Wheel-Spinning: Students Who Fail to Master a Skill

Beck, Joseph E.; Gong, Yue

doi:10.1007/978-3-642-39112-5_44

Cited by 109 publications

(64 citation statements)

References 7 publications

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“…In one of my recent studies, I found that there are students who, despite access to the same instructional supports, do not successfully take advantage of them and therefore do not learn (Kelly, Wang, Thompson, & Heffernan 2015). This has resulted in a phenomenon called wheel spinning (Beck & Gong, 2013), where students persist without making progress towards learning. I hypothesize that wheel spinning is in part a result of ineffective help-seeking behaviors.…”

Section: Help Seeking Behaviorsmentioning

confidence: 99%

Artificial Intelligence in Education

Conati¹,

Heffernan²,

Mitrović³

et al. 2015

Lecture Notes in Computer Science

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Abstract. Peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. However, in previous models, the parameter estimation accuracy decreases as the number of raters increases because the number of rater parameters increases drastically. To solve that problem, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible.

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Section: Help Seeking Behaviorsmentioning

confidence: 99%

Artificial Intelligence in Education

Conati¹,

Heffernan²,

Mitrović³

et al. 2015

Lecture Notes in Computer Science

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“…Some researchers have demonstrated how models trained on educational technology interaction data can predict long‐term standardized test results (e.g.,) or college enrollment (e.g.,). Others have built models to predict when students will get stuck in a course or drop out (e.g.,). Other areas of interest include employing recommender systems in education (e.g.,), social network analysis (e.g.,), and peer grading …”

Section: Edm Addresses Research Questions About the Psychology Of Leamentioning

confidence: 99%

Data mining and education

Koedinger

D’Mello

McLaughlin

et al. 2015

WIRES Cognitive Science

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An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board.

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“…The 25 studies presented in Table 5 span a variety of investigations including: assessing the effect of various types of video tutoring (i.e., pencasts, teacher recorded instruction, online resources) compared to traditional text-based tutoring across multiple designs (i.e., using scaffolding, using hints, as an intervention to wheel-spinning [2], or provided based on student choice), investigating the manipulation of content (i.e., interspersing learning with humor through comics in content or feedback, asking students to gauge their confidence in solving problem content, and altering student mindset (as inspired by [4]), and challenging cognitive principles (i.e., mental representations, and alterations in the consistency of math equations). Assignment names, as presented in Table 5, are tagged with the grade level and domain of the skill content as defined by Common Core State Standards [10].…”

Section: Analysis At Scalementioning

confidence: 99%

“…Users of online learning platforms (i.e., students and teachers) create hundreds of thousands of data points each day, with databases of rich learner information growing exponentially as platforms gain popularity and validity as powerful learning aids. Beyond achievement measures, these systems provide opportunities to collect information including (but not limited to) behavior and affect [2,17], learning interventions within content or feedback [14,15], and interactions between skill domains that help guide curriculum development [1]. Through flexibility in content design, manipulation, and delivery, researchers are able to tap into the elements that drive effective learning within authentic K-12 classroom environments.…”

Section: Introductionmentioning

confidence: 99%

The assessment of learning infrastructure (ALI)

Ostrow

Selent

Wang

et al. 2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge

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Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.

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Wheel-Spinning: Students Who Fail to Master a Skill

Cited by 109 publications

References 7 publications

Artificial Intelligence in Education

Artificial Intelligence in Education

Data mining and education

The assessment of learning infrastructure (ALI)

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