Search for: All records

Most postsecondary cybersecurity education focuses on technical knowledge and skills without commensurate attention to vital nontechnical skills. In this position paper, we argue that cybersecurity education must integrate the teaching and practicing of non-technical competencies alongside technical knowledge and skills to ensure that both technical and non-technical skills transfer to cybersecurity workplaces. 

Weaving is a fabrication process that is grounded in mathematics and engineering: from the binary, matrix-like nature of the pattern drafts weavers have used for centuries, to the punch card programming of the first Jacquard looms. This intersection of disciplines provides an opportunity to ground abstract mathematical concepts in a concrete and embodied art, viewing this textile art through the lens of engineering. Currently, available looms are not optimized to take advantage of this opportunity to increase mathematics learning by providing hands-on interdisciplinary learning in collegiate classrooms. In this work, we present SPEERLoom: an open-source, robotic Jacquard loom kit designed to be a tool for interweaving cloth fabrication, mathematics, and engineering to support interdisciplinary learning in the classroom. We discuss the design requirements and subsequent design of SPEERLoom. We also present the results of a pilot study in a post-secondary class finding that SPEERLoom supports hands-on, interdisciplinary learning of math, engineering, and textiles. 

For nearly two decades, conversational agents have been used to structure group interactions in online chat-based environments. More recently, this form of dynamic support for collaborative learning has been extended to physical spaces using a combination of multimodal sensing technologies and instrumentation installed within a physical space. This demo extends the reach of dynamic support for collaboration still further through an application of what has recently been termed on-device machine learning, which enables a portable form of multimodal detection to trigger real-time responses. 

Socially shared regulation (SSRL) has been recognized as a contributing factor to successful collaborative learning. In this paper, we adopted a process-oriented approach to examine how students deliberate for SSRL through different regulatory triggers in a collaborative learning context. More specifically, this study examines the relationship between different types of regulatory and deliberative characteristics of interactions and then explores their sequential patterns through cognitive and emotional triggers. The study involved ten triads of secondary students (N=30) working on a collaborative learning task. The process mining results showed that following regulatory triggers, groups switched to more metacognitive and socio-emotional interactions as they adopted control strategies, such as defining problems, establishing strategies, and providing social support. This study not only contributes to a better understanding of SSRL by exploring learners’ deliberative negotiation but also presents a novel fine-grain video analysis approach to examine SSRL in collaborative learning. 

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. 

At its core, collaboration is about bringing diverse perspectives together to create something new. Diversity may arise along a multiplicity of dimensions, leading to some very similar challenges, and other dimension-specific challenges, each of which require discrete skills to address. Interdisciplinary collaboration, while understudied, has particular workplace relevance. This research seeks to understand what is specific to interdisciplinary collaboration as part of a broader agenda to operationalize key underlying skills that enable interdisciplinary collaboration and subsequently assess and support interdisciplinary collaboration, both in the classroom and in the workplace. The aim of this poster presentation is to engage the community in an intellectual exchange about underlying questions to inform work in progress. 

Understanding abstract concepts in mathematics has continuously presented as a challenge, but the use of directed and spontaneous gestures has shown to support learning and ground higher-order thought. Within embodied learning, gesture has been investigated as part of a multimodal assemblage with speech and movement, centering the body in interaction with the environment. We present a case study of one dyad’s undertaking of a robotic arm activity, targeting learning outcomes in matrix algebra, robotics, and spatial thinking. Through a body syntonicity lens and drawing on video and pre- and post- assessment data, we evaluate learning gains and investigate the multimodal processes contributing to them. We found gesture, speech, and body movement grounded understanding of vector and matrix operations, spatial reasoning, and robotics, as anchored by the physical robotic arm, with implications for the design of learning environments that employ directed gestures. 

Abstract Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges. 

Contributing to the literature on aptitude-treatment interactions between worked examples and problem-solving, this paper addresses differential learning from the two approaches when students are positioned as domain experts learning new concepts. Our evaluation is situated in a team project that is part of an advanced software engineering course. In this course, students who possess foundational domain knowledge but are learning new concepts engage alternatively in programming followed by worked example-based reflection. They are either allowed to finish programming or are curtailed after a pre-specified time to participate in a longer worked example-based reflection. We find significant pre- to post-test learning gains in both conditions. Then, we not only find significantly more learning when students participated in longer worked example-based reflections but also a significant performance improvement on a problem-solving transfer task. These findings suggest that domain experts learning new concepts benefit more from worked example-based reflections than from problem-solving.