Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments

@article{Baker2010BetterTB,
  title={Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments},
  author={R. Baker and Sidney K. D’Mello and Ma. Mercedes T. Rodrigo and Arthur C. Graesser},
  journal={Int. J. Hum. Comput. Stud.},
  year={2010},
  volume={68},
  pages={223-241}
}
View via Publisher
learnlab.org
746 Citations
Highly Influential Citations
42
Background Citations
315
Methods Citations
51
Results Citations
35

Figures and Tables from this paper

746 Citations

Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies

The presence of confusion and joy during learning had a positive impact on student confidence in their performance while the presence of frustration and transition from confusion to frustration had a negative impact on confidence, even after accounting for individual differences in multiple-choice confidence.

Which Is More Responsible for Boredom in Intelligent Tutoring Systems: Students (Trait) or Problems (State)?

An unobtrusive computational detector of student boredom is applied to log data from an intelligent tutoring system to determine whether state or trait factors better predict the prevalence of boredom in students using that system.

Comparing the Incidence and Persistence of Learners’ Affect During Interactions with Different Educational Software Packages

Engaged concentration was the most common affective state, by a large margin, in all studies run in private schools in the Philippines; confusion was slightly more common than engaged concentration in the two public school studies.

Does the Frequency of Pedagogical Agent Intervention Relate to Learners' Self-Reported Boredom while using Multiagent Intelligent Tutoring Systems?

Pedagogical agents have the ability to scaffold and regulate students' learning about complex topics while using intelligent tutoring systems (ITSs), but results indicated that Sam negatively impacted students' experiences of enjoyment, in relation to the other agents of MetaTutor.

Emotions During the Learning of Difficult Material

ASSESSMENT OF STUDENTS’ COGNITIVE–AFFECTIVE STATES IN LEARNING WITHIN A COMPUTER-BASED ENVIRONMENT: EFFECTS ON PERFORMANCE

Students’ cognitive-affective states are human-elements that are crucial in the design of computer-based learning (CBL) systems. This paper presents an investigation of students’ cognitiveaffective…

Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes.

In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the…

The Incidence and Persistence of Affective States While Playing Newton ’ s Playground

We study the incidence (rate of occurrence), and persistence (rate of reoccurrence immediately after occurrence) of students’ affective states while playing Newton’s Playground (NP), an educational…

Sequences of Frustration and Confusion, and Learning

The analyses suggest that the effect may be stronger for frustration than confusion, but is strongest when these two affective states are taken together, including the role of confusion and frustration in online learning.

Dynamics of Student Cognitive-Affective Transitions During a Mathematics Game

Researchers of interactive learning environments have grown increasingly interested in designing these systems to become more responsive to differences in students’ cognitive-affective states. They…
...

References

SHOWING 1-10 OF 164 REFERENCES

This computer responds to user frustration: Theory, design, and results

Exploring Relationships Between Affect and Learning with AutoTutor

The relationship between emotions and learning was investigated by tracking the emotions that college students experienced while learning about computer literacy with AutoTutor. AutoTutor is an…

Off-task behavior in the cognitive tutor classroom: when students "game the system"

Analysis of students who choose to game the system suggests that learned helplessness or performance orientation might be better accounts for why students choose this behavior than lack of interest in the material.

Affect and learning: An exploratory look into the role of affect in learning with AutoTutor

The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow,…

Predicting Affective States expressed through an Emote-Aloud Procedure from AutoTutor's Mixed-Initiative Dialogue

Correlation and regression analyses confirmed the hypothesis that dialogue features could significantly predict the affective states of confusion, eureka, and frustration, and discussed the prospects of extending AutoTutor into an affect-sensing intelligent tutoring system.

Affective learning companions: strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance

This thesis has developed an affective agent research platform that advances the architecture of relational agents and intelligent tutoring systems. The system realizes non-invasive multimodal…

Cognitive Tutors: Lessons Learned

The 10-year history of tutor development based on the advanced computer tutoring (ACT) theory is reviewed, finding that a new system for developing and deploying tutors is being built to achieve the National Council of Teachers of Mathematics (NCTM) standards for high-school mathematics in an urban setting.

Affect and Usage Choices in Simulation Problem-Solving Environments

Boredom and confusion appear both to precede gaming behavior and to co-occur withGaming behavior; delight and flow are negatively associated with gaming behavior.

Modeling and understanding students' off-task behavior in intelligent tutoring systems

We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a…

The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?

It is shown that gaming tends to have immediate harmful impact while off-task tends to has aggregated harmful impact on learning.
...