1,623 research outputs found

    Survey of data mining approaches to user modeling for adaptive hypermedia

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    The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

    A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

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    Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

    Personalized face and gesture analysis using hierarchical neural networks

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    The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures

    Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

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    Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

    On the cybernetic arrangement of feedback in serious games: A systems-theoretical perspective

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    This paper explores the cybernetic regulation of complex human learning and teaching. It provides a theoretical description of the arrangement of adaptive, machine- generated learner feedback which relies on cybernetic principles. Cybernetics — today often referred to as control theory, or feedback control theory — involves the incorporation of self-establishing feedback mechanisms for optimal control in complex systems. Although feedback is considered a key element of any learning process, the arrangement of feedback by teachers and educators is under pressure because of the ever-growing complexity of learning environments which is being reinforced by open, online learning technologies and topical models of learning (competence learning, experiential learning, situated cognition, serious gaming). This paper explores how cybernetic principles could be implemented in complex learning environments, e.g. serious games, for the arrange- ment of self-regulating feedback loops for learners. The approach is based on a quanti- tative description of learning activities and learning performances. For the presentation of the feedback, a feedback decision procedure is suggested which is to be linked with pedagogical theories and assessment models. The proposed cybernetic approach is elucidated with a theoretical example. The paper provides a proof of principle and gives suggestions for further development

    On the cybernetic arrangement of feedback in serious games: A systems-theoretical perspective

    Get PDF
    This paper explores the cybernetic regulation of complex human learning and teaching. It provides a theoretical description of the arrangement of adaptive, machine- generated learner feedback which relies on cybernetic principles. Cybernetics — today often referred to as control theory, or feedback control theory — involves the incorporation of self-establishing feedback mechanisms for optimal control in complex systems. Although feedback is considered a key element of any learning process, the arrangement of feedback by teachers and educators is under pressure because of the ever-growing complexity of learning environments which is being reinforced by open, online learning technologies and topical models of learning (competence learning, experiential learning, situated cognition, serious gaming). This paper explores how cybernetic principles could be implemented in complex learning environments, e.g. serious games, for the arrange- ment of self-regulating feedback loops for learners. The approach is based on a quanti- tative description of learning activities and learning performances. For the presentation of the feedback, a feedback decision procedure is suggested which is to be linked with pedagogical theories and assessment models. The proposed cybernetic approach is elucidated with a theoretical example. The paper provides a proof of principle and gives suggestions for further development

    Student Modeling and Analysis in Adaptive Instructional Systems

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    There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability
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