Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience

Treceño-Fernández, Daniel; Calabia-del-Campo, Juan; Bote‐Lorenzo, Miguel L.; Gómez‐Sánchez, Eduardo; Luis‐García, Rodrigo de; Alberola-López, Carlos

doi:10.1016/j.cmpb.2020.105634

Cited by 6 publications

(10 citation statements)

References 30 publications

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“…Dutch Education system Adaptive practice program that provides individualized sequences of exercises which can support students to practice the learned content Klaveren et al ( 2017 ) Personalization 18. CTAT A model tracing ITS, which can trace the interaction of students with the system and recommend the content to learn in the next steps Treceño-Fernández et al ( 2020 ) 6 (15%) 19. HINTS Hypergraph Intelligent Tutoring System (HINTS) can provide personalized learning path del Olmo-Muñozet al ( 2022 ) 20.…”

Section: Resultsmentioning

confidence: 99%

“…For example, it is reported that there was a lack of computer labs, computers, electricity outages (Wijekumar et al, 2013 ), high quality video equipment (Roll et al, 2011 ) or learning systems (Mousavi et al, 2021 ). In addition, there can be limited data access (Hickey et al, 2020 ; Butcher & Aleven, 2013 ; Jiang et al, 2020 ), and no sufficient resources for one-on-one tutoring (Ward et al, 2011 ), applying randomized assignment (Treceño-Fernández et al, 2020 ; Hickey et al, 2020 ), and keeping intervention dosage (Nye et al, 2018 ), grading scheme, and the number of benchmarks (Cung et al, 2019 ) consistent during the study. Additionally, in some studies, the participants did not have the necessary skills (e.g., keyboarding, Baker et al, 2020 ) to manage and use ITSs.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

et al. 2023

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Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs work in real and natural educational contexts and the associated challenges of ITS application and evaluation. Through a systematic literature review method, this study analyzed 40 qualified studies that applied social experiment methods to examine the effectiveness of ITS during 2011–2022. The obtained results highlighted a complicated landscape regarding the effectiveness of ITS in real educational contexts. Specifically, there was an “intelligent” regional gap regarding the distribution of countries where ITS studies using social experiment methods were conducted. Compared to learning performance, relatively less attention was paid to investigating the impact of ITS on non-cognitive factors, process-oriented factors, and social outcomes, calling for more research in this regard. Considering the complexities and challenges existing in real educational fields, there was a lack of scientific rigor in terms of experimental design and data analysis in some of the studies. Based on these findings, suggestions for future study and implications were proposed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

et al. 2023

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“…The 2019 experience, however, received better scores than the one in the previous year and with figures approaching those of the classroom experience here described (LTR were above 4 in both cases, in a 1–5 scale, while the one in 2018 did not exceed 3.7). Complete results can be found in [ 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…For interactivity purposes as an educational tool, simulations should be characterized by speed even although image quality could be slighty compromised. This simulator has been complemented with an intelligent tutoring system for additional hands-on guidance [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience

Treceño-Fernández

Calabia-del-Campo

Matute-Teresa

et al. 2021

Sensors

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Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.

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“…Additionally, with the increasing use of Machine Learning models, simulation becomes even more relevant, because it can be used to generate synthetic data for training, 1,2 or to construct signal dictionaries to infer quantitative measurements from the acquired data 3,4 . Moreover, simulations are an excellent tool for education and training, as hands‐on experience is a great way to assimilate the theoretical and practical components of MRI 5‐7 …”

Section: Introductionmentioning

confidence: 99%

KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration

Castillo-Passi

Coronado

Varela-Mattatall

et al. 2023

Magnetic Resonance in Med

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Purpose To develop an open‐source, high‐performance, easy‐to‐use, extensible, cross‐platform, and general MRI simulation framework (Koma). Methods Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq‐compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions. Results Koma was compared to two well‐known open‐source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature. Conclusions Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.

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Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience

Cited by 6 publications

References 30 publications

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience

KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration

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