“Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities

Müldner, Kasia; Burleson, Winslow; VanLehn, Kurt

doi:10.1007/978-3-642-13470-8_16

Cited by 31 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 98%

“…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%

See 1 more Smart Citation

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

Blikstein¹,

Gomes

Akiba

et al. 2016

Tech Know Learn

View full text Add to dashboard Cite

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.

show abstract

Section: Main Finding: Different Instructions Generate Different Actimentioning

confidence: 98%

Section: Arousal Activation Cognition and Creativitymentioning

confidence: 99%

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

Blikstein¹,

Gomes

Akiba

et al. 2016

Tech Know Learn

View full text Add to dashboard Cite

show abstract

“…Previous research has linked eye-related features to cognitive, mental and memory load [31], [35] as well as emotional aspects, such as valence or negative affect [32], [34]. Brain-related Measurements.…”

Section: Biometric Datamentioning

confidence: 99%

Leveraging Biometric Data to Boost Software Developer Productivity

Fritz

Müller

2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

View full text Add to dashboard Cite

Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developer's output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers. The recent advances in biometric sensor technology offer new opportunities to measure a developer's cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developer's productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developer's work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developer's work and that biometrics can be Abstract-Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developer's output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers.The recent advances in biometric sensor technology offer new opportunities to measure a developer's cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developer's productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developer's work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developer's work and that biometrics can be used to boost the productivity of each individual developer.

show abstract

“…It has been found to be a good indicator of the "Yes!" moments of delight while a student interacts with an ITS [13]. Gaze tracking data has also been used to supplement and change the underlying student model [4,10].…”

Section: Introductionmentioning

confidence: 99%

Do Your Eyes Give It Away? Using Eye Tracking Data to Understand Students’ Attitudes towards Open Student Model Representations

Mathews

Mitrović

Lin

et al. 2012

Intelligent Tutoring Systems

View full text Add to dashboard Cite

Abstract. There is sufficient evidence to show that allowing students to see their own student model is an effective learning and metacognitive strategy. Different tutors have different representations of these open student models, all varying in complexity and detail. EER-Tutor has a number of open student model representations available to the student at any particular time. These include skill meters, kiviat graphs, tag clouds, concept hierarchies, concept lists, and treemaps. Finding out which representation best helps the student at their level of expertise is a difficult task. Do they really understand the representation they are looking at? This paper looks at a novel way of using eye gaze tracking data to see if such data provides us with any clues as to how students use these representations and if they understand them.

show abstract

“Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities

Cited by 31 publications

References 26 publications

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

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

Leveraging Biometric Data to Boost Software Developer Productivity

Do Your Eyes Give It Away? Using Eye Tracking Data to Understand Students’ Attitudes towards Open Student Model Representations

Contact Info

Product

Resources

About