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This study investigates small group collaborative learning with a technology-supported 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. 

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. 

Writing and revising scientific explanations helps students integrate disparate scientific ideas into a cohesive understanding of science. Natural language processing technologies can help assess students’ writing and give corresponding feedback, which supports their writing and revision of their scientific ideas. However, the feedback is not always helpful to students. Our study investigated 241 middle school students’ a) use of feedback, b) how it affected their revisions, and c) how these factors affected students’ writing improvement. We found that students made more content-related revisions when they used feedback. Making content-related revisions also assisted students in improving their writing. But students still found it difficult to make integrated revisions and did not use feedback often. Additional support to assist students to understand and use feedback, especially for students with limited science knowledge, is needed. 

To assess student knowledge, educators face a tradeoff between open-ended versus fixed response questions. Open-ended questions are easier to formulate, and provide greater insight into student learning,vbut are burdensome. Machine learning methods that could reduce the assessment burden also have a cost, given that large datasets of reliably assessed examples (labeled data) are required for training and testing. We address the human costs of assessment and data labeling using selective prediction, where the output of a machine learned model is used when the model makes a confident decision, but otherwise the model defers to a human decision-maker. The goal is to defer less often while maintaining human assessment quality on the total output. We refer to the deferral criteria as a deferral policy, and we show it is possible to learn when to defer. We first trained an autograder on a combination of historical data and a small amount of newly labeled data, achieving moderate performance. We then used the autograder output as input to a logistic regression to learn when to defer. The learned logistic regression equation constitutes a deferral policy. Tests of the selective prediction method on a held out test set showed that human-level assessment quality can be achieved with a major reduction of human effort. 

Building causal knowledge is critical to science learning and scientific explanations that require one to understand the how and why of a phenomenon. In the present study, we focused on writing about the how and why of a phenomenon. We used natural language processing (NLP) to provide automated feedback on middle school students’ writing about an underlying principle (the law of conservation of energy) and its related concepts. We report the role of understanding the underlying principle in writing based on NLP-generated feedback. 

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. 

In principle, educators can use writing to scaffold students’ understanding of increasingly complex science ideas. In practice, formative assessment of students’ science writing is very labor intensive. We present PyrEval+CR, an automated tool for formative assessment of middle school students’ science essays. It identifies each idea in a student’s science essay, and its importance in the curriculum. 

Science writing skills depend on a student’s ability to co-ordinate conceptual understanding of science with the ability to articulate ideas independently, and to distinguish between gradations of importance in ideas. Real-time scaffolding of student writing during and immediately after the writing process could ease the cognitive burden of learning to co-ordinate these skills and enhance student learning of science. This paper presents a design process for automated support of real-time scaffolding of middle school students’ science explanations. We describe our adaptation of an existing tool for automatic content assessment to align more closely with a rubric, and our reliance on data mining of historical examples of middle school science writing. On a reserved test set of semi-synthetic examples of science explanations, the modified tool demonstrated high correlation with the manual rubric. We conclude the tool can support a wide range of design options for customized student feedback in real time.