Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game

Mogessie, Michael; Richey, J. Elizabeth; McLaren, Bruce M.; Andres-Bray, Juan Miguel L.; Baker, Ryan S.

doi:10.1007/978-3-030-52240-7_38

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…Based on the results of studies finding similar patterns for frustration and confusion, other work has treated confusion and frustration as a single combined emotion [16], [17], [18]). This work has used the term confrustion to refer to the merger of these two affective states, and has used human labels of confrustion (confusion and/or frustration) from interaction log data, as well as automated detectors built using these human labels.…”

Section: Confusion and Frustration As One Unitary Emotionmentioning

confidence: 99%

“…Additional analyses revealed that across both groups, learners tended to demonstrate less confrustion later than earlier in the learning session, but this varied across the course of the intervention. A follow-up study by the same group found that confrustion was associated with a lower frequency of gaming the system behavior (a type of behavioral disengagement) [17]. Another follow-up study by the group compared confrustion and learning between two experimental conditions: a digital math learning game and an intelligent tutor [18].…”

Section: Confusion and Frustration As One Unitary Emotionmentioning

confidence: 99%

“…Within this paper, we consider an alternate framing --treating confusion and frustration as part of a confrustion constellation. Some papers have already used a measurement approach that combines confusion and frustration [16], [17], [18]. We discuss the literature around these two constructs, considering evidence of commonalities and differences, and evidence that these two T constructs themselves incorporate a range of sub-constructs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Confrustion Constellation: A New Way of Looking at Confusion and Frustration

Baker,

Cloude,

Andres

et al. 2024

Preprint

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There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of affect, distinct from other prominent affective categories. We discuss how these types of affect have been considered historically and in key models. We then discuss unusual manifestations of each form of affect that have been documented in the literature, and what light they shed on the broader constructs. We conclude with a discussion of a new theoretical framing that treats confusion and frustration as a confrustion constellation, and the opportunities and open questions that this perspective presents.

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Section: Confusion and Frustration As One Unitary Emotionmentioning

confidence: 99%

Section: Confusion and Frustration As One Unitary Emotionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Confrustion Constellation: A New Way of Looking at Confusion and Frustration

Baker,

Cloude,

Andres

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, both traditional games and computer games have been more and more used for education (Becker 2010;Mogessie et al 2020), making use of their natural immersive problem solving tasks. Also called serious games, they address both the cognitive and the affective dimensions of learning (O'Neil, Wainess, and Baker 2005;Virvou, Katsionis, and Manos 2005) and should facilitate learning for students of different cognitive needs and interests.…”

Section: Learning Through Gamingmentioning

confidence: 99%

I AM A.I. Gradient Descent – an Open-Source Digital Game for Inquiry-Based CLIL Learning

Geldhauser

Matt²,

Stussak³

2022

AAAI

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We present an interactive online workshop for K-12 students, which aims in familiarizing students with core concepts of AI. The workshop consists of a variety of resources, inspired by inquiry-based learning techniques, of which we present in detail one module, centered around a browser-based game called Gradient Descent. This module introduces the mathematical concepts behind a gradient descent-based optimization algorithm through the computer game of a treasure hunt at an unknown sea surface landscape. Finally, we report on student feedback for the module in a series of content and language integrated learning in German (CLiLiG) workshops for students aged 14-17 in 30 countries.

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“…Researchers have investigated game-based learning environments for a variety of educational domains, including anti-bullying (Aylett et al 2005), science learning (Ketelhut et al 2007;Rowe et al 2011), interactive health-education (Marsella 2003), graphics design (Cutumisu 2018), mathematics (Mogessie et al 2020), and computational thinking (Min et al 2017;Zhao and Shute 2019). Efforts are now underway to leverage game-based learning to connect AI concepts, such as search, reasoning, and machine learning to high school math (Wang and Johnson 2019).…”

Section: Game-based Learningmentioning

confidence: 99%

AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach

Lee

Mott

Ottenbreit-Leftwich

et al. 2021

AAAI

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Artificial intelligence has emerged as a technology that is profoundly reshaping society and enabling rapid improvements in science, engineering, and mathematics, as well as information technology itself. This has generated increased demand for fostering an AI-literate populace as well as a growing recognition of the importance of promoting K-12 students’ awareness and interest in AI. Although efforts are be-ginning to incorporate AI learning within K-12 education, there is little research exploring how to introduce students to AI and how to support teachers to integrate AI learning experiences in their classrooms. This is especially true at the elementary school level. A particularly promising approach for providing effective and engaging AI learning experiences for elementary students is game-based learning. In this paper, we explore how to introduce AI-infused collaborative inquiry learning into upper elementary school (student ages 8 to 11) using game-based learning. To ground the work in the realities of elementary school classrooms, we present insights from interviews with elementary school teachers to under-stand how best to support them in integrating AI into their classrooms. We then present the design of PrimaryAI, a game-based learning environment that supports rich problem-based learning activities within upper elementary classrooms centered on AI applied toward solving life-science problems. Finally, we discuss some of the challenges we face in bringing AI-infused collaborative inquiry learning to upper elementary students.

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Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game

Cited by 8 publications

References 17 publications

The Confrustion Constellation: A New Way of Looking at Confusion and Frustration

The Confrustion Constellation: A New Way of Looking at Confusion and Frustration

I AM A.I. Gradient Descent – an Open-Source Digital Game for Inquiry-Based CLIL Learning

AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach

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