Stimmungskurven im Arbeitsalltag - eine Feldstudie

Triemer, Antje; Rau, Renate

doi:10.1024//0170-1789.22.1.42

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…They range between the end items bored -interested [gelangweilt -interessiert] (interest), without energy -full of energy [energielos -voller Energie] (energy), unpleasantpleasant [unangenehm -angenehm] (valence), focused -not focused [unkonzentriert -konzentriert] (concentration), and relaxed -tense [entspannt -angespannt] (tension). Similar or identical items have been used in the past to assess affective states in other longitudinal designs (Triemer and Rau, 2001;Wilhelm and Schoebi, 2007). Items were chosen to ensure a short response time (resulting in 16 s response time in average).…”

Section: Process Measures (Experience Sampling)mentioning

confidence: 99%

Sensor Measures of Affective Leaning

2020

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The aim of this study was to predict self-report data for self-regulated learning with sensor data. In a longitudinal study multichannel data were collected: self-report data with questionnaires and embedded experience samples as well as sensor data like electrodermal activity (EDA) and electroencephalography (EEG). 100 students from a private university in Germany performed a learning experiment followed by final measures of intrinsic motivation, self-efficacy and gained knowledge. During the learning experiment psychophysiological data like EEG were combined with embedded experience sampling measuring motivational states like affect and interest every 270 s. Results of machine learning models show that consumer grade wearables for EEG and EDA failed to predict embedded experience sampling. EDA failed to predict outcome measures as well. This gap can be explained by some major technical difficulties, especially by lower quality of the electrodes. Nevertheless, an average activation of all EEG bands at T7 (left-hemispheric, lateral) can predict lower intrinsic motivation as outcome measure. This is in line with the personality system interactions (PSI) theory of Julius Kuhl. With more advanced sensor measures it might be possible to track affective learning in an unobtrusive way and support micro-adaptation in a digital learning environment.

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Section: Process Measures (Experience Sampling)mentioning

confidence: 99%

Sensor Measures of Affective Leaning

2020

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“…Obwohl Flow-Erleben besonders häufig von passioniert betriebenen Freizeitaktivitäten berichtet wird, ist dieser Zustand auch aus der Arbeit bekannt (Csikszentmihalyi & LeFevre, 1989;Pfister, 2002;Schallberger & Pfister, 2001). (Triemer & Rau, 2001;Triemer, 2001).…”

Section: Die Qualitative Flow-forschungunclassified

Intrinsische Motivation und Flow-Erleben

Rheinberg

Engeser

2018

Springer-Lehrbuch

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Intrinsische Motivation und Flow-Erleben

Rheinberg

Springer-Lehrbuch

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Falko RheinbergIntrinsische Motivation und Flow-Erleben U n i v e r s i t ä t P o t s d a mHumanwissenschaftliche Fakultät Intrinsisch als "in der Tätigkeit"Wenn wir uns an die Ausführungen zu den Tätigkeitsanreizen im Einführungsabschnitt erinnern, so macht es durchaus Sinn, hier den Begriff "intrinsisch" zu verwenden. Wir hatten es dort ja mit Anreizen zu tun, die die Tätigkeit selbst betreffen. "Innen", also intrinsisch, wären demnach die Anreize, die im Vollzug der Tätigkeit liegen, "außen", also extrinsisch, Um historisch genau zu sein, muss man allerdings -wie so oft -bis Artistoteles zurückgehen.Schneider (1996)

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Stimmungskurven im Arbeitsalltag - eine Feldstudie

Cited by 9 publications

References 17 publications

Sensor Measures of Affective Leaning

Sensor Measures of Affective Leaning

Intrinsische Motivation und Flow-Erleben

Intrinsische Motivation und Flow-Erleben

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