# Analysis of permanence time in emotional states: A case study using educational software

**Authors:** Helena Reis, Danilo Alvares, Patricia Jaques, Seiji Isotani  
**arXiv:** [1803.10810](https://arxiv.org/abs/1803.10810)  
**Date:** 2018-03-28  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1803.10810v1/

## Key points

- The article presents findings of an experiment on emotional states in a computer learning environment
- Prior algebra knowledge and personality can influence permanence time from confusion to frustration/boredom state
- Individuals with neurotic personalities and low levels of algebra knowledge tend to deal with confusion for less time and easily feel frustrated or bored without intervention
- People with extroverted personalities and low levels of algebra knowledge are able to control confusion for longer periods, leading to later interventions
- Results suggest it is possible to detect emotions in a less invasive way without the need for physiological sensors or complex algorithms
- Median times obtained from this study can be incorporated into computational regulation models such as adaptive interfaces to regulate students' emotions during the teaching-learning process
- Research provides valuable insights into how personality traits and prior knowledge impact emotional states during learning, which can inform the development of more effective educational software and personalized learning experiences.

## Summary

The article "Analysis of permanence time in emotional states: A case study using educational software" presents the findings of an experiment that investigated how prior algebra knowledge and personality can influence the permanence time from confusion to frustration/boredom state in a computer learning environment. The study found that individuals with neurotic personalities and low levels of algebra knowledge tend to deal with confusion for less time and easily feel frustrated or bored without intervention. On the other hand, people with extroverted personalities and low levels of algebra knowledge are able to control confusion for longer periods, leading to later interventions. These results suggest that it is possible to detect emotions in a less invasive way without the need for physiological sensors or complex algorithms. Additionally, median times obtained from this study can be incorporated into computational regulation models such as adaptive interfaces to regulate students' emotions during the teaching-learning process. This research provides valuable insights into how personality traits and prior knowledge impact emotional states during learning, which can inform the development of more effective educational software and personalized learning experiences.