Computers and Education Open最新文献

The increasing reliance in schools on educational technology (edtech) poses a threat to children's digital privacy, particularly where children's data is used for or shared with others for commercial purposes. However, assessing the privacy of digital products is challenging given the opaque and evolving nature of the digital economy. Many schools share the responsibility for assessing edtech with education departments and authorities; however, to date, there has been very little empirical or theoretical work on how schools, education departments and authorities evaluate the privacy risks and data practices associated with the digital products used in schools. Drawing on an analysis of the Safer Technologies 4 Schools (ST4S) framework developed by Education Services Australia, education department policies, as well as interviews with education department staff and representatives, we examine how the data practices of digital products are examined in government schools in Australia and how schools are supported to choose tools that demonstrate best practice in terms of protecting students’ digital privacy. Findings suggest that while the goal of the ST4S framework is to streamline and unify digital privacy standards across states and territories, the complexity of the Australian education system, the number and diversity of digital products used, and the different governance approaches across the country make this difficult. Our conclusions reveal a compliance culture towards children's digital privacy, rather than a best practice approach, and a trust (or even ‘overtrust’) in ‘big tech’. However, we note some promising developments in this area and make recommendations for future research.

The integration of artificial intelligence into education has emerged as a promising avenue for enriching teaching and learning experiences. Nevertheless, the successful implementation of artificial intelligence in educational contexts hinges upon various factors, one of which is the perspective of instructors. With this in mind, this study aimed to examine the perspectives of 536 instructors in STEM and non-STEM disciplines regarding AI integration. The respondents’ thoughts, opinions, and concerns regarding advantages, disadvantages and challenges were gathered through an online questionnaire featuring both closed and open-ended questions. Additionally, a series of semi-structured interview sessions were conducted with a cohort of instructors to collect qualitative and quantitative data. The findings revealed that both STEM and non-STEM instructors expressed positive attitudes toward the integration of AI technologies into education. However, notable differences in responses and concerns were also identified in relation to the perceived capabilities and limitations of AI technologies within educational contexts. The results further elucidated a spectrum of opinions on the benefits (e.g., scalability and tirelessness), drawbacks (e.g., deepfake technology and comfort-seeking behavior), and potential challenges (e.g., educational disillusionment and espionage) associated with AI integration. The study concluded by discussing the implications of these findings for STEM and non-STEM education and offering recommendations for the effective and ethical integration of AI technologies in classrooms.

The outbreak of COVID-19 necessitated the call for integrating teaching and assessment into virtual environments. To explore the types of resources students accessed during online exams, engineering students were administered an unproctored online English for Academic Purposes (EAP) mid-term exam. Subsequently, a questionnaire was administered to investigate students’ use of resources while taking the exam. The results of an exploratory factor analysis was used to categorize students’ use of resources, external to one's self, shedding light on student perception of the act. The analysis revealed that 60 % of the common variance could be attributed to access to both human and nonhuman resources external to oneself. The transition from face to face teaching and assessment to online education has led to an increase in the utilization of resources external to one's self. Traditionally this act has been termed academic dishonesty. The study argues that this should not be viewed as a disruption, but rather a symptom of the transition embraced by online education with recourse to high accessibility to online and offline resources. Despite the general notion that of the use of resources external to one's self is discouraged, students exhibited a prevalent tendency to use resources external to themselves on the online exam. Of significance is the call for a redefinition of what constitutes academic misbehavior or dishonesty in a world that is digitally connected 24/7.

This study investigates the acceptance and utilization of artificial intelligence (AI) among in-service teachers in Lesotho, focusing on the mediating role of school support and resources (SSR). In Lesotho's educational landscape, which is characterized by a growing interest in technology integration, this study fills an essential gap in the existing literature by exploring in-service teachers' perspectives on AI adoption and the mediating influence of SSR. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework, the study adopts a cross-sectional design, collecting data from a sample of 315 in-service teachers through online surveys. The data was analyzed using maximum likelihood estimation. The results reveal a substantial positive relationship between perceived usefulness, perceived ease of use, and a positive attitude towards AI, with SSR playing a pivotal role as a complementary mediator in these connections. However, the study identifies a non-significant relationship between technical proficiency and behavioral intention, suggesting a need for further investigation into the technical skills essential for effective AI integration. The results highlight the critical role of SSR in shaping in-service teachers' intentions to use AI in their teaching practices. As a result, the study recommends tailored continuous professional development programs and collaborative learning communities to enhance teachers' skills. Additionally, it emphasizes the importance of advocating for policies that support AI integration in education and underscores the ethical considerations related to AI use. We discuss the implications of our results concerning integrating AI into teachers' teaching practices in schools and outline future directions.

