The Impact of Multiple Real-Time Scaffolding Experiences on Science Inquiry Practices

Li, Haiying; Gobert, Janice D.; Dickler, Rachel; Moussavi, Raha

doi:10.1007/978-3-319-91464-0_10

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…The combination of the CFE and the see-solve-move-respond phases could be an automatic and data-driven solution for the “assistance dilemma” (Miwa et al, 2012 ; Maniktala et al, 2020 ). It is important to provide a timely and appropriate amount of feedback to the learners (Li et al, 2018 ) and if we know the problem-solving phase the learner is in and the priority list of the feedback options, we can optimize the learning experience by controlling multiple feedbacks (Li et al, 2018 ). It is also important to understand the constraints of the interactive situation before providing the feedback (Khodeir et al, 2018 ), which can be achieved by having the CFE-based priorities a way to optimize the constraints-solution.…”

Section: Discussionmentioning

confidence: 99%

Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

Sharma

Lee-Cultura

Giannakos

2022

Front. Artif. Intell.

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The integration of Multimodal Data (MMD) and embodied learning systems (such as Motion Based Educational Games, MBEG), can help learning researchers to better understand the synergy between students' interactions and their learning experiences. Unfolding the dynamics behind this important synergy can lead to the design of intelligent agents which leverage students' movements and support their learning. However, real-time use of student-generated MMD derived from their interactions with embodied learning systems (MBEG in our case) is challenging and remains under-explored due to its complexity (e.g., handle sensor-data and enable an AI agent to use them). To bridge this gap, we conducted an in-situ study where 40 children, aged 9–12, played MBEG on maths and language development. We automatically, unobtrusively, and continuously monitored students' experiences using eye-tracking glasses, physiological wristbands, and Kinect, during game-play. This allowed us to understand the different cognitive and physiological dimensions of students' progress (right/wrong responses) during the three different stages of the MBEG problem-solving processes, namely the “see-solve-move-respond” (S2MR) cycle. We introduce the novel Carry Forward Effect (CFE); a phenomenon occurring in such games, whereby students propagate, or “carry forward,” the cognitive and physiological effects derived from their MMD, to subsequent phases in the see-solve-move-respond cycle. By identifying moments when the Carry Forward Effect is congruent (or not) to students' learning performance, we uncover opportunities for feedback delivery to encourage or subdue the impact of the CFE. Our results demonstrate the importance of wristband and eye-tracking data as key indicators for prioritizing adaptive feedback to support students in MBEG and emphasize the significance of using MMD to support students' performance in real-time educational settings.

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Section: Discussionmentioning

confidence: 99%

Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

Sharma

Lee-Cultura

Giannakos

2022

Front. Artif. Intell.

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“…The move towards measuring learning and other forms of progress using log data has allowed the development of measures which are immediate, proximal, and tightly aligned to the learning experience, with emerging interest by both data scientists (Fischer et al, 2020) and psychometricians (Bergner & von Davier, 2019) Simply developing the ability to measure learning as it changed (i.e., Pelánek, 2017) was a step that enabled mastery learning, the underpinning of many modern adaptive learning systems. Going beyond that to measuring complex learning and performance in realtime (Gobert et al, 2013;Henderson et al, 2020;Rowe et al, 2017) enabled learning systems such as Inq-ITS (Li et al, 2018) to provide feedback and support on complex skills such as scientific inquiry. Going further still, recent experimental systems measure and attempt to support students in learning to self-regulate their strategy (Duffy & Azevedo, 2015;Roll et al, 2018) and affect (DeFalco et al, 2018;Karumbaiah et al, 2017).…”

Section: Example: Edm and Learning Analytics: Better Measurement Of L...mentioning

confidence: 99%

Learning engineering: A view on where the field is at, where it’s going, and the research needed.

Baker¹,

Boser²,

Snow³

2022

Technology, Mind, and Behavior

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Although the idea of learning engineering dates back to the 1960s, there has been an explosion of interest in the area in the last decade. This interest has been driven by an expansion in the computational methods available both for scaled data analysis and for much faster experimentation and iteration on student learning experiences. This article describes the findings of a virtual convening brought together to discuss the potential of learning engineering and the key opportunities available for learning engineering over the next decades. We focus the many possibilities into ten key opportunities for the field, which in turn group into three broad areas of opportunity. We discuss the state of the current art in these ten opportunities and key points of leverage. In these cases, a relatively modest shift in the field's priorities and work may have an outsized impact.

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“…Several applications of ML-based text scoring have been used in assessment systems to provide automated guidance based on student responses (Tansomboon et al, 2017;Li et al, 2018;Lee et al, 2019). Importantly, ML-based scoring has been shown to be reliable and consistent with human scoring when measuring progression toward deeper understanding as reflected in learning progression-based assessments (Jescovitch et al, 2020;Wilson et al in revision 1 ).…”

Section: Using Artificial Intelligence Technology To Score Next-gener...mentioning

confidence: 99%

Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression

Kaldaras

Yoshida²,

Haudek³

2022

Front. Educ.

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IntroductionThe Framework for K-12 Science Education (the Framework) and the Next- Generation Science Standards (NGSS) define three dimensions of science: disciplinary core ideas, scientific and engineering practices, and crosscutting concepts and emphasize the integration of the three dimensions (3D) to reflect deep science understanding. The Framework also emphasizes the importance of using learning progressions (LPs) as roadmaps to guide assessment development. These assessments capable of measuring the integration of NGSS dimensions should probe the ability to explain phenomena and solve problems. This calls for the development of constructed response (CR) or open-ended assessments despite being expensive to score. Artificial intelligence (AI) technology such as machine learning (ML)-based approaches have been utilized to score and provide feedback on open-ended NGSS assessments aligned to LPs. ML approaches can use classifications resulting from holistic and analytic coding schemes for scoring short CR assessments. Analytic rubrics have been shown to be easier to evaluate for the validity of ML-based scores with respect to LP levels. However, a possible drawback of using analytic rubrics for NGSS-aligned CR assessments is the potential for oversimplification of integrated ideas. Here we describe how to deconstruct a 3D holistic rubric for CR assessments probing the levels of an NGSS-aligned LP for high school physical sciences.MethodsWe deconstruct this rubric into seven analytic categories to preserve the 3D nature of the rubric and its result scores and provide subsequent combinations of categories to LP levels.ResultsThe resulting analytic rubric had excellent human- human inter-rater reliability across seven categories (Cohen’s kappa range 0.82–0.97). We found overall scores of responses using the combination of analytic rubric very closely agreed with scores assigned using a holistic rubric (99% agreement), suggesting the 3D natures of the rubric and scores were maintained. We found differing levels of agreement between ML models using analytic rubric scores and human-assigned scores. ML models for categories with a low number of positive cases displayed the lowest level of agreement.DiscussionWe discuss these differences in bin performance and discuss the implications and further applications for this rubric deconstruction approach.

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The Impact of Multiple Real-Time Scaffolding Experiences on Science Inquiry Practices

Cited by 7 publications

References 9 publications

Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

Learning engineering: A view on where the field is at, where it’s going, and the research needed.

Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression

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