Improving Science Achievement—Is It Possible? Evaluating the Efficacy of a High School Chemistry and Physics Project-Based Learning Intervention

Krajcik, Joseph; Lavonen, Jari; Salmela‐Aro, Katariina; Klager, Christopher; Bradford, Lydia; Chen, I-Chien; Baker, Quinton; Touitou, Israel; Peek-Brown, Deborah; Dezendorf, Rachel Marias; Maestrales, Sarah; Bartz, Kayla

doi:10.3102/0013189x211067742

Cited by 30 publications

(44 citation statements)

References 33 publications

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“…Due to individual difference in learning preferences and students' familiarity with the representational "language," multirepresentations allow them to select the best channel to express their knowledge. Schneider et al (2022) employed a randomized controlled trial design to examine the efficacy of high school chemistry and physics project-based learning interventions.…”

Section: Multirepresentation Of Phenomena: An Inclusive Means For Ass...mentioning

confidence: 99%

“…Due to individual difference in learning preferences and students' familiarity with the representational “language,” multirepresentations allow them to select the best channel to express their knowledge. Schneider et al (2022) employed a randomized controlled trial design to examine the efficacy of high school chemistry and physics project‐based learning interventions. They found that students' proficiency improved significantly when experiencing multirepresentational modeling activities in project‐based learning classrooms, as compared with students in traditional science classrooms.…”

Section: An Inclusive Perspective Of Modeling Using Multirepresentationsmentioning

confidence: 99%

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Applying machine learning to automatically assess scientific models

Zhai

Krajcik

2022

J Res Sci Teach

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Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student‐developed models is time‐ and cost‐intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student‐drawn models and their written descriptions of those models. We developed six modeling assessment tasks for middle school students that integrate disciplinary core ideas and crosscutting concepts with the modeling practice. For each task, we asked students to draw a model and write a description of that model, which gave students with diverse backgrounds an opportunity to represent their understanding in multiple ways. We then collected student responses to the six tasks and had human experts score a subset of those responses. We used the human‐scored student responses to develop ML algorithmic models (AMs) and to train the computer. Validation using new data suggests that the machine‐assigned scores achieved robust agreements with human consent scores. Qualitative analysis of student‐drawn models further revealed five characteristics that might impact machine scoring accuracy: Alternative expression, confusing label, inconsistent size, inconsistent position, and redundant information. We argue that these five characteristics should be considered when developing machine‐scorable modeling tasks.

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Section: Multirepresentation Of Phenomena: An Inclusive Means For Ass...mentioning

confidence: 99%

Section: An Inclusive Perspective Of Modeling Using Multirepresentationsmentioning

confidence: 99%

Applying machine learning to automatically assess scientific models

Zhai

Krajcik

2022

J Res Sci Teach

Self Cite

View full text Add to dashboard Cite

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“…The activities students were most interested in involved doing science rather than simply reading about it, which is what the CESE intervention emphasises. In the full sample in the prior year, a positive effect for science learning was found among treatment students, including for low‐income and minority students who were over‐sampled (Schneider et al, under review). This leads to concerns of potential learning loss when students are unable to participate in experiential science learning.…”

Section: Discussionmentioning

confidence: 99%

“…In the United States, CESE was undergoing a maturation study. Having shown promising results for the learning outcomes of treatment students in the previous year (Schneider et al, under review), the goal was to determine whether teachers in their second year of teaching the project‐based learning intervention would show greater impacts on learning outcomes than teachers implementing the lessons for the first time. Although the pandemic ended the ability to study students' engagement during hands‐on lessons, a unique opportunity arose to study academic engagement in this remote learning environment.…”

Section: A Two‐country Intervention Facing a Pandemicmentioning

confidence: 99%

“…When students are engaged in learning we expect them to feel that their science work is related to their future education goals (Schneider et al, 2016). U.S. students who participated in the CESE intervention during the previous year's efficacy trial showed increased educational ambitions (Schneider et al, under review). During a pandemic, however, uncertain futures might weaken educational goals.…”

Section: Studying Engagementmentioning

confidence: 99%

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U.S. and Finnish high school science engagement during the COVID‐19 pandemic

Maestrales

Dezendorf²,

Tang

et al. 2021

Int J Psychol

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When the COVID‐19 pandemic struck, research teams in the United States and Finland were collaborating on a study to improve adolescent academic engagement in chemistry and physics and the impact remote teaching on academic, social, and emotional learning. The ongoing “Crafting Engaging Science Environments” (CESE) intervention afforded a rare data collection opportunity. In the United States, students were surveyed at the beginning of the school year and again in May, providing information for the same 751 students from before and during the pandemic. In Finland, 203 students were surveyed during remote learning. Findings from both countries during this period of remote learning revealed that students' academic engagement was positively correlated with participation in hands‐on, project‐based lessons. In Finland, results showed that situational engagement occurred in only 4.7% of sampled cases. In the United States, students show that academic engagement, primarily the aspect of challenge, was enhanced during remote learning. Engagement was in turn correlated with positive socioemotional constructs related to science learning. The study's findings emphasise the importance of finding ways to ensure equitable opportunities for students to participate in project‐based activities when learning remotely.

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Teacher professional development programs integrating science and language with multilingual learners: A conceptual framework

2023

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As the vision in A Framework for K‐12 Science Education and the Next Generation Science Standards (NGSS) takes hold in schools and classrooms, there is an urgent need for teacher professional development (PD) programs that align with NGSS‐designed curriculum materials and address the unique strengths and needs of diverse student groups, including multilingual learners (MLs). The purpose of this article is to propose our conceptual framework for PD programs that aligns with current reform efforts and is grounded in the mutually supportive nature of contemporary science and language instructional shifts. Specifically, we examine our previous NGSS‐designed curriculum development project with MLs and review the literature in science education and language education with MLs. Our conceptual framework for PD programs is grounded in the perspective of symmetry that teacher professional learning experiences should be symmetrical to the learning experiences we organize for students. Grounded in this perspective, our conceptual framework consists of three design principles that describe how PD programs can guide teachers to (a) develop an asset‐oriented view of MLs and instructional practices for recognizing and leveraging their assets, (b) integrate science and language in mutually supportive ways with MLs, and (c) develop more sophisticated instructional practices for integrating science and language with MLs over time. We describe contributions of our conceptual framework, which could generate a new research agenda and inform PD programs aimed at facilitating uptake of NGSS‐designed curriculum materials in linguistically diverse science classrooms.

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Improving Science Achievement—Is It Possible? Evaluating the Efficacy of a High School Chemistry and Physics Project-Based Learning Intervention

Cited by 30 publications

References 33 publications

Applying machine learning to automatically assess scientific models

Applying machine learning to automatically assess scientific models

U.S. and Finnish high school science engagement during the COVID‐19 pandemic

Teacher professional development programs integrating science and language with multilingual learners: A conceptual framework

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