Automated writing evaluation (AWE) has shown promise in enhancing students’ writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students’ perceptions of the MI Write AWE system. Students reported their perceptions of MI Write's usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students’ comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students’ ratings of MI Write's usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students’ perspectives of AWE.

In this commentary paper, I will introduce the concept of engagement data. I define engagement data as the digital metrics, calculations and visualisations that are deemed to give an insight into students’ on-task behaviour, their participation, their perceived capacity to pay attention, or their (technical) interactions with an educational platform. These kinds of data are common in Learning Management Systems and learning content platforms on which schools increasingly rely. The categories of engagement data discussed in this paper include time spent on-task, task completion, contribution, and biometric data. Besides conceptualising engagement data, this paper invites both scholars and educators to reflect critically on the datafication of engagement. I will argue that engagement data only offer a limited, quantified idea of student engagement and that this has far-reaching implications for children's digital rights. Children's behavioural data is harvested without their explicit consent or knowledge. These engagement data then become prescriptive constructs used for monitoring and accountability purposes, ignoring children's voice in relation to their own (dis)engagement.

To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing.

This paper seeks to contribute to the emergent literature on Artificial Intelligence (AI) literacy in higher education. Specifically, this convergent, mixed methods case study explores the impact of employing Generative AI (GenAI) tools and cyber-social teaching methods on the development of higher education students’ AI literacy. Three 8-week courses on advanced digital technologies for education in a graduate program in the College of Education at a mid-western US university served as the study sites. Data were based on 37 participants’ experiences with two different types of GenAI tools–a GenAI reviewer and GenAI image generator platforms. The application of the GenAI review tool relied on precision fine-tuning and transparency in AI-human interactions, while the AI image generation tools facilitated the participants’ reflection on their learning experiences and AI's role in education. Students’ interaction with both tools was designed to foster their learning regarding GenAI's strengths and limitations, and their responsible application in educational contexts. The findings revealed that the participants appeared to feel more comfortable using GenAI tools after their course experiences. The results also point to the students’ enhanced ability to understand and critically assess the value of AI applications in education. This study contributes to existing work on AI in higher education by introducing a novel pedagogical approach for AI literacy development showcasing the synergy between humans and artificial intelligence.

Ubiquitous datafication of children and families in educational and everyday settings is both a result of and catalyst for power asymmetries between digital platforms and their users. These platforms seek to know their users by extracting and analysing various streams of personal information, often discreetly. However, the users are rarely equipped to do the same to the platforms as information about these platforms is often obscured, convoluted, or simply unavailable. This article explores the possibilities of reversing such power imbalance via the design intervention of an Australia-focused educational technology (EdTech) database developed by the authors of this article. Employing a design intervention method, the database is a collection of publicly available information about EdTech companies and products that target young children and their parents or educators in Australia. This alternate commentary article presents and discusses the ongoing processes of developing the database to reflect on what it says about the power relations between EdTech providers and their users. It demonstrates how the database works as a pedagogical space where people learn how to critically unpack and think about contemporary EdTech platforms. The database is positioned as a point of convergence for different actors involved in children's digital learning to collectively understand what needs to be done to enact and protect children's digital rights in education.

The role schools are playing in children's datafication is increasingly recognised. This article explores how school publication of children's images on social media is contributing to children's datafication and how it is negatively impacting upon children's rights more generally. Identifying that schools across the UK, the US, Australia and Europe are now publishing children's images online, it situates the findings of an inquiry into UK practice within wider discussion of school practices worldwide.

This article uses practice-based documentary analysis to evaluate guidance provided to schools by local education authorities (LEAs) across England, Scotland and Wales. It identifies that LEAs are failing to recognise children's Article 12 UNCRC right to be heard. LEAs frequently advise schools to seek consent from parents, denying children agency. Online publications which identify children potentially contravene Article 16 UNCRC's requirement not to subject a child to arbitrary or unlawful interference with their privacy and may expose children to harms such as harassment and grooming. Nonetheless the template consent forms provided to parents provide minimal information about such risks. Concerns are raised that online publications are not always in children's best interests, thus contravening Article 3 UNCRC.

With similar concerns having been raised in other jurisdictions, this article argues for a new child-rights focused approach. Drawing upon Dimopoulos’ (2021) theory of children's decisional privacy and upon best practice from the UK, it suggests how schools across the globe can ensure children's rights and views inform school decisions to share children's images via social media.