Implementing group teaching in music performance

When technology is used for assessment in music, certain considerations can affect the validity, reliability, and depth of analysis. This chapter explores factors that are present in the three phases of the assessment process: recognition, analysis, and display of assessment of a musical performance. Each phase has inherent challenges embedded within internal and external factors. The goal here is not to provide an exhaustive analysis of any or all aspects of assessment but, rather, to present the rationale for and history of using technology in music assessment and to examine the philosophical and practical considerations. A discussion of possible future directions of product research and development concludes the chapter.

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Typological trends in contemporary popular music performance venues

Arts Marketing An International Journal ◽

10.1108/20442081111180359 ◽

2011 ◽

Vol 1 (2) ◽

pp. 136-144 ◽

Cited By ~ 3

Author(s):

Robert Kronenburg

Keyword(s):

Popular Music ◽

Music Performance

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Performers of the night: Examining the mental health of electronic music artists

Psychology of Music ◽

10.1177/0305735620976985 ◽

2021 ◽

pp. 030573562097698

Author(s):

Jolan Kegelaers ◽

Lewie Jessen ◽

Eline Van Audenaerde ◽

Raôul RD Oudejans

Keyword(s):

Mental Health ◽

Social Support ◽

Sleep Disturbance ◽

Electronic Music ◽

Music Performance ◽

Well Being ◽

Cross Sectional ◽

Music Performance Anxiety ◽

Symptoms Of Depression ◽

Levels Of Functioning

Despite growing popular interest for the mental health of electronic music artists, scientific research addressing this topic has remained largely absent. As such, the aim of the current study was to examine the mental health of electronic music artists, as well as a number of determinants. Using a cross-sectional quantitative design, a total of 163 electronic music artists participated in this study. In line with the two-continua model of mental health, both symptoms of depression/anxiety and well-being were adopted as indicators for mental health. Furthermore, standardized measures were used to assess potential determinants of mental health, including sleep disturbance, music performance anxiety, alcohol abuse, drug abuse, occupational stress, resilience, and social support. Results highlighted that around 30% of participants experienced symptoms of depression/anxiety. Nevertheless, the majority of these participants still demonstrated at least moderate levels of functioning and well-being. Sleep disturbance formed a significant predictor for both symptoms of depression/anxiety and well-being. Furthermore, resilience and social support were significant predictors for well-being. The results provide a first glimpse into the mental health challenges experienced by electronic music artists and support the need for increased research as well as applied initiatives directed at safeguarding their mental health.

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How does music performance anxiety relate to other anxiety disorders?

Psychology of Music ◽

10.1177/0305735620988600 ◽

2021 ◽

pp. 030573562098860

Author(s):

Anna Wiedemann ◽

Daniel Vogel ◽

Catharina Voss ◽

Jana Hoyer

Keyword(s):

Cluster Analysis ◽

Anxiety Disorder ◽

Anxiety Disorders ◽

Graphical Model ◽

Music Performance ◽

Performance Anxiety ◽

Separation Anxiety Disorder ◽

Systematic Analysis ◽

Music Performance Anxiety ◽

Dsm 5

Music performance anxiety (MPA) is considered a social anxiety disorder (SAD). Recent conceptualizations, however, challenge existing MPA definitions, distinguishing MPA from SAD. In this study, we aim to provide a systematic analysis of MPA interdependencies to other anxiety disorders through graphical modeling and cluster analysis. Participants were 82 music students ( Mage = 23.5 years, SD = 3.4 years; 69.5% women) with the majority being vocal (30.5%), string (24.4%), or piano (19.5%) students. MPA was measured using the German version of the Kenny Music Performance Anxiety Inventory (K-MPAI). All participants were tested for anxiety-related symptoms using the disorder-specific anxiety measures of the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM-5), including agoraphobia (AG), generalized anxiety disorder (GAD), panic disorder (PD), separation anxiety disorder (SEP), specific phobia (SP), SAD, and illness anxiety disorder (ILL). We found no evidence of MPA being primarily connected to SAD, finding GAD acted as a full mediator between MPA and any other anxiety type. Our graphical model remained unchanged considering severe cases of MPA only (K-MPAI ⩾ 105). By means of cluster analysis, we identified two participant sub-groups of differing anxiety profiles. Participants with pathological anxiety consistently showed more severe MPA. Our findings suggest that GAD is the strongest predictor for MPA among all major DSM-5 anxiety types.

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