“I guess my question is”: What is the co-occurrence of uncertainty and learning in computer-mediated discourse?

Jordan, Michelle E.; Cheng, An-Chih; Schallert, Diane L.; Song, Kwangok; Lee, SoonAh; Park, Yangjoo

doi:10.1007/s11412-014-9203-x

Cited by 20 publications

(6 citation statements)

References 58 publications

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“…Third, the association between students' perceptions of learning and learning efficiency indicates that social constructivism can be an efficient practice in IS education when students have a positive perception of CSCL. This association supports Jordan et al's (2014) remark that positive attitude is favorable for learning because of its influence on learning efficiency, motives, and knowledge application.…”

Section: Discussionsupporting

confidence: 84%

“…This finding contributes to existing research that shows a close relationship between learning and knowledge building (e.g., Baker, 1999;Jordan et al, 2014;Paavola & Hakkarainen, 2005;Wise et al, 2014) but also extends it to suggest that, if students have limited time to devote to online discussion, they would be better focusing on depth than breadth. However, as for RQ1d, we found that the difference in knowledge gain scores between the two groups was marginally significant and represented a medium effect size.…”

Section: Discussionsupporting

confidence: 82%

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How Design Science Research Helps Improve Learning Efficiency in Online Conversations

Eryilmaz¹,

Thoms²,

Canelón³

2018

CAIS

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Abstract:In this design science research paper, we report on our constructing and evaluating an attention-guidance system that we integrated into a computer-supported collaborative learning system. Drawing on social constructivist literature, our proposed design focuses on attracting, retaining, and, if necessary, reacquiring users' attention on task-relevant information in online collaborative literature processing. The investigation involved an experiment across two sections of students in a human-computer interaction course. Results show that the new design allowed users to consistently reflect and evaluate the content of a text as they capitalized on one another's reasoning to resolve misconceptions. Moreover, we found that the new system increased users' perceptions of learning. However, the difference in knowledge gain scores was marginally significant and represented a medium effect size. Interestingly, we found that the attention-guidance system supported more efficient learning. Finally, we discovered that task-oriented reading of text, revisions of incomplete or incorrect ideas, and perceptions of learning mediated the relationship between software system and learning efficiency. We discuss the theoretical and practical implications.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionsupporting

confidence: 82%

How Design Science Research Helps Improve Learning Efficiency in Online Conversations

Eryilmaz¹,

Thoms²,

Canelón³

2018

CAIS

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“…These researchers found that positive peer support may reduce uncertainty, and thus lead to idea generation, whereas negative peer support may cause statements of uncertainty to be withdrawn or ignored. In a study on online discussions, Jordan et al () suggested that social negotiation through collaboration with peers allowed students to accomplish two goals in sequence: to “increase their uncertainty in ways that prompted learning” (p. 469) and then to navigate and decrease this uncertainty through reinterpreting past experiences and exploring novel ideas.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Managing uncertainty in scientific argumentation

2019

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Argumentation is a core practice of science that inherently contains uncertainty. Relatively few studies have examined the role of uncertainty within argumentation and how teachers manage uncertainty leading to conceptual development. This design-based, multiple-case study employed the constant K E Y W O R D S argumentation, dialogue, epistemic understanding, managing uncertainty, social negotiation

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“…Furthermore, Jordan et al exclude words, e.g., modal verbs, if they have other common uses besides expressing confusion. Therefore, with the exception of the Questions category, we augment the lists of features in each category based on additional linguistic sources [16], [18], [19], [20], [21], [22]. The complete list of features of each category is publicly available.…”

Section: A Theoretical Frameworkmentioning

confidence: 99%

“…Identifying confusion is not an easy task, and its has been being operationalized using keywords. We used an existing confusion framework [16] and also considered features from different sources [16], [18], [19], [20], [21], [22]. Considering the internal validity we note that none of the raters has been involved in Android development, so they might have misinterpreted certain comments as confusion or no confusion.…”

Section: Threats To Validitymentioning

confidence: 99%

Confusion Detection in Code Reviews

Ebert

Castor

Novielli

et al. 2017

2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Code reviews are an important mechanism for assuring quality of source code changes. Reviewers can either add general comments pertaining to the entire change or pinpoint concerns or shortcomings about a specific part of the change using inline comments. Recent studies show that reviewers often do not understand the change being reviewed and its context. Our ultimate goal is to identify the factors that confuse code reviewers and understand how confusion impacts the efficiency and effectiveness of code review(er)s. As the first step towards this goal we focus on the identification of confusion in developers' comments. Based on an existing theoretical framework categorizing expressions of confusion, we manually classify 800 comments from code reviews of the Android project. We observe that confusion can be reasonably well-identified by humans: raters achieve moderate agreement (Fleiss' kappa 0.59 for the general comments and 0.49 for the inline ones). Then, for each kind of comment we build a series of automatic classifiers that, depending on the goals of the further analysis, can be trained to achieve high precision (0.875 for the general comments and 0.615 for the inline ones), high recall (0.944 for the general comments and 0.988 for the inline ones), or substantial precision and recall (0.696 and 0.542 for the general comments and 0.434 and 0.583 for the inline ones, respectively). These results motivate further research on the impact of confusion on the code review process. Moreover, other researchers can employ the proposed classifiers to analyze confusion in other contexts where software development-related discussions occur, such as mailing lists. Index Terms-code review; confusion; machine learning.

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“I guess my question is”: What is the co-occurrence of uncertainty and learning in computer-mediated discourse?

Cited by 20 publications

References 58 publications

How Design Science Research Helps Improve Learning Efficiency in Online Conversations

How Design Science Research Helps Improve Learning Efficiency in Online Conversations

Managing uncertainty in scientific argumentation

Confusion Detection in Code Reviews

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