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About 10,000 papers for “Adaptive learning”
Philip Henry Kerr · ELT Journal · 2015
Journal Article Adaptive learning Get access Philip Kerr Philip Kerr Search for other works by this author on: Oxford Academic Google Scholar ELT Journal, Volume 70, Issue 1, January 2016, Pages 88–93, https://doi.org/10.1093/elt/ccv055 Published: 07 October 2015
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2024
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Grundtman, Per · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.
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International Encyclopedia of Organization Studies · 2008
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Nishikant Sonwalkar · Distance Learning · 2007
The genesis of adaptive learning systems is from the artificial intelligence (AI) research. In the early 1980s there was significant development of systems to provide intelligent response to user interacting with the computers. The early AI research developed into three overlapping streams, namely, knowledge-based expert systems, neural networks, and genetic algorithms. These technologies were used primarily in adaptive control systems that managed the difficult task of controlling electromechanical actuators to adapt to the given situation and respond accordingly.The artificial intelligence systems were based on strategies to learn users’ behavior and respond accordingly. The conceptual and philosophical differences of theses approaches led to the learning systems that were either influenced by the connectionists model that created supervised neural nets or unsupervised self-organizing maps or reduction of the knowledge domain into set-of symbolic representations leading to knowledge-based expert rules that can be fired to resolve a decision for the given situation.Unfortunately, the learning management systems (LMS), learning content management systems (LCMS) or even the course management systems (CMS) completely have been completely void of any tool that allowed intelligent tutoring system to become part of the learning system to help individual learners to learn.The learning systems developed by the author combine the elements of pedagogical learning framework with the intelligent systems to develop adaptive learning systems.Adaptive learning systems can be defined as the intelligent systems that are dynamically organized based on the observation of the learning preferences of an individual for the best learning performance.The definition above illustrates following important characteristics of adaptive learning systems:The three-dimensional learning cube provides a logical framework to identify individual learning preferences based on the learning styles that define distinctive learning pathway. Three dimensions of the learning cube represent media, models and interactivity.The proposed learning cube depicted in Figure 1 is composed of three dimensions—learning media, learning models/strategies and interactivities. The media elements are the modes of collecting information through text, graphics, audio, video, animations and simulations based on visual, auditory and kinesthetic preferences, the learning models refers to the process preferred by a learner to understand the information and turn it into useful knowledge, such as apprenticeship, incidental, inductive, deductive, and discovery, and the interactivity is used to provide feedback for confirmation, reinforcement and discussions. The learning cube is useful to map the individual learning preferences based on media, learning models and interaction.For adaptive learning we define the five functional leaning styles/strategies as:These models represent selected learning processes chosen from the numerous learning theories. These models and strategies are organized from simplest linear learning model to complex simulated environments.The learning performance correlation matrix is based on the statistical inference engine that collects information about the user behavior from each individual learning trajectory and creates a probability distribution for the entire set of learning content. These probability distributions are then updated based on the performance of individual user in a given diagnostic assessment.The diagnostic assessments are as simple as a multiple choice questionnaire on the given concept or a complex exercise to demonstrate the level of mastery of an individual for a given concept.The diagnostic performance correlation leads to the identification of preferred learning model and concept deficiency. Based on the concepts that need remediation and the preferred learning style the content is sequenced dynamically to match the individual learning preference.A remedial short presentation is dynamically generated following every diagnostic test to provide continuous intelligent feedback. The revision of the concept with the specific feedback leads to enhancement of the performance on each concept. This process of diagnostics and remediation leads to adaptive learning cycles that ensure that every individual learner reaches the necessary competency level.The adaptive learning system as described earlier consists of a four-step learning process:The future of e-learning is not in providing static content that just provide information, but lies in the power of customizing the content to match the learning needs of each individual learner. The learning process that is based on strong dynamic presentation and continuous adaptive feedback can overcome the deficiencies prevalent in the current on-line learning. Adaptive learning systems will provide the expected results that are long-awaited promise of educational revolution. It is time to build the next generation of adaptive learning systems.
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Warren B. Powell, Ilya O. Ryzhov · Wiley series in probability and statistics · 2012
This chapter contains sections titled: The Frequentist View The Bayesian View Updating for Non-Gaussian Priors Monte Carlo Simulation Why does it Work? Bibliographic Notes Problems
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Karun Thankachan · 2017 International Conference on Inventive Computing and Informatics (ICICI) · 2017
This paper discusses the design for an intelligent and adaptive tutoring system offering pedagogical support that deviates from the traditional chalk and talk form of teaching for online courses. The design proposed utilizes the information in the MOOC database with user-system interaction logs to create a model which enables the system to adapt according to user needs. There also exists room for manual intervention by the course administrators in case of at-risk participants. Educational data mining and learning analytics techniques have been applied to pinpoint the best instructional methods and pedagogical support for each student over time. The design proposed allows the system to personalize the learning experience and supports the instructor in administering the course to a huge number of participants.
