5 research outputs found

    Integrating Data Science into a General Education Information Technology Course: An Approach to Developing Data Savvy Undergraduates

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    The National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students

    Hacking the Non-Technical Brain: Maximizing Retention in a Core Introductory IT Course

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    Maximizing student retention of, and ability to apply, technical material in introductory information technology courses is a complex task, especially with respect to the general student population. This population struggles with the application of programming concepts in the time-constrained testing environment. Our study considers the implementation of daily quizzes in a core-curriculum information technology and programming course as a means to improve student concept retention and application. Between the first and second exams, the instructors implemented a series of high-frequency, no-risk quizzes. Of the four sections of the course that each instructor taught, two sections each were provided with the quizzes as the experimental group and two remained with the standard curriculum as the control. The results demonstrate the benefits of frequent, effortful recall on student performance in a core-curriculum information technology and programming course

    Data autonomy in the age of AI: designing autonomy-supportive data tools for children & families

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    The age of AI is a rapidly evolving and complex space for children. As children increasingly interact with AI-based apps, services and platforms, their data is being increasingly tracked, harvested, aggregated, analysed and exploited in multiple ways that include behavioural engineering and monetisation. Central to such datafication is online service providers' ability to analyse user data to infer personal attributes, subtly manipulating interests and beliefs through micro-targeting and opinion shaping. This can alter the way children perceive and interact with the world, undermining their autonomy. Yet, this datafication often unfolds behind the scenes in apps and services, remaining less noticed and discussed compared to the more straightforward data privacy issues like direct data collection or disclosure. On the other hand, children are often seen as less capable of navigating the intricacies of online life, with parents and guardians presumed to possess greater expertise to steer their children through the digital world. However, the rapid evolution of AI technology and online trends has outpaced parents' ability to keep up. As they adapt to platforms like Snapchat or YouTube, children may already move to the next trend, a shift accelerated by rapid datafication that heightens the challenge of effectively guiding children online. Consequently, there's a mounting call for a child-centred approach, which shifts from just protecting or limiting children with parents in charge, to actively guiding and empowering children to take a leading role. In this shift towards a child-centred approach, there's growing consensus on fostering children's autonomy in the digital space, encompassing the development of their understanding, values, self-determination, and self-identity. Given that data is the cornerstone of AI-based platforms' vast influence, this thesis uniquely focuses on the key concept of data autonomy for children. This exploration follows a structured four-step methodology: 1) Landscape analysis to comprehend the present scope of AI-based platforms for children and the prevalent challenges they encounter; 2) Conceptual review to elucidate the meaning of autonomy for children in the digital realm; 3) Empirical investigation focusing on children's perceptions, needs, and obstacles concerning data autonomy; and 4) Technical evaluation to assess the impact of technical interventions on children's sense of data autonomy. Synthesising the research presented in this thesis, we propose the pivotal concept of data autonomy for children in the age of AI, aiming to address their online wellbeing from a unique data perspective. This work not only lays the foundation for future research on data autonomy as a novel research agenda, but also prompts a rethinking of existing data governance structures towards a more ethical data landscape
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