Search for: All records

Automated methods are becoming increasingly used to support formative feedback on students’ science explanation writing. Most of this work addresses students’ responses to short answer questions. We investigate automated feedback on students’ science explanation essays, which discuss multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass. We have found that students revisions generally improve their essays. Here, we focus on two factors that affect the accuracy of the automated feedback. First, learned representations of the six main ideas in the rubric differ with respect to their distinctiveness from each other, and therefore the ability of automated methods to identify them in student essays. Second, sometimes a student’s statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others. 

Automated writing evaluation (AWE) systems automatically assess and provide students with feedback on their writing. Despite learning benefits, students may not effectively interpret and utilize AI-generated feedback, thereby not maximizing their learning outcomes. A closely related issue is the accuracy of the systems, that students may not understand, are not perfect. Our study investigates whether students differentially addressed false positive and false negative AI-generated feedback errors on their science essays. We found that students addressed nearly all the false negative feedback; however, they addressed less than one-fourth of the false positive feedback. The odds of addressing a false positive feedback was 99% lower than addressing a false negative feedback, representing significant missed opportunities for revision and learning. We discuss the implications of these findings in the context of students’ learning. 

The present study examined teachers’ conceptualization of the role of AI in addressing inequity. Grounded in speculative design and education, we examined eight secondary public teachers’ thinking about AI in teaching and learning that may go beyond present horizons. Data were collected from individual interviews. Findings suggest that not only equity consciousness but also present engagement in a context of inequities were crucial to future dreaming of AI that does not harm but improve equity. 

In this paper, we present a case study of designing AI-human partnerships in a realworld context of science classrooms. We designed a classroom environment where AI technologies, teachers and peers worked synergistically to support students’ writing in science. In addition to an NLP algorithm to automatically assess students’ essays, we also designed (i) feedback that was easier for students to understand; (ii) participatory structures in the classroom focusing on reflection, peer review and discussion, and (iii) scaffolding by teachers to help students understand the feedback. Our results showed that students improved their written explanations, after receiving feedback and engaging in reflection activities. Our case study illustrates that Augmented Intelligence (USDoE, 2023), in which the strengths of AI complement the strengths of teachers and peers, while also overcoming the limitations of each, can provide multiple forms of support to foster learning and teaching. 

This study investigates small group collaborative learning with a technologysupported environment. We aim to reveal key aspects of collaborative learning by examining variations in interaction, the influence of small group collaboration on science knowledge integration, and the implications for individual knowledge mastery. Results underscore the importance of high-quality science discourse and user-friendly tools. The study also highlights that group-level negotiations may not always affect individual understanding. Overall, this research offers insights into the complexities of collaboration and its impact on science learning. 

Eighth grade students received automated feedback from PyrEval - an NLP tool - about their science essays. We examined essay quality change when revised. Regardless of prior physics knowledge, essay quality improved. Grounded in literature on AI explainability and trust in automated feedback, we also examined which PyrEval explanation predicted essay quality change. Essay quality improvement was predicted by high- and medium-accuracy feedback. 

Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback. 

This study explored the Idea Wall, a collaborative knowledge-building tool to support students’ collaboration in small groups during a plant biology science curriculum. We examined the affordances and challenges of the Idea Wall and found the effective use of the tool's spatial organization capabilities by students, particularly the Yup Zone and the intermediary neutral spaces, for collaboratively organizing notes. But there's also a need for improvements in some features of the tool’s design and instructional guidance. 

Computer-aided simulation-based platforms have been shown to be effective tools for teaching STEM concepts. At the same time, Computer Supported Collaborative Learning (CSCL) platforms encourage different viewpoints and approaches from the learners which can enrich the learning experience in STEM classrooms. The deployment in recent years of networked personal devices such as Chromebooks in classrooms has motivated educators to design collaborative learning tools for these devices. However, prior work has shown that using one-on-one devices may discourage students from talking among each other, which hinders collaboration. To understand the affordances of personal devices for CSCL tools within Biology curricula, we designed a collaborative plant growth simulation application that provides mirrored plant growth simulation views for every group member to facilitate a common visualization. In this paper, we present our findings from an in-the-wild study that evaluated the affordance and usability of the plant growth simulation application and investigated the nature of collaboration and engagement aided through the simulation mirroring feature. Our study results showed that the plant simulation application had high usability and acceptance. Moreover, mirroring the plant growth simulation improved collaboration, generated excitement, and stimulated conversation. We also identified episodes where collaboration was hindered due to off-task activities, troubleshooting, group dynamics, and lack of understanding that led us to outline some potential guidelines to improve the collaborative learning experience for the students in Biology classroom. 

Creating effective middle school STEM curricula requires a combination of individual and collaborative learning. Prior studies showed that finding a proper balance and providing uninterrupted knowledge transmission between different learning modes can be challenging in such mixed pedagogical approaches. In this paper, we present a multi-device interactive educational platform named SimSnap to teach biology curriculum to middle school children. SimSnap facilitates interactions among touchscreen Chromebooks to perform in-class individual and group activities. We present a usability analysis study with eight middle school children where they learn about the influence of temperature on tomato plant growth. Our study demonstrated that SimSnap facilitates group discussions to complete collaborative tasks. It also creates seamless knowledge propagation between prior to current tasks to learn about more complex concepts from previous simpler activities. Middle school children gave overall high usability ratings and positive feedback on SimSnap. This study also helped to outline some design recommendations for future improvements of SimSnap.