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It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies. 

Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics to assess hands-on laboratory skills in K-12 science classrooms. This study demonstrated the feasibility of gauging laboratory skills based on students’ process data logged by a mobile augmented reality (AR) application for conducting science experiments. Students can use the mobile AR technology to investigate a variety of science phenomena that involve concepts central to physics understanding. Seventy-two students from a suburban middle school in the Northeastern United States participated in this study. They conducted experiments in pairs. Mining process data using Bayesian networks showed that most students who participated in this study demonstrated some degree of proficiency in laboratory skills. Also, findings indicated a positive correlation between laboratory skills and conceptual learning. The results suggested that learning analytics provides a possible solution to measure hands-on laboratory learning in real-time and at scale. 

Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students’ navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students’ various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes. 

Science identity has been widely discussed in recent years; however, research on its development in multimodal composing environments, especially in formal classroom settings, has yet to be fully investigated. This qualitative study unraveled the science identity development of sixth-grade students as they created multimodal science fiction stories in a STEAM course. Thirty-two students enrolled in the course and worked in groups of 3–5, and each student self-selected one of three roles: designer, scientist, or writer. This study focused on the students (n = 9) who took the role of scientist and examined their science identity development. Data sources include digital surveys, semi-structured group interviews, and multimodal artifacts. Our qualitative analysis suggests that (a) composing with modes of choices could drive interests in science; (b) students connected science practices in classrooms with those in professional domains through taking the role of scientist; (c) taking hybrid roles (i.e., a combination of scientist and other roles) while composing with multiple modes contributed to the recognition of science in non-science careers. Based on these findings, we discuss the implications for cultivating positive science identities and engaging early adolescents in career exploration. 

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.

What is already known about this topic

Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

What this paper adds

Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

Implications for practice and/or policy

It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

 

This article describes how middle school students collaborated in small groups to propose creative solutions to a variety of socioscientific issues through composing digital multimodal science fictions. In particular, we illustrate the various ways students explored socioscientific issues (e.g., climate change) through their multimodal sci-fi narratives, embodied different roles (e.g., scientist, designer, and writer) while collaboratively composing, and infused elements of their identities into their sci-fis. We conclude by discussing key strategies for integrating collaborative multimodal sci-fi narratives into different classroom contexts in order to support adolescents in creatively exploring and proposing solutions to challenging socioscientific issues.