Stress and Resources in Vocational Problem Solving

Kärner, Tobias; Minkley, Nina; Rausch, Andreas; Schley, Thomas; Sembill, Detlef

doi:10.1007/s12186-017-9193-8

Cited by 8 publications

(6 citation statements)

References 87 publications

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“…Several studies which already investigated the association between physiological (primarily cortisol secretion) and psychological (perceived stress or anxiety) stress responses reported heterogeneous results (for a review see Campbell and Ehlert, 2012). In most studies, there was no systematic relationship between these parameters (Buchanan et al, 1999;Weekes et al, 2006;Campbell and Ehlert, 2012;Minkley et al, 2014;Kärner et al, 2018;Ringeisen et al, 2019). Only a few studies found low or moderate associations between increased cortisol concentration and perceived stress (e.g., Spangler et al, 2002;Lindahl et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Analyzing Relationships Between Causal and Assessment Factors of Cognitive Load: Associations Between Objective and Subjective Measures of Cognitive Load, Stress, Interest, and Self-Concept

Minkley¹,

Xu²,

Krell³

2021

Front. Educ.

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The present study is based on a theoretical framework of cognitive load that distinguishes causal factors (learner characteristics affecting cognitive load e.g., self-concept; interest; perceived stress) and assessment factors (indicators of cognitive load e.g., mental load; mental effort; task performance) of cognitive load. Various assessment approaches have been used in empirical research to measure cognitive load during task performance. The most common methods are subjective self-reported questionnaires; only occasionally objective physiological measures such as heart rates are used. However, the convergence of subjective and objective approaches has not been extensively investigated yet, leaving unclear the meaning of each kind of measure and its validity. This study adds to this body of research by analyzing the relationship between these causal and assessment (subjective and objective) factors of cognitive load. The data come from three comparable studies in which high school students (N = 309) participated in a one-day out of school molecular biology project and completed different tasks about molecular biology structures and procedures. Heart rate variability (objective cognitive load) was measured via a chest belt. Subjective cognitive load (i.e., mental load and mental effort) and causal factors including self-concept, interest, and perceived stress were self-reported by participants on questionnaires. The findings show that a) objective heart rate measures of cognitive load are related to subjective measures of self-reported mental effort but not of mental load; b) self-reported mental effort and mental load are better predictors of task performance than objective heart rate measures of cognitive load; c) self-concept, interest and perceived stress are associated with self-reported measures of mental load and mental effort, and self-concept is associated with one of the objective heart rate measures. The findings are discussed based on the theoretical framework of cognitive load and implications for the validity of each measure are proposed.

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Section: Discussionmentioning

confidence: 99%

Analyzing Relationships Between Causal and Assessment Factors of Cognitive Load: Associations Between Objective and Subjective Measures of Cognitive Load, Stress, Interest, and Self-Concept

Minkley¹,

Xu²,

Krell³

2021

Front. Educ.

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“…However, few studies use shimmer devices to detect electrodermal activity or practice concurrent thinking aloud. Studies on psychophysiological measures have shown to be valid indicators for problem-solving performance-related measures such as stress (Kärner et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Analysing domain-specific problem-solving processes within authentic computer-based learning and training environments by using eye-tracking: a scoping review

