Improving Learning &amp; Reducing Time

Shen, Shitian; Ausin, Markel Sanz; Mostafavi, Behrooz; Chi, Min

doi:10.1145/3209219.3209232

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…Shen and Chi (2016) propose a pedagogical policy based on reinforcement learning; their evaluation distinguishes slow and fast learners and shows different results for these two groups (the strategy is useful, but only for slow learners). Shen et al (2018) propose a pedagogical policy based on a Markov decision process that uses response time as a reward for reducing students' time on task.…”

Section: Item Sequencingmentioning

confidence: 99%

Leveraging Response Times in Learning Environments: Opportunities and Challenges

Pelánek

2023

Preprint

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Computer-based learning environments can easily collect student response times. These can be used for multiple purposes, such as modeling student knowledge and affect, domain modeling, and cheating detection. However, to fully leverage them, it is essential to understand the properties of response times and associated caveats. In this study, we delve into the properties of response time distributions, including the influence of aberrant student behavior on response times. We then provide an overview of modeling approaches that use response times and discuss potential applications of response times for guiding the adaptive behavior of learning environments.

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Section: Item Sequencingmentioning

confidence: 99%

Leveraging Response Times in Learning Environments: Opportunities and Challenges

Pelánek

2023

Preprint

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“…Intelligent Tutoring Systems (ITS) can be considered as recommendation systems for educational content. The recommendation task has been formulated in the Partially Observable Markov Decision Process (POMDP) framework [11]- [13], as a Multi-Armed Bandit (MAB) problem [5], [14]. We refer to [15] for a review of reinforcement learning based approaches for tutoring.…”

Section: A Intelligent Tutoring Systemsmentioning

confidence: 99%

Prerequisite Structure Discovery in Intelligent Tutoring Systems

Annabi,

Nguyen

2023

2023 IEEE International Conference on Development and Learning (ICDL)

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This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.

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“…The communication methods and amount of information necessary to keep the "human in the loop" may also depend on the preferences of each user. An individual may prefer a higher amount of information and communication in a mostly visual manner, while others may prefer a more simplified interface and voice communication [13].…”

Section: Multimodal Interaction Design For Tor Warning Systemmentioning

confidence: 99%

Driver Model for Take-Over-Request in Autonomous Vehicles

Barisic,

Sigrist,

Oliver

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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This work presents a driver model for Take-Over-Request (TOR) in autonomous vehicles (AVs) that considers the driver's mental state and response time during the transition from automatic to manual driving modes. The Fallback Ready User (FRU) is introduced as a key component that defines the minimum attention required from the driver to respond to TORs and system failures. Highly adaptive FRU models are essential for ensuring the safety of AVs. By using nonintrusive methods, such as facial expression tracking, to capture the driver's mental state and improve AV safety we study shared control between the vehicle and driver during TOR. The paper presents two application scenarios for TOR analysis in ADAVEC system: detecting when the driver is ready for TOR and reacting to unpredictable situations, such as driver sickness or drowsiness. The proposed driver model considers user personalization based on high-level features, long-term changes, and real-time evidence. CCS CONCEPTS• Human-centered computing → User models; User centered design; • Software and its engineering → Empirical software validation; • Computing methodologies → Modeling methodologies; • Applied computing → Transportation.

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Improving Learning & Reducing Time

Cited by 15 publications

References 11 publications

Leveraging Response Times in Learning Environments: Opportunities and Challenges

Leveraging Response Times in Learning Environments: Opportunities and Challenges

Prerequisite Structure Discovery in Intelligent Tutoring Systems

Driver Model for Take-Over-Request in Autonomous Vehicles

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