Changes in Cognitive Performance Due to Three Types of Emotional Tension

Choi, Mi-Hyun; Lee, Su-Jeong; Yang, Jae-Woong; Kim, Jihye; Choi, Jinwoo; Park, Jang‐Yeon; Jun, Jae-Hoon; Tack, Gye−Rae; Lim, Dae‐Woon; Chung, Soon−Cheol

doi:10.1007/978-3-642-17622-7_26

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Considering that the activation response is a consequence of a subjective state toward some demand, including emotional effort (Choi et al 2010;Muldner et al 2010), cognitive load (Shi et al 2007), or stress (Fechir et al 2008) and favoring exploitation , our hypothesis concerning this particular finding is that the detailed instruction mode evoked an increase in activation near the completion of the task, increasing the behavioral propensity to implementation, possibly as the result of the perceived stress due to the deadline. That is, the necessity of meeting the detailed requirements given by someone else, within the allotted time, could be the key for generating pressure to complete the task and the perception that students are far from done, increasing the arousal in the final segment, even though it does not surpass the activation level in the beginning of the task.…”

Section: Main Finding: Different Instructions Generate Different Actimentioning

confidence: 96%

“…Activation have been associated with orienting responses Bernstein 1979;Bernstein et al 1975) to either emotional states (Choi et al 2010;Muldner et al 2010), mental stress (Fechir et al 2008), cognitive load (Shi et al 2007) and performance (VaezMousavi et al 2007b). The simple presence of a new stimulus can evoke orienting responses.…”

Section: Arousal Activation Cognition and Creativitymentioning

confidence: 99%

“…For example, in a continuous performance task, the activation level affected reaction time positively, speeding up the response (VaezMousavi et al 2007b). However, increases in levels of arousal generated by looking at tense pictures prior to a task were associated with a decrease in performance (Choi et al 2010). Alexander et al (2007), based on the LC activation mechanism, suggested that this difference is due to the nature of the task: performance in cognitive tasks that require attentional focus is facilitated by increased LC activation; however, tasks that require greater cognitive flexibility is impaired by the same mechanism (Alexander et al 2007).…”

Section: Arousal Activation Cognition and Creativitymentioning

confidence: 99%

“…Following a procedure used by others, after visual inspection to remove artifacts, the SC level (SCL) was exported in a 2 min time window for each subject (Choi et al 2010;Nourbakhsh et al 2012), with exception of the Stroop test and the math baseline tasks that lasted 65 and 90 s, respectively. The unit of measurement is microsiemens (lS).…”

Section: Galvanic Skin Response (Gsr) Measurementmentioning

confidence: 99%

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The Effect of Highly Scaffolded Versus General Instruction on Students’ Exploratory Behavior and Arousal

Blikstein¹,

Gomes

Akiba

et al. 2016

Tech Know Learn

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Technology is changing the way students interact with knowledge, and openended activities are one of the main types of tasks that students engage with in technologyrich environments. However, the amount of guidance needed to promote learning in these environments remains unknown. We explore this issue by focusing on the effects of stepby-step versus generic instructions on student's exploratory behavior and arousal levels. In this experiment, students completed three computer-based activities within a physics simulation software: building a tower, building a bridge and a free task. We did not find any effect of our experimental manipulation on students' task performance. We found, however, that detailed instruction induced higher level of activation followed by a relaxation phase and a recovery of the activation level in the last segment of the task (U-shaped curve). On the other hand, generic instructions seemed to lead students into a continuous relaxation pattern along the task (decreasing slope). Moreover, low and high-aroused students appear to be affected by the instructions differently, with high-aroused students at baseline showing less cognitive flexibility. Finally, we observed carryover effects, where types of instruction kept influencing students' levels of activation in a following openended task. We discuss implications of those results for designing learning activities in constructionist, technology-rich environments.

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Section: Main Finding: Different Instructions Generate Different Actimentioning

confidence: 96%

Section: Arousal Activation Cognition and Creativitymentioning

confidence: 99%

Section: Arousal Activation Cognition and Creativitymentioning

confidence: 99%

Section: Galvanic Skin Response (Gsr) Measurementmentioning

confidence: 99%

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The Effect of Highly Scaffolded Versus General Instruction on Students’ Exploratory Behavior and Arousal

Blikstein¹,

Gomes

Akiba

et al. 2016

Tech Know Learn

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“…An increase in physiological reactivity was observed by Lunn and Harper (2010) to be associated with a frustrating web-based activity. Moreover, Choi et al (2010) demonstrated that tense emotions induced by an external stimulus have a negative effect on performance in a subsequent cognitive task.…”

Section: Physiological Markers Of Affective Statementioning

confidence: 99%

Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

Blikstein¹,

2016

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(2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. http://dx.doi.org/10.18608/jla.2016220-238. http://dx.doi.org/10.18608/jla. .32.11 ISSN 1929 Marcelo Worsley Learning Sciences & Computer ScienceNorthwestern University, USA marcelo.worsley@northwestern.edu ABSTRACT: New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into student learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational applications. Keywords: Learning analytics, multimodal interaction, constructivism, constructionism, assessment INTRODUCTIONThe same battle is fought in every field of educational research and practice: the champions of the direct instruction of well-defined content pitted against those who encourage student-centred exploration of ill-defined domains. These wars have taken place repeatedly over past decades, and partisans on each side have been reborn in multiple incarnations. The first tradition tends to be aligned with behaviourist or neo-behaviourist approaches, while the second favours constructivist-inspired pedagogies. In language arts, the battle has been between phonics and the whole word approach. In math, war is wagged between teaching algorithms versus instruction in how to think mathematically. In history, they clash over the relative merits of critical interpretations and the memorization of historical facts. In science, they clash about inquiry-based approaches versus direct instruction of formulas and principles.

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