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Artificial intelligence in multimodal learning analytics: A systematic literature review

computers and Education: Artificial Intelligence 2025年 8卷

作者： Mohammadi, Mehrnoush Tajik, Elham Martinez-Maldonado, Roberto Sadiq, Shazia Tomaszewski, Wojtek Khosravi, Hassan School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Department of Educational Psychology and Learning Systems Florida State University Tallahassee United States Centre for Learning Analytics at Monash Faculty of Information Technology Monash University Melbourne Australia Australian Research Council Centre of Excellence for Children and Families Over the Life Course The University of Queensland Brisbane Australia Institute for Social Science Research The University of Queensland Brisbane Australia Institute for Teaching and Learning Innovation The University of Queensland Brisbane Australia

The proliferation of educational technologies has generated unprecedented volumes of diverse, multimodal learner data, offering rich insights into learning processes and outcomes. However, leveraging this complex, multimodal data requires advanced analytical methods. While Multimodal Learning Analytics (MMLA) offers promise for exploring this data, the potential of Artificial Intelligence (AI) to enhance MMLA remains largely unexplored. This paper bridges these two evolving domains by conducting the first systematic literature review at the intersection of AI and MMLA, analyzing 43 peer-reviewed papers from 11 reputable databases published between 2019 and 2024. The findings indicate a growing trend in AI-enhanced MMLA studies published predominantly in high-quality venues, led by education researchers with a predominant focus on tertiary education targeting diverse stakeholders. Guided by a novel conceptual framework, our analysis highlights the transformative role of AI across the MMLA process, particularly in model learning and feature engineering. However, it also uncovers significant gaps, including limited AI implementation in components requiring deep integration with learning theories, insufficient application of advanced AI techniques, and lack of large-scale studies in authentic learning environments. The review identifies key benefits, such as enhanced personalization and real-time feedback, while also addressing challenges related to ethical considerations, data integration, and scalability. Our study contributes by offering comprehensive recommendations for future research, emphasizing international collaboration, multi-level studies, and ethical AI implementation. These findings advance the theoretical understanding of AI's role in education, providing a foundation for developing sophisticated, interpretable, and scalable AI-enhanced MMLA approaches, potentially revolutionizing personalized learning across diverse educational settings. © 2025

关键词： Educational robots

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Codes with Hierarchical Locality from Covering Maps of Curves

Codes with Hierarchical Locality from Covering Maps of Curve...

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作者： Ballentine, Sean Barg, Alexander Vladut, Serge Department of Mathematics University of Maryland at College Park College ParkMD20742 United States Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland at College Park College ParkMD20742 United States Russian Academy of Sciences Institute for Problems of Information Transmission Moscow13453 Russia

Locally recoverable (LRC) codes provide ways of recovering erased coordinates of the codeword without having to access each of the remaining coordinates. A subfamily of LRC codes with hierarchical locality (H-LRC codes) provides added flexibility to the construction by introducing several tiers of recoverability for correcting different numbers of erasures. We present a general construction of codes with 2-level hierarchical locality from maps between algebraic curves and specialize it to several code families obtained from quotients of curves by a subgroup of the automorphism group, including rational, elliptic, Kummer, and Artin-Schreier curves. We further address the question of H-LRC codes with availability, and suggest a general construction of such codes from fiber products of curves. Detailed calculations of parameters for H-LRC codes with availability are performed for Reed-Solomon- and Hermitian-like code families. Finally, we construct asymptotically good families of H-LRC codes from curves related to the Garcia-Stichtenoth tower. © 1963-2012 IEEE.

