The overall aim of this article is to explain how the MUSIC Model of Motivation can be applied to L2 instruction in a manner that is consistent with positive psychology, which emphasizes individuals' strengths and the conditions in which they thrive. The article begins by describing the MUSIC model, which is a research-based framework that organizes strategies that instructors can use to motivate students to engage in learning. The MUSIC model can be used by L2 instructors to create learning experiences that consider learners' cognition, affect, needs, and desires in order to foster their motivation and engagement in L2 classes. The article also provides teaching strategies related to the MUSIC model and presents an assessment tool (the MUSIC Model of Academic Motivation Inventory) that can be used by L2 instructors to measure students' MUSIC perceptions of their class. The assessment tool provides feedback to instructors that can be used to improve their instruction by incorporating strategies that allow their students to flourish. Examples of how the MUSIC Inventory can be used to assess L2 instruction are provided. Although researchers have examined the use of the MUSIC model in L2 classes, this research is underdeveloped, and many questions related to its use in L2 classes remain. The article concludes by proposing unanswered questions that could lead to more effective uses of the MUSIC model in L2 classes.
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http://dx.doi.org/10.3389/fpsyg.2020.01204 | DOI Listing |
Ear Hear
January 2025
McMaster Institute for Music and the Mind, McMaster University, Hamilton, Ontario, Canada.
Objectives: Live music creates a sense of connectedness in older adults, which can help alleviate the social isolation frequently associated with hearing loss and aging. However, most hearing-aid (HA) users are dissatisfied with the sound quality of live music and rate sound quality as important to them. Assistive listening systems are frequently independent of a user's HAs and fall short in tailoring to each individual's hearing loss.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Front Neurosci
January 2025
Department of Mathematics, University of Antwerp-Interuniversity Microelectronics Centre (imec), Antwerp, Belgium.
Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.
Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks.
World J Cardiol
January 2025
Department of Medicine, Niramoy Hospital, Panchagarh 5010, Bangladesh.
Background: Listening to music has been shown to reduce pain and anxiety before, during, and after invasive coronary procedures.
Aim: To perform a systematic review and meta-analysis to explore the effect of therapeutic use of music on both, perioperative and postoperative outcomes of invasive coronary procedures.
Methods: An exhaustive literature search of 3 electronic databases (MEDLINE, Scopus, Cochrane CENTRAL) was conducted from inception until 10 December 2023.
Sensors (Basel)
January 2025
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Frequency diversity array-multiple-input multiple-output (FDA-MIMO) radar realizes an angle- and range-dependent system model by adopting a slight frequency offset between adjacent transmitter sensors, thereby enabling potential target localization. This paper presents FDA-MIMO radar-based rapid target localization via the reduction dimension root reconstructed multiple signal classification (RDRR-MUSIC) algorithm. Firstly, we reconstruct the two-dimensional (2D)-MUSIC spatial spectrum function using the reconstructed steering vector, which involves no coupling of direction of arrival (DOA) and range.
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