Mayer

Rausch

Seifried

2023

Empirical Res Voc Ed Train

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Recently, many studies have been published on the use of eye-tracking to analyse complex problem-solving processes within authentic computer-based learning and training environments. This scoping review aims to provide a systematic report of the current state-of-the-art for related papers. Specifically, this work offers a scoping review of studies that analyse problem-solving processes by using eye-tracking (alongside additional process data such as log files, think aloud, facial expression recognition algorithms, or psychophysiological measures) within authentic technology-based learning and training environments for professional and vocational education and training (VET). A total of 12 studies were identified. The most commonly calculated measures in eye-tracking research are position measures, and these are almost exclusively position duration measures such as the proportion of fixation times or total dwell times. Count measures are also mostly related to the number or proportion of fixations and dwells. Movement measures are rarely computed and usually refer to saccade directions or a scan path. Also, latency and distance measures are almost never calculated. Eye-tracking data is most often analysed for group comparisons between experts vs. novices or high vs. low-performing groups by using common statistical methods such as t-test, (M)ANOVA, or non-parametric Mann–Whitney-U. Visual attention patterns in problem-solving are examined with heat map analyses, lag sequential analyses, and clustering. Recently, linear mixed-effects models have been applied to account for between and within-subjects differences. Also, post-hoc performance predictions are being developed for future integration into multimodal learning analytics. In most cases, self-reporting is used as an additional measurement for data triangulation. In addition to eye-tracking, log files and facial expression recognition algorithms are also used. Few studies use shimmer devices to detect electrodermal activity or practice concurrent thinking aloud. Overall, Haider and Frensch’s (1996, 1999) “information reduction hypothesis” is supported by many studies in the sample. High performers showed a higher visual accuracy, and visual attention was more focused on relevant areas, as seen by fewer fixation counts and higher fixation duration. Low performers showed significantly fewer fixation durations or substantially longer fixation durations and less selective visual attention. Performance is related to prior knowledge and differences in cognitive load. Eye-tracking, (in combination with other data sources) may be a valid method for further research on problem-solving processes in computer-based simulations, may help identify different patterns of problem-solving processes between performance groups, and may hold additional potential for individual learning support.

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“…Moreover, physiological measures such as heart rate (HR), heart rate variability (HRV), skin conductance or cortisol may be used to further validate the EES data. One such study was conducted by Kärner et al (2018) who also used the above office simulation and found that CSSM data and physiological data (HR, HRV, and cortisol) showed very similar trends in the course of problem solving. A further data source for validation is the log files from the office simulation.…”

Section: Discussionmentioning

confidence: 99%

Validation of Embedded Experience Sampling (EES) for Measuring Non-cognitive Facets of Problem-Solving Competence in Scenario-Based Assessments

2019

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To measure non-cognitive facets of competence, we developed and tested a new method that we refer to as Embedded Experience Sampling (EES). Domain-specific problem-solving competence is a multi-faceted construct that is not limited to cognitive facets such as domain knowledge or problem-solving strategies but also comprises non-cognitive facets in the sense of domain-specific emotional and motivational dispositions such as, for instance, interest and self-concept. However, in empirical studies non-cognitive facets are usually either neglected or measured by generalized self-report questionnaires that are detached from the performance assessment. To enable an integrated measurement, we developed the EES method to collect data on non-cognitive facets during scenario-based low-stakes assessments. Test-takers are requested to stop at certain times and spontaneously answer short items (EES items) regarding their actual experience of the problem situation. These EES items are embedded in an EES event that resembles typical social interactions with non-player characters. To evaluate the feasibility and validity of the method, we implemented EES in a series of three studies in the context of commercial vocational education and training (VET): A feasibility study with 77 trainees, a pilot study with 20 trainees, and the main study with 780 trainees who worked on three complex problem scenarios in a computer-based office simulation. In the present paper, we investigate how test-takers perceived the EES events, and whether social desirability biased their answers, and investigate the internal structure of the data and the relationship between EES data and data from several other sources. Interview data and survey data indicated no biases due to social desirability and no additional burden for the test-takers due to the EES events. A correlation analysis following the multitrait-multimethod approach as well as the calibration of a multidimensional model based on Item Response Theory (IRT) also supported the construct validity. Furthermore, EES data shows substantial correlations with test motivation but almost zero correlations with data from generalized retrospective self-report questionnaires on non-cognitive facets. Altogether, EES offers an alternative approach to measuring non-cognitive facets of competence under certain conditions. For instance, EES is also based on self-reporting and thus might not be suitable for high-stakes testing.

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Stress and Resources in Vocational Problem Solving

Cited by 8 publications

References 87 publications

Analyzing Relationships Between Causal and Assessment Factors of Cognitive Load: Associations Between Objective and Subjective Measures of Cognitive Load, Stress, Interest, and Self-Concept

Analyzing Relationships Between Causal and Assessment Factors of Cognitive Load: Associations Between Objective and Subjective Measures of Cognitive Load, Stress, Interest, and Self-Concept

Analysing domain-specific problem-solving processes within authentic computer-based learning and training environments by using eye-tracking: a scoping review

Validation of Embedded Experience Sampling (EES) for Measuring Non-cognitive Facets of Problem-Solving Competence in Scenario-Based Assessments

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