关键词： Reed-Solomon codes

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Reconfigurable intelligent surface assisted two–way communications: Performance analysis and optimization

TechRxiv

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TechRxiv 2020年

作者： Atapattu, Saman Fan, Rongfei Dharmawansa, Prathapasinghe Wang, Gongpu Evans, Jamie Tsiftsis, Theodoros A. The Department of Electrical and Electronic Engineering The University of Melbourne VIC Australia The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electronic and Telecommunications Engineering University of Moratuwa Moratuwa Sri Lanka The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Institute of Physical Internet and the School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this paper, we investigate the two-way communication between two users assisted by a re-configurable intelligent surface (RIS). The scheme that two users communicate simultaneously in the same time slot over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ)L rate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR ρ. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solutions are derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter is relaxed to be a semidefinite programming problem, whose optimal solution is achievable and serves as a sub-optimal solution. © 2020, CC BY.

关键词： Signal to noise ratio

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A separation principle for discrete-Time fractional-order dynamical systems and its implications to closed-loop neurotechnology

arXiv

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arXiv 2019年

作者： Chatterjee, Sarthak Romero, Orlando Pequito, Śergio Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Closed-loop neurotechnology requires the capability to predict the state evolution and its regulation under (possibly) partial measurements. There is evidence that neurophysiological dynamics can be modeled by fractional-order dynamical systems. Therefore, we propose to establish a separation principle for discrete-Time fractional-order dynamical systems, which are inherently nonlinear and are able to capture spatiotemporal relations that exhibit non-Markovian properties. The separation principle states that the problems of controller and state estimator design can be done independently of each other while ensuring proper estimation and control in closed-loop setups. Lastly, we illustrate, as proof-of-concept, the application of the separation principle when designing controllers and estimators for these classes of systems in the context of neurophysiological data. In particular, we rely on real data to derive the models used to assess and regulate the evolution of closed-loop neurotechnologies based on electroencephalographic data. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurophysiology

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An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

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arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe Terrien, Ryan Holcomb, Rae Ford, Eric Zawadzki, Brianna Bowler, Brendan P. Bender, Chad Cochran, William Diddams, Scott Endl, Michael Fredrick, Connor Halverson, Samuel Hearty, Fred Hill, Gary J. Lin, Andrea Metcalf, Andrew Monson, Andrew Ramsey, Lawrence Roy, Arpita Schwab, Christian Wright, Jason Zeimann, Gregory Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

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Knowledge discovery in multidimensional knowledge representation framework: An integrative approach for the visualization of text analytics results

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Iran Journal of computer Science 2018年第4期1卷 199-216页

作者： Zenkert, Johannes Klahold, André Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Management and Knowledge Based Systems University of Siegen Siegen Germany

Visualization of results is one of the central challenges in big data analytics and integrative text mining. With a growing amount of unstructured data and different perspectives on big data, knowledge graphs have difficulties to simultaneously represent and visualize all analyzed dimensions of knowledge. This paper proposes integrative text mining as a solution to combine results from different dimensional analysis in a multidimensional knowledge representation (MKR) for knowledge discovery and visualization purpose. Analysis results from named entity recognition, topic detection, sentiment analysis and the extraction of semantic relationships are, therefore, integrated into a common representation structure. In the implementation part of this research, an application is introduced which utilizes MKR based on the results of stated text mining methods applied on a German and English news data set. State-of-the-art visualizations are used in the application and MKR adaptively transforms the visualization type of the knowledge graph according to the selected context for knowledge discovery. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Information visualization Knowledge graphs Knowledge representation Natural language processing Text mining

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An Automatic Comparator Offset Calibration for High-Speed Flash ADCs in FDSOI CMOS Technology

An Automatic Comparator Offset Calibration for High-Speed Fl...

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IEEE Latin American Symposium on Circuits and systems (LASCAS)

作者： Yulang Feng Qingjun Fan Hao Deng Jeffrey Chen Runxi Zhang Phaneendra Bikkina Jinghong Chen Department of Electrical and Computer Engineering University of Houston Houston TX USA St. Mark’s School of Texas Dallas TX USA Institute of Microelectronic Circuits and Systems East China Normal University Shanghai Alphacore Inc Tempe AZ USA

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

This paper presents an automatic comparator offset calibration scheme for designing high-speed flash analog-to-digital data converters (ADCs). It leverages the threshold voltage control capability via back-gate in FDSOI CMOS technology and thus does not require extra transistor pairs or capacitive loads, avoiding comparator speed degradation. An automatic calibration approach employing a successive approximation algorithm (SAA) is also developed. The comparator along with the calibration circuit are designed in a 28-nm FDSOI CMOS process. Simulation results show that the design achieves a resolution of 1.84 mV and a calibration range of ±58 mV with a power consumption of 440 μW under a 1V power supply.

