Automated, adaptive guidance for K-12 education

Gerard, Libby; Matuk, Camillia; McElhaney, Kevin W.; Linn, Marcia C.

doi:10.1016/j.edurev.2015.04.001

Cited by 54 publications

(29 citation statements)

References 50 publications

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“…The learning outcomes of students for each teacher align with previous research that illustrates the importance of teacher guidance (Gerard et al, ; McNeill & Krajcik, ; Ruiz‐Primo & Furtak, ). In a review of inquiry‐supported instruction, Furtak, Seidel, Iverson, and Briggs () found learning gains for inquiry based curricula, but these gains tended to be greater when students were supported with teacher guidance.…”

Section: Discussionsupporting

confidence: 80%

“…In WISE, teacher's guidance prompts and scores reflect engagement with student work (Gerard, Matuk, McElhaney, & Linn, ). The teachers demonstrated nuanced approaches in their use of the guidance tools (see Table ).…”

Section: Resultsmentioning

confidence: 99%

“…Research shows that guidance providing students with individualized, targeted guidance that focuses on integrating ideas results in better learning outcomes than procedural and logistical guidance (Gerard et al, ; Ruiz‐Primo & Furtak, ; Vitale et al, ). Rose and Jane both provided effective guidance.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, if students are effectively guided by the teacher to distinguish among competing ideas, they develop deeper understanding than when guided to elaborate on ideas. Automated guidance studies have shown some promise in offering not just additional, but targeted prompts to encourage further revision by students (Gerard et al, ). These results offer insight for both design of automated prompts and design of professional development for inquiry teaching.…”

Section: Discussionmentioning

confidence: 99%

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Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

et al. 2016

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In this study we explore how two different prompt types within an online computer-based inquiry learning environment enhance 392 7th grade students' explanations of evolution with 3 teachers. In the elaborating prompt condition, students are prompted to write explanations that support the accepted theory of evolution. In the competing prompt condition, students are prompted to write explanations that differentiate two views of evolution associated with Darwin and Lamarck. Data sources included a pretest and posttest, an embedded item, observations, logged teacher guidance, and teacher interviews. Findings show similar pretest to posttest gains in students' understanding of evolution for both conditions, but this pattern was not uniform across all three teachers.For one teacher, students who received competing theory prompts produced significantly higher gains than those who received elaborating theory prompts. A closer look at embedded student work reveals a higher degree of teacher participation (i.e., grading and guidance) than for the other teachers. Our findings illustrate how helping students distinguish between competing scientific claims can support learning in an inquiry unit, but may require a higher degree of teacher participation and reinforcement. We discuss the implications of these findings for enhancing students' scientific explanations.

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Section: Discussionsupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

et al. 2016

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“…However, given that teachers' time is an increasingly valuable resource, several adaptive software agents have recently been developed to support teachers on specific tasks and that adapt the guidance to students' characteristics. While Belland et al (2016) found no added effect of limited adaptive scaffolding over static scaffolding, intelligent tutoring systems (Nye et al, 2014), adaptive environments (Durlach and Ray, 2011;Vandewaetere et al, 2011), and automated feedback (Gerard et al, 2015(Gerard et al, , 2016 have all shown promising results. The common-sense conclusion appears to be that the more guidance is adapted to the individual student, the better the guidance-and thus the student-performs.…”

Section: Adaptive and Automated Scaffoldingmentioning

confidence: 99%

Automated Feedback Can Improve Hypothesis Quality

et al. 2019

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Stating a hypothesis is one of the central processes in inquiry learning, and often forms the starting point of the inquiry process. We designed, implemented, and evaluated an automated parsing and feedback system that informed students about the quality of hypotheses they had created in an online tool, the hypothesis scratchpad. In two pilot studies in different domains ("supply and demand" from economics and "electrical circuits" from physics) we determined the parser's accuracy by comparing its judgments with those of human experts. A satisfactory to high accuracy was reached. In the main study (in the "electrical circuits" domain), students were assigned to one of two conditions: no feedback (control) and automated feedback. We found that the subset of students in the experimental condition who asked for automated feedback on their hypotheses were much more likely to create a syntactically correct hypothesis than students in either condition who did not ask for feedback.

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From substitution to redefinition: A framework of machine learning‐based science assessment

et al. 2020

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This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map three variables in a three‐dimensional framework: construct, functionality, and automaticity. The 12 characteristics used to construct a profile for ML‐based science assessments for each article were further analyzed by a two‐step cluster analysis. The clusters identified for each variable were summarized into four levels to illustrate the evolution of each. We further conducted cluster analysis to identify four classes of assessment across the three variables. Based on the analysis, we conclude that ML has transformed—but not yet redefined—conventional science assessment practice in terms of fundamental purpose, the nature of the science assessment, and the relevant assessment challenges. Along with the three‐dimensional framework, we propose five anticipated trends for incorporating ML in science assessment practice for future studies: addressing developmental cognition, changing the process of educational decision making, personalized science learning, borrowing 'good' to advance 'good', and integrating knowledge from other disciplines into science assessment.

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Automated, adaptive guidance for K-12 education

Cited by 54 publications

References 50 publications

Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

Enhancing student explanations of evolution: Comparing elaborating and competing theory prompts

Automated Feedback Can Improve Hypothesis Quality

From substitution to redefinition: A framework of machine learning‐based science assessment

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