset-Newman-Moore greedy modularity maximization. We evaluate the built networks using metrics such as assortativity, density, clustering, diameter, and shortest path length, closeness, and coverage. Then, we propose EMUS, a method for establishing an emotional user score based on the extraction of emotional features in texts of posts and comments. To extract emotional features, we combine the use of the Empath framework and VADER lexicon. Finally, based on the standard deviation among users, we establish a metric for assessing mood levels. We evaluated users for 33 days, and the results show a sequence of mixed emotional behaviors with high correlations between the number of active users in the network communities, and the form and quality of interactions. The developed approach can be further applied to other database graphs, for different sequential patterns analysis and text-mining contexts.

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The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

Cited by 38 publications

References 137 publications

A Unified Framework of Five Principles for AI in Society

A Unified Framework of Five Principles for AI in Society

Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

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