关键词： Calibration Silicon-on-insulator Transistors CMOS technology Threshold voltage Power demand Approximation algorithms

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Quantum Metamaterial for Broadband Detection of Single Microwave Photons

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Physical Review Applied 2021年第3期15卷 034074-034074页

作者： Arne L. Grimsmo Baptiste Royer John Mark Kreikebaum Yufeng Ye Kevin O’Brien Irfan Siddiqi Alexandre Blais Institut quantique and Départment de Physique Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Department of Physics Yale University New Haven Connecticut 06520 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Quantum Nanoelectronics Laboratory Department of Physics University of California Berkeley California 94720 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Canadian Institute for Advanced Research Toronto Canada

Detecting traveling photons is an essential primitive for many quantum-information processing tasks. We introduce a single-photon detector design operating in the microwave domain, based on a weakly nonlinear metamaterial where the nonlinearity is provided by a large number of Josephson junctions. The combination of weak nonlinearity and large spatial extent circumvents well-known obstacles limiting approaches based on a localized Kerr medium. Using numerical many-body simulations we show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. A remarkable feature of the detector is that the metamaterial approach allows for a large detection bandwidth. The detector is nondestructive and the photon population wavepacket is minimally disturbed by the detection. This detector design offers promising possibilities for quantum information processing, quantum optics and metrology in the microwave frequency domain.

关键词： Josephson effect Quantum information architectures & platforms Quantum information with solid state qubits Quantum sensing Metamaterials Optical sources & detectors Superconducting devices Microwave techniques

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Fractional-order model predictive control for neurophysiological cyber-physical systems: A case study using transcranial magnetic stimulation

arXiv

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arXiv 2019年

作者： Romero, Orlando Chatterjee, Sarthak Pequito, Śergio Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Fractional-order dynamical systems are used to describe processes that exhibit temporal long-Term memory and power-law dependence of trajectories. There has been evidence that complex neurophysiological signals like electroencephalogram (EEG) can be modeled by fractional-order systems. In this work, we propose a model-based approach for closed-loop Transcranial Magnetic Stimulation (TMS) to regulate brain activity through EEG data. More precisely, we propose a model predictive control (MPC) approach with an underlying fractional-order system (FOS) predictive model. Furthermore, MPC offers, by design, an additional layer of robustness to compensate for system-model mismatch, which the more traditional strategies lack. To establish the potential of our framework, we focus on epileptic seizure mitigation by computational simulation of our proposed strategy upon seizure-like events. We conclude by empirically analyzing the effectiveness of our method, and compare it with event-Triggered open-loop strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

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Hybrid Generative-Discriminative Generalized Dirichlet-Based Hidden Markov Models with Support Vector Machines

Hybrid Generative-Discriminative Generalized Dirichlet-Based...

引用

IEEE International Symposium on Multimedia

作者： Samr Ali Nizar Bouguila Department of Electrical and Computer Engineering Concordia University Montreal Canada Concordia Institute for Information Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9781728156064

ISBN: (纸本)9781728156071

Recent years have witnessed tremendous advancement in data modelling. In this paper, we focus on Dynamic textures (DT). We propose the use of Fisher kernels with Generalized Dirichlet based hidden Markov models (HMM) for DT recognition. Experiments demonstrate promising results on the DynTeX Alpha dataset using the proposed generative discriminative approach. This is the first application of Generalized Dirichlet HMMs to DT recognition, to the best of our knowledge.

关键词： Hybrid generative-Discriminative Hidden markov models Support vector machines Fisher kernels Generalized dirichlet Dynamic textures Proportional data Hidden Markov models Support Vector Network

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