Cited by 3
Philip Kerr · Cambridge University Press eBooks · 2022
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
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Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning and Data Mining · 2017
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Carol Thompson, Lydia Spenceley, Mark Tinney et al. · 2024
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2012
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Nader Rifai, Ann Gronowski, Christina Ellervik et al. · Clinical Chemistry · 2015
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Burov, O. Yu. · Digital Library NAES of Ukraine (National Academy of Educational Sciences of Ukraine) · 2019
Individualized Adaptive LearningProblem setting.The global trends of education reformation demonstrate needs in individualization of learning, as well as in mutual adaptation of a human and a System [1], including learner and learning tools [2].Analysis of recent research and publications.The solution of the problem is based on the features of the balance between individual and general learning [3], the need to take into account the emerging problems of student safety [4], their cognitive workload [5], as well as changes of intellect and personality structure in the micro-age intervals [6].The goal of the work.To describe main features of adaptive learning based on a learner psychophysiological dynamic changes. Results.Adaptive learning is an educational method which uses ITC as interactive teaching devices, and to orchestrate the allocation of human and mediated resources according to the unique needs of each learner.Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences.
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M. E. Müller · IGI Global eBooks · 2011
This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described. The most important learning methods are introduced by examples, and their applicability in adaptive hypermedia is discussed. The chapter concludes with a comparison of all approaches one might consider when applying machine learning in adaptive hypermedia systems.Request access from your librarian to read this chapter's full text.
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Tetiana Suvorova, Muhammet Demirbilek · Advances in computational intelligence and robotics book series · 2025
This article explores artificial intelligence's role in developing metacognitive skills in educational settings. Through analysis of adaptive learning systems and AI technologies, the research demonstrates advances in personalized learning support and real-time metacognitive interventions. The study examines AI-enhanced systems like MetaTutor and Betty's Brain, which support self-regulated learning through sophisticated monitoring and feedback mechanisms. Findings show AI-supported environments can achieve 47% improvement in self-regulated learning strategies and 42% increase in strategy adjustment. Implementation challenges include accuracy in detecting metacognitive states, data privacy concerns, and balancing AI support with learner autonomy. Future developments in natural language processing, emotion recognition, and multimodal learning analytics promise enhanced support capabilities. Success requires integrating technological innovation with established pedagogical practices while maintaining ethical considerations and human oversight.
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Benedetto, Vertecchi, Bruno, Losito, Mauro La Torre, et al. · IRIS UNIMORE (University of Modena and Reggio Emilia) · 2009
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Robert Gütig (262117), Haim Sompolinsky (39889) · Figshare · 2015
<p>Change in synaptic peak conductance Δ<i>g</i> versus the time difference Δ<i>t</i> between synaptic firing and the voltage maximum, as a function of the mean total synaptic conductance <i>G</i> during this interval (color bar). Data were collected during the initial 100 cycles of learning with <i>β</i><sub>max</sub> = 2.5 and averaged over 100 realizations.</p>
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Miloš Kravčík, Marcus Specht, Ambjörn Naeve · DSpace (Open University in the Netherlands) · 2009
Kravcik, M., Specht, M., & Naeve, A. (2008). Personalized Adaptive Learning. Presentation of PROLEARN WP1 Personalized Adaptive Learning at the final review meeting. February, 27, 2008, Hannover, Germany.
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Fuliang Weng, David Farwell · 1994
This paper first gives a brief survey on current efforts in language learning 1 . Next, it presents a description of our learning system, an Adaptive Language Learner (AL Learner), for Context-Free Grammars (CFGs). This language learner is based on adaptation process, the EGLR parser serves as assimilation process and AL Learner serves as accomodation process. Then, an analysis on some complexity problems related to our learning methods is given. Finally, we conclude this paper by a comparison of our method with others. 1 Introduction Language learning is fascinating topic and there has been much effort lately, devoted to corpora-based techniques. In section 2, we first give a brief survey on current efforts in language learning. Then, we present our learning algorithm, Adaptive Language Learner (AL Learner), for Context-Free Grammars (CFGs). An analysis on some complexity issues related to our learning method is given in section 4. Finally, we conclude this paper by a comparison of...
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Martin Müller · IGI Global eBooks · 2011
This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described. The most important learning methods are introduced by examples, and their applicability in adaptive hypermedia is discussed. The chapter concludes with a comparison of all approaches one might consider when applying machine learning in adaptive hypermedia systems.
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