Towards a Personalized Learning Experience Using Reinforcement Learning

Shawky, Doaa M.; Badawi, Ashraf H.

doi:10.1007/978-3-030-02357-7_8

Cited by 31 publications

(19 citation statements)

References 36 publications

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“…Recommender systems based on RL have the advantage of updating the policies during online interaction, which enables the system to generate recommendations that best suit users evolving preferences (Zhao et al, 2019). Examples include news (Zheng et al, 2018), music recommendations (Hong et al, 2020) and personalized learning systems (Shawky & Badawi, 2019).…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

“…One of the most challenging and critical issues in designing the MDP model is to properly identify the factors that in uence the effectiveness of a motivator, especially when these factors may differ from one child to another. The personalization of intervention can be achieved by carefully determining these features that represent the state space (Shawky & Badawi, 2019). Through careful investigation of the research that investigates motivation stimuli for students with ASD, the features outlined in Table 1 were considered: The ID of the last motivator used that was not successful in motivating the student within an episode, including an option for "none".…”

Section: Statementioning

confidence: 99%

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Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

Siyam

Abdallah

2021

Preprint

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Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to solve the problem of selecting the right motivator for children with ASD using Reinforcement Learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov Decision Process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on Applied Behavior Analysis as well as learners’ individual preferences. We use a Q-Learning algorithm to solve the modelled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

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Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Section: Statementioning

confidence: 99%

Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

Siyam

Abdallah

2021

Preprint

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“…What makes the problem even harder is that not only cognitive load varies from one learner to another, but it also varies for the same learner and for the same material depending on other factors that might affect how they perceive the content delivered. For instance, the affective states of learners and how motivated they are have an influence on how they effectively learn [ 2 , 3 , 4 ]. Thus, a personalized learning system needs to extend and improve cognition, affection, and metacognition of learners based on the collected physiological data [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

The Automatic Detection of Cognition Using EEG and Facial Expressions

Kerdawy

Halaby

Hassan

et al. 2020

Sensors

Self Cite

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Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.

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“…Whereas this process comprises dependability of each other. Due to the concept of distance learning second generation web-based E-Learning systems [2] comes into existence where these systems used artificial intelligence techniques to support new functions beyond content presentation. In recent years e-learning is an asynchronous or synchronous accomplishment.…”

Section: Introductionmentioning

confidence: 99%

Performance Based Adaptive Personalized eLearning System

Shekapure¹,

Patil²

2019

IJRTE

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Step by step eLearning is developing pattern in industry. To the extent, learning technique is concerned it has been seen that conventional learning strategy, for example, instructor and learner as well as chalk and duster swings too inventive learning. Because of innovation in technology each one started learning by utilizing web. If learner is asking for particular learning material sometimes they are not getting relevant result. So there is need to acquire certain data of learner. This data incorporates their learning style, foundation learning, Knowledge level, learning interest, age and so forth. This proposed system tends to use retrieve, reuse, revise and retain phases of CBR. For construction of customized eLearning there has been identification of various list of features. In light of list of features there has been task of assignment of priorities according to need of it. Before retrieval process standardization of features set process is carried out. Job of K-nearest neighbour strategy to recognize impeccable k factor for better examination. Because of dynamically incremental dataset this work identifies which classification algorithm has more suitable for the dataset. Eventually eLearning saves time, enhance learning experience and provides academic success.

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Towards a Personalized Learning Experience Using Reinforcement Learning

Cited by 31 publications

References 36 publications

Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

Towards Automatic Motivator Selection for Autism Behavior Intervention Therapy

The Automatic Detection of Cognition Using EEG and Facial Expressions

Performance Based Adaptive Personalized eLearning System

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