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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. 

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task‐based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as “clickbait” or “news.” Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human‐perception level and the computer‐extraction level and conceptualizing connections between them.

 

To sensitively detect multiple and cross-species disease-related targets from a single biological sample in a quick and reliable manner is of high importance in accurately diagnosing and monitoring diseases. Herein, a surface-enhanced Raman scattering (SERS) sensor based on a functionalized multiple-armed tetrahedral DNA nanostructure (FMTDN) immobilized silver nanorod (AgNR) array substrate and Au nanoparticle (AuNP) SERS tags is constructed to achieve both multiplex detection and enhanced sensitivity using a sandwich strategy. The sensor can achieve single, dual, and triple biomarker detections of three lung cancer-related nucleic acid and protein biomarkers, i.e. , miRNA-21, miRNA-486 and carcinoembryonic antigen (CEA) in human serum. The enhanced SERS signals in multiplex detections are due to the DNA self-assembled AuNP clusters on the silver nanorod array during the assay, and the experimentally obtained relative enhancement factor ratios, 150 for AuNP dimers and 840 for AuNP trimers, qualitatively agree with the numerically calculated local electric field enhancements. The proposed FMTDN-functionalized AgNR SERS sensor is capable of multiplex and cross-species detection of nucleic acid and protein biomarkers with improved sensitivity, which has great potential for the screening and clinical diagnosis of cancer in the early stage. 

Research focusing on the integration of computational thinking (CT) into science, technology, engineering, and mathematics (STEM) education started to emerge. We conducted a semi-systematic literature review on 55 empirical studies on this topic. Our findings include: (a) the majority of the studies adopted domain-general definitions of CT and a few proposed domain-specific CT definitions in STEM education; (b) the most popular instructional model was problem-based instruction, and the most popular topic contexts included game design, robotics, and computational modelling; (c) while the assessments of student learning in integrated CT and STEM education targeted different objectives with different formats, about a third of them assessed integrated CT and STEM; (d) about a quarter of the studies reported differential learning processes and outcomes between groups, but very few of them investigated how pedagogical design could improve equity. Based on the findings, suggestions for future research and practice in this field are discussed in terms of operationalizing and assessing CT in STEM contexts, instructional strategies for integrating CT in STEM, and research for broadening participation in integrated CT and STEM education. 

Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide personalized feedback on a timely basis. This study proposes a Bayesian network model to dynamically and automatically assess students’ engagement with engineering design tasks and to support formative feedback. Specifically, we applied a Bayesian network to 111 ninth-grade students’ process data logged by a computer-aided design software program that students used to solve an engineering design challenge. Evidence was extracted from the log files and fed into the Bayesian network to perform inferential reasoning and provide a barometer of their performance in the form of posterior probabilities. Results showed that the Bayesian network model was competent at predicting a student’s task performance. It performed well in both identifying students of a particular group (recall) and ensuring identified students were correctly labeled (precision). This study also suggests that Bayesian networks can be used to pinpoint a student’s strengths and weaknesses for applying relevant science knowledge to engineering design tasks. Future work of implementing this tool within the computer-aided design software will provide instructors a powerful tool to facilitate engineering design through automatically generating personalized feedback to students in real time. 

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).