Computers & Education最新文献

Although access to technology has substantially increased in U.S. schools, inequities in technology usage persist, especially in classrooms serving disproportionately higher numbers of minoritized students. Driven by expert recommendations for increased teacher preparation focused on addressing this phenomenon, we sought to cultivate preservice teachers' (PSTs') digital equity mindsets in a stand-alone technology integration course. A qualitative analysis of pre- and post-course drawings and written reflections where PSTs indicated their intentions to build digital equity in the curriculum showed a significant evolution of their mindsets in five related dimensions of digital equity. This study provides novel insight into how PSTs' digital equity mindsets can be effectively cultivated, and how doing so might engender more equitable technology integration practices in the K-12 curriculum.

Video-based reflection on one's own teaching represents a crucial tool in teacher education. When student teachers reflect on negative classroom events, it elicits “self-focused attention,” which has been associated with more intense negative emotionality. Self-focused attention can be quantitatively captured using first-person singular pronouns (“I,” “me,” “my”) in written reflections by, for instance, student teachers. What is unclear is whether student teachers' use of these first-person singular pronouns in their written reflections is linked to and predicts their negative affective experiences during teaching. For the present study, a fully immersive virtual reality (VR) classroom was implemented in which student teachers taught a lesson, provided written reflections on their teaching, and then taught a second lesson. We measured N = 59 student teachers' self-reported stress and heartrate responses while teaching in the VR classroom and determined the percentage of first-person singular pronouns in their written reflections. Firstly, our results showed that the use of first-person singular pronouns provides incremental information on manual ratings of student teachers' foci in their written reflections. Secondly, student teachers' heartrates during instruction—a measure of physiological stress—were associated with the use of first-person singular pronouns in subsequent written reflections. Thirdly, the use of first-person singular pronouns predicted the increase in physiological stress from the first to the second round of VR teaching. We discuss implications for automated feedback and for designing reflective tasks.

This study examined middle school students' perceptions of an automated writing evaluation (AWE) system, MI Write. We summarize students' perceptions of MI Write's usability, usefulness, and desirability both quantitatively and qualitatively. We then estimate hierarchical entry regression models that account for district context, classroom climate, demographic factors (i.e., gender, special education status, limited English proficiency status, socioeconomic status, grade), students' writing-related beliefs and affect, and students' writing proficiency as predictors of students' perceptions. Controlling for districts, students reporting more optimal classroom climate also reported higher usability, usefulness, and desirability for MI Write. Also, model results revealed that eighth graders, students with limited English proficiency, and students of lower socioeconomic status perceived MI Write relatively more useable; students with lower socioeconomic status also perceived MI Write relatively more useful and desirable. Students who liked writing more and more strongly believed that writing is a recursive process viewed MI Write as more useable, useful, and desirable. Students with greater writing proficiency viewed MI Write as less useable and useful; writing proficiency was not related to desirability perceptions. We conclude with a discussion of implications and future directions.

Classroom videos are commonly used to observe and assess teaching quality in both teacher education and research on teaching and learning. In recent years, traditional video environments have increasingly been complemented by 360-degree videos, which promise a more immersive and realistic classroom experience and may affect the teaching-quality ratings that result. The aim of the present study was to explore differences between immersive 360-degree and traditional video environments in preservice teachers' (PSTs') classroom observation experiences and teaching-quality ratings. Seventy-five PSTs observed two classroom videos: one using an immersive 360-degree video environment and one using a traditional video environment. For 360-degree videos, observers reported higher cognitive, affective, and physiological involvement in the classroom situation, higher motivation, and higher mental effort when making classroom observations. For one aspect of teaching quality (focus on key concepts), the observation-based ratings resulting from 360-degree videos were better aligned with experts’ assessments of the videos. Furthermore, the results showed that the differences between video environments remained significant after the novelty of VR technology was controlled for.

Recently, generative artificial intelligence (AI)-powered chatbots such as ChatGPT and Bing Chat have garnered increasing attention on a global scale. Previous studies have focused mostly on the influence of generative AI on writing while few researchers have investigated how generative AI can facilitate students' multimodal writing process. To fill in this gap, we explored the generative AI-assisted composing processes of two groups of English as a foreign language (EFL) writers over two weeks in this qualitative study. One group completed a multimodal PowerPoint (PPT) project, and the other group completed a traditional argumentative essay project. Our data consist of students’ screen recordings with think-aloud protocols, final multimodal texts, and post-project interviews. Our analysis showed different patterns in text production across the two groups. Students in the PPT group tended to construct more bridge texts and examples to corroborate their sub-claims in the hierarchical order. They also inclined to borrow the summarized search results from the Bing Chat to expand texts for their PPT slides. With regard to image generation for PPT slides, descriptions of AI images from ChatGPT were used as effective prompts to generate AI images from Bing Image Creator. Moreover, students were interested in producing and refining AI images following the recommended prompts by Bing Chat. They also evaluated these AI images from different perspectives. We conclude the study with a discussion on the pedagogical implications and suggestions for further study.

