Contextual multi-armed bandit algorithms for personalized learning action selection

Manickam, Indu; Lan, Andrew S.; Baraniuk, Richard G.

doi:10.1109/icassp.2017.7953377

Cited by 20 publications

(8 citation statements)

References 8 publications

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“…Existing studies have focused on modeling learners' learning paths (Chen, Culpepper, et al, 2018;Wang et al, 2018), accelerating learners' memory speed (Reddy et al, 2017), providing model-based sequence recommendation (Chen, Li, et al, 2018;Lan & Baraniuk, 2016;Xu et al, 2016), tracing learners' concept knowledge state transitions over time (Lan et al, 2014), and selecting materials for learners optimally based on model-free algorithms (Li et al, 2018;Tang et al, 2019). However, explicit models are typically needed to characterize learners' learning progresses in these studies.…”

Section: Et Almentioning

confidence: 99%

“…A conventional adaptive learning system is illustrated in Figure 1. Such an adaptive learning system is typical in traditional classrooms and online courses like massive open online courses (MOOCs; Lan & Baraniuk, 2016). In an adaptive learning system, the learner takes some learning materials to improve their latent traits.…”

Section: Problem Statementmentioning

confidence: 99%

“…However, the latent trait estimator will for sure introduce errors due to the nature of estimation methods. Some studies instead constructed the system assuming that learning materials influence learners' responses to test items directly, without the latent trait estimator being incorporated (Lan & Baraniuk, 2016;Lan et al, 2014). Following this direction, a learning process could be modeled and traced directly and model-free algorithms could be proposed to find the optimal learning policy.…”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Reinforcement Learning for Adaptive Learning Systems

Li¹,

Xu²,

Zhang

et al. 2022

Journal of Educational and Behavioral Statistics

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The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner’s latent traits. In this article, we study an important yet less-addressed adaptive learning problem—one that assumes continuous latent traits. Specifically, we formulate the adaptive learning problem as a Markov decision process. We assume latent traits to be continuous with an unknown transition model and apply a model-free deep reinforcement learning algorithm—the deep Q-learning algorithm—that can effectively find the optimal learning policy from data on learners’ learning process without knowing the actual transition model of the learners’ continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner’s learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy. Especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.

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Section: Et Almentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Reinforcement Learning for Adaptive Learning Systems

Li¹,

Xu²,

Zhang

et al. 2022

Journal of Educational and Behavioral Statistics

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show abstract

“…ITS is a field with use of computer for instructions, feedback and students guides (D'mello&Graesser, 2013). ITS is capable of assessing students' mastery of knowledge as noted by Baker et al (2010),modelling of students cognitive states (Cobett et al, 2010), patterning or adapting the learning contents to individual needs as noted by Manickam (2017) and also capable of capturing students affect and motivations (D'mello&Graesser, 2013). Social assistive robots have been applied in the education of people with special needs/vulnerable people.…”

Section: Applications Of Games and Robotics In Educationmentioning

confidence: 99%

Application of Educational Gaming and Robotics in Teaching and Learning Process

Nwigbo¹

2020

IJRITCC

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The development of technology has resulted in the development of many assistive tools employed to facilitate education. Amongst these technologically enhanced tools developed for education are games and robots. This paper examines the application of educational games and robots through review of available literature. The areas of application of games and robots in education have been presented. The paper further highlights some challenges encountered in the deployment of games and robotics in education.

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“…It is important to have an accurate estimation learners' initial states so that the most appropriate optimal learning strategy can be distributed to each individual. Second, different algorithms can be proposed to selects the personalized learning materials that can maximize learners' immediate or future rewards (Manickam, Lan, & Baraniuk, 2017). Lastly, learners' attributes are restricted to a state space satisfying hierarchical learning model assumptions.…”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

Optimal Hierarchical Learning Path Design with Reinforcement Learning

Xiao

Zhang

et al. 2018

Preprint

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E-learning systems are capable of providing more adaptive and efficient learning experiences for students than the traditional classroom setting. A key component of such systems is the learning strategy, the algorithm that designs the learning paths for students based on information such as the students' current progresses, their skills, learning materials, and etc. In this paper, we address the problem of finding the optimal learning strategy for an E-learning system. To this end, we first develop a model for students' hierarchical skills in the E-learning system. Based on the hierarchical skill model and the classical cognitive diagnosis model, we further develop a framework to model various proficiency levels of hierarchical skills. The optimal learning strategy on top of the hierarchical structure is found by applying a model-free reinforcement learning method, which does not require information on students' learning transition process. The effectiveness of the proposed framework is demonstrated via numerical experiments.

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Contextual multi-armed bandit algorithms for personalized learning action selection

Cited by 20 publications

References 8 publications

Deep Reinforcement Learning for Adaptive Learning Systems

Deep Reinforcement Learning for Adaptive Learning Systems

Application of Educational Gaming and Robotics in Teaching and Learning Process

Optimal Hierarchical Learning Path Design with Reinforcement Learning

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