Modeling Student Persistence in a Learning-By-Teaching Environment

Dumdumaya, Cristina E.

doi:10.1145/3209219.3213596

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Therefore, in recent years, there has been considerable research on identifying or detecting productive or unproductive persistence to redesign learning tasks as well as provide timely interventions or supports (e.g. Almeda, 2018; Botelho et al, 2019; Dumdumaya & Rodrigo, 2018; Flores & Rodrigo, 2020; Kai et al, 2018; Owen et al, 2019; Palaoag et al, 2016; Wang et al, 2020). The differences between productive and unproductive persistence have been revealed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The differences between productive and unproductive persistence have been revealed. For example, based on the interaction logs of students learning in an ITS, a Naïve Bayes model was developed by Dumdumaya and Rodrigo (2018) to detect task persistence. They found that average time on task, average number of reattempts, average time spent on resources following a failure, and the percentage of difficult problems attempted are important indicators for predicting students task persistence.…”

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Grit and learning: Understanding the sequential learning behaviours in snap!

Gao

Zhang

et al. 2022

Computer Assisted Learning

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Background As a non‐cognitive trait, grit plays an important role in human learning. Although students higher in grit are more likely to perform well on tests, how they learn in the process has been underexamined. Objectives This study attempted to explore how students with different levels of grit behave and learn in an exploratory learning environment. Methods In this study, 66 students participated in seven exploratory tasks in Snap! for approximately 60 min after a 30‐min lecture. Students were categorized into a high grit group and low grit group using a grit scale. The Mann–Whitney U test, epistemic network analysis and lag sequential analysis were used to explore the differences between groups in learning performance, technology acceptance and behavioural patterns. Results Students with different levels of grit engaged in explorative tasks in a short period of time might not present significantly different learning performance, perceptions of usefulness and ease of use, but students higher in grit, actively engaging more different types of activities, tended to put greater sustained effort to solve the challenging task. Take Away Although grit was not significantly correlated with learning performance when students engage in classroom‐based explorative activities, grit did predict whether students are more likely to explore and put greater sustained effort into solving the challenging task.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Grit and learning: Understanding the sequential learning behaviours in snap!

Gao

Zhang

et al. 2022

Computer Assisted Learning

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“…In our tutor, students can skip up to three problems per training level and thus we also measure the time students spent in these unsolved skipped problems as a measure of effort. Moreover, Dumdumaya et al defined their effort metric as the number of reattempts made on a problem after a failed attempt predicted task persistence [30]. In our tutor, this corresponds to the number of restarts on problems that students eventually solve.…”

Section: Effort and Persistencementioning

confidence: 99%

Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor

Maniktala,

Cody,

Barnes

et al. 2020

Preprint

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Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to how hints are presented. In this paper, we propose a new hint delivery mechanism called "Assertions" for providing unsolicited hints in a data-driven intelligent tutor. Assertions are partiallyworked example steps designed to appear within a student workspace, and in the same format as student-derived steps, to show students a possible subgoal leading to the solution. We hypothesized that Assertions can help address the well-known hint avoidance problem. In systems that only provide hints upon request, hint avoidance results in students not receiving hints when they are needed. Our unsolicited Assertions do not seek to improve student helpseeking, but rather seek to ensure students receive the help they need. We contrast Assertions with Messages, text-based, unsolicited hints that appear after student inactivity. Our results show that Assertions significantly increase unsolicited hint usage compared to Messages. Further, they show a significant aptitude-treatment interaction between Assertions and prior proficiency, with Assertions leading students with low prior proficiency to generate shorter (more efficient) posttest solutions faster. We also present a clustering analysis that shows patterns of productive persistence among students with low prior knowledge when the tutor provides unsolicited help in the form of Assertions. Overall, this work provides encouraging evidence that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance.

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