Video learning is a crucial component in flipped learning related to learners' readiness to attend more complex face-to-face activities. However, instructors may not know students' preparedness and motivation for each class. This study intended to examine the underlying mechanism between students' before-class video-viewing engagement and performance. We investigated learners' motivation (i.e., autonomous and controlled motivation) based on self-determination theory and controlled for their gender, age, and prior performance. An integrated temporal model was built under the dynamic structural equation modeling framework. Participants were 118 Taiwanese graduate students (78 females and 40 males) with a mean age of 26.14 years old. Study findings showed that students engage with the video content in a consistent viewing pattern, which has a significant relationship with their motivation and academic performance from week to week. The frequency of skipping backward was negatively associated with controlled motivation, while the frequency of pauses was positively associated with autonomous and controlled motivation. A high frequency of skipping forward predicted lower learning performance. Besides, a higher ratio of skipping backward predicted higher controlled motivation. Male students, older students, and students with higher prior performance exhibited higher autonomous motivation, which was related to better learning performance. Understanding these relationships can help educators and instructional designers develop more effective strategies for fostering students’ preparedness, engagement and success in active learning contexts. The study findings were discussed considering theoretical advancement and practical implications for enhancing personalized and active learning in higher education to achieve educational equity.

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features.

Critical thinking, Communication, Collaboration, and Creativity are regarded as key skills for today's learners. Interest in robotics usage to develop such competencies in educational settings arises but there hasn't been so far mapping of the research conducted in this field. Thus, we present a scoping review of 59 articles published from 2012 to 2023 in 4 Databases and 2 Registers examining kindergarteners' skills through Educational Robotics. Our goal was to map which skills are commonly examined in kindergarten and under which terminology as well as to investigate the studies' features, the types of robotics, and the assessment tools utilized. Our findings depict that several skills are evaluated using different terms. Critical thinking is examined most frequently, creativity the least, and communication is integrated into collaboration and the opposite. Most of the researchers used the mixed method focusing on qualitative tools, examining quite a small sample size, and conducting the intervention for a short time. Also: BeeBot, Lego robotics kits (WeDo, Mindstorms), and social, humanoid robots are often used. Additionally, the evaluation tools used in the reviewed papers are presented and categorized into groups. This scoping demonstrates that robotics might affect positively the children's cognitive skills and creativity but may not facilitate their interactions so much regarding emotional expressions. Moreover, a gap in quantitative research on kindergarteners' skills development via Educational Robotics is spotted. Finally, this scoping review suggests: creating clear terms for skills, developing robotics tailored to kindergarten age, forming full robotics activities, and designing modular evaluation tools for young learners' capacities.

AI-powered learning technologies are increasingly being used to automate and scaffold learning activities (e.g., personalised reminders for completing tasks, automated real-time feedback for improving writing, or recommendations for when and what to study). While the prevailing view is that these technologies generally have a positive effect on student learning, their impact on students’ agency and ability to self-regulate their learning is under-explored. Do students learn from the regular, detailed and personalised feedback provided by AI systems, and will they continue to exhibit similar behaviour in the absence of assistance? Or do they instead continue to rely on AI assistance without learning from it? To contribute to filling this research gap, we conducted a randomised controlled experiment that explored the impact of AI assistance on student agency in the context of peer feedback. With 1625 students across 10 courses, an experiment was conducted using peer review. During the initial four-week period, students were guided by AI features that utilised techniques such as rule-based suggestion detection, semantic similarity, and comparison with previous comments made by the reviewer to enhance their submissions if the feedback provided was deemed insufficiently detailed or general in nature. Over the following four weeks, students were divided into four different groups: control (AI) received prompts, (NR) received no prompts, (SR) received self-monitoring checklists in place of AI prompts, and (SAI) had access to both AI prompts and self-monitoring checklists. Results of the experiment suggest that students tended to rely on rather than learn from AI assistance. If AI assistance was removed, self-regulated strategies could help fill the gap but were not as effective as AI assistance. Results also showed that hybrid human-AI approaches that complement AI assistance with self-regulated strategies (SAI) were not more effective than AI assistance on its own. We conclude by discussing the broader benefits, challenges and implications of relying on AI assistance in relation to student agency in a world where we learn, live and work with AI.

Over the past decade, there has been an enormous upsurge in the use of educational apps in primary schools. However, few studies have examined how children interact with these apps and how their interaction patterns relate to learning outcomes. An interaction pattern that is potentially detrimental to learning is repeated mistakes, defined as making the same mistake more than once when answering a task. With interaction data from an eight-week digital vocabulary intervention, we examined 1) whether the propensity to make repeated mistakes changes across app sessions, and 2) how repeated mistakes relate to children's prior knowledge and their learning gains from the intervention. Our sample consisted of 363 Norwegian second graders who worked with the vocabulary app in a randomized controlled trial. Using growth curve modeling and confirmatory factor analyses, we found that the propensity to repeat mistakes remained stable over time. Furthermore, a structural equation model showed that repeated mistakes related negatively to both pre-test and post-test scores. A substantial proportion of the total effect of prior knowledge on learning gains was mediated by the propensity to repeat mistakes. Children who made more repeated mistakes had lower expected learning gains across all levels of prior knowledge. We suggest that the propensity to repeat mistakes may pose a double threat to learning by diminishing exposure to relevant content, and amplifying the exposure to incorrect input. Considering the stability of mistake repetition, it is crucial to identify students with a high propensity to repeat mistakes and help them break the pattern to support learning. App developers can help this process by implementing automatic detection and feedback.