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

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

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

Leveraging Emotional Learning Process (ELP) Data-based Interventions in Undergraduate Computing Education

The increasing demand for a diverse pool of computing talent combined with a persistent shortage of skilled workers, particularly from underrepresented groups, has engendered a need to support students pursuing computer science (CS) careers. This dissertation presents the results of an empirical study on the effectiveness of using emotional learning process (ELP) data to support community building in CS courses, particularly introductory programming courses. Building community, such as communities of practice, plays a part in retention,as students often cite social isolation and lack of support as reasons for withdrawing from computing programs. This is especially true for those from underrepresented groups.We designed and implemented the HELPd Empathy Tool (HELPd), integrating a customized IDE to gather programming behavior data and a private website to collect ELP data. HELPd used both sets of data to generate ELP data-based interventions. Interventions occurred but, unfortunately, participants did not act on them and, thus, we were unable to evaluate the usage of HELPd for community building. However, the study did result in some useful data.Participants were recruited from a class of 73 students enrolled in a semester-long CS0 programming course and were offered extra credit and a $10 gift card for completing the study. At the conclusion of the study, we were able to evaluate data from 19 participants.Data were gathered over an 8-week period divided into two parts. The first four weeks were the control period when programming behavior data were collected to establish a baseline. The treatment period included ELP data collection and ELP data-based interventions inaddition to the programming behavior data collection. Survey data were collected at pre-, mid-, and post-time points, that is, before the start of the 8-week period, before the start of the treatment period, and at the end of the treatment period. One- and two-way repeatedmeasures ANOVAs were conducted to explore the relationship of dependent variables, such as classroom community, empathy, and intention to persist, and the demographics of students, such as age or major.The results of our one-way repeated measures ANOVAs indicated no significant change in pre-, mid-, and post-scores for each dependent variable. The two-way repeated measures ANOVAs, however, showed computing and non-computing majors differed significantly in their intention to persist scores throughout the study. Although we did not see significant differences with our ANOVA analysis in general, we used ELP, programming behavior, and demographic data to produce counts for changes in survey responses over time. Whilechanges were noted between pre/mid and mid/post scores, no patterns emerged most likely because of the limited number of study participants.Based on an analysis of our results, we recommend design approaches for future iterations of ELP data-based interventions as well as the direct integration of these interventions into classroom coursework. We hope these recommendations will enable other researchers toascertain the effectiveness of ELP data-based interventions in community building through help-seeking and help-giving actions. We also discuss further customizing interventions to engage populations from underrepresented groups and from groups with other demographics

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

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