Computers and Education Open最新文献

The public release of generative artificial intelligence (GAI) tools (e.g., ChatGPT) has had a disruptive effect on the assessment practices of higher education institutions (HEIs) worldwide. Concerns have largely been associated with academic integrity, cheating and plagiarism. HEIs have had to develop guidelines in response to GAI. As many of these guidelines were developed in haste and could affect a large number of instructors and students, there is a need to examine their content, coverage and suitability. This review examines the extent to which the world's 50 top-ranking HEIs have developed or modified their assessment guidelines to address GAI use and, where guidelines exist, the primary content and advice given to guide instructors in their GAI assessment design and practices. The findings show that just under half of the institutions have developed publicly available guidelines. The guidelines cover three main areas: academic integrity, advice on assessment design and communicating with students. Amongst the suggestions for teachers on assessment design, two appear particularly pertinent in helping develop effective assessment tasks and developing learners’ AI literacy: first, running assessment tasks through GAI to check the extent to which the tool can accomplish the task and, second, having students use GAI as part of the assessment process. Overall, the review suggests that HEIs have come to accept the use of GAI and drafted assessment guidelines to advise instructors on its use. In the article, we argue that it may be beneficial to embrace GAI as a part of the assessment process since this is the reality of today's educational and job landscape. This will require instructors to develop a new competence - generative artificial intelligence assessment literacy - which is conceptualised in this article.

The article presents a Portuguese national study on integrating information and communication technologies (ICT) in initial teacher education courses (ITE), which sought to highlight to what extent ICT are presently considered in the training curriculum of future teachers. The study was developed on a documental corpus of 819-course syllabus sheets of 45 master's courses, which were analysed through a methodology supported by content analysis procedures. The results achieved were also put under analysis, considering the European Framework for Educators' Digital Competence (DigCompEdu), and a shy presence of technologies was identified in the analysed curricula. Regarding DigCompEdu, which functions as a common framework to be followed in the European Union, the results show that this is configured as another factor to be remedied in the context of integrating ICT in ITE in Portugal. Among the contributions that this study promotes is the finding of the reduced presence of ICT identified in the context under study, a reality familiar to several countries, which implies in curricular terms, initial teacher training is still ineffective in preparing teachers to meet the needs of today's digital society.

Digital media are widely used among Higher Education (HE) students; however, it is not clear yet how students from different disciplines apply learning technology. When differences in technology use were found among disciplines, they were identified broadly, for example, between soft and hard sciences, that is, social sciences/humanities vs. natural sciences. Beyond this, disciplines may also differ according to their traditions, norms and values that form their discipline-specific cultures. Thus, based on the theoretical perspectives of social cognitive theory and organisational culture, this present study aimed to examine students’ technology use in different HE disciplines. In a multilevel analysis, data from four German universities were analyzed, including more than 1,200 students from 105 disciplines. The findings suggest that students in different disciplines vary substantially in their average use of study-related technology. Furthermore, on the individual level, study-related technology use depends on individual factors, e.g., perceived usefulness. This present study's results contribute to further extending our knowledge about student technology use in HE. Above all, the conceptual and methodical models can serve as a theoretical and empirical basis for future studies. To analyze disciplinary differences, the study shows that a rough classification, for example hard versus soft disciplines, is not sufficient. Instead, the different disciplines need to be distinguished.

Immediate feedback has been considered a cornerstone of online language learning platforms. However, a closer reading of relevant research reveals that the definition of the term “immediate feedback” is inconsistent. Furthermore, findings from the STEM literature have not been well supported by other fields. As a result, clarification is required to assess which type of immediate feedback improves students’ performance in a computer-assisted learning environment. Moreover, research on the effects of immediate feedback outside of STEM classes should provide an enhanced understanding of whether the findings can be generalized. Therefore, this study investigated the effects of immediate feedback timing in online language learning exercises. The following three conditions were examined: no feedback, end-of-question feedback, and end-of-assignment feedback. A planned contrast test revealed that with a pretest as the covariate, students in the end-of-question feedback condition received significantly higher grades in the posttest compared with those in the end-of-assignment feedback condition. Furthermore, students with lower pretest scores required more attempts, although their learning progress was not significantly superior to that of students with higher prior knowledge. This study's findings provide insights into the use of immediate feedback for improving learning as part of foreign language classroom instruction.

Despite several benefits attributed to the use of MOOCs for learning, acceptance levels remain low and studies investigating adoption and use of the technology are limited. This study draws from the literature on specific factors that influence online learning environments and extends the technology acceptance model (TAM) to understand if those factors can be used to explain and facilitate MOOCs acceptance and use behaviors among learners. The factors investigated include, course content quality, course relevance, course instructor quality, course design quality, learner-instructor interaction, and learner-interaction. There were 138 participants in the study. Partial Least Squares SEM (PLS-SEM) was used to analyze the relationships proposed for the study. The results suggest that the extended TAM offered a good explanation of MOOC acceptance and use, with course relevance and course content quality as external variables affecting MOOCs acceptance. Findings from this study provides practical implications for MOOCs implementation to increase acceptance and further lays a foundation for future research

Although scholars have explored the drivers of students’ e-learning intention, actual responses to online classes and the impact of online classes on student performance and the satisfaction-performance link remain under-explored. Thus, this study aimed to investigate higher education students' attendance, performance, and satisfaction with online classes. We collected data from two sources: (i) a cross-sectional survey of 214 undergraduate students in two phases and (ii) the administrative office. Data were analyzed using partial least square structural equation modeling via the Smart PLS (4.0.8.9). The results showed that students’ response to online classes is influenced by their intentions and other external antecedents, which in turn determine satisfaction. However, no statistical evidence was found for the relationship between student satisfaction and performance. The findings of this study contribute to the extension of theoretical models, such as the theory of planned behavior, the theory of reasoned actions, and the technology acceptance model used in the domain of online learning. The study findings have significant practical implications for the acceptance and use of e-learning.

This is a study of two populations of learners/teachers: Pre-service teacher students (i.e., formal education for teaching certification) and In-service teachers (i.e., engaged in professional development), and involves an examination of their use of internet-based instructional applications. In these studies, the technologies are: a) Online Learning [OL]; b) Blended Learning [BL] and c) Flipped Classrooms [FC]. Treatments were compared to standard face-to-face classroom instruction (CI) on three dependent measures analysed separately to produce 77 achievement measures in the first meta-analysis, 21 attitude/satisfaction measures in the second, and 22 studies of self-efficacy in the third. Achievement data yielded a statistically significant, moderate effect size (i.e., g⁺= 0.44) in favor of the combined OL, BL and FC approaches versus CI, although OL showed only a small positive effect versus CI. Attitude yielded a non-significant effect size comparing CI with technology-supported strategies (g⁺= 0.12). Interestingly, self-efficacy produced a significant and moderate average random effect size of g⁺ = 0.45. Perhaps the most impactful finding of this analysis is related to improved performance of BL/FC when contrasted with OL, yielding significantly positive differences in all three measures. This confirms that improved outcomes are the result of better pedagogy, not the mere presence of technology. Finally, self-efficacy was found to be meaningfully improved when using BL/FC. This is the first meta-analysis with this target population implicating self-efficacy. These two outcomes offer important implications for institutions regarding the future design of instructional delivery for both pre- and in-service teachers.

The central role of technologies like social media and smartphones in children's lives means that learning to navigate the digital world is now a core developmental task for elementary school students. While access to the Internet affords children the potential to find connections at scale, it can also expose them to potential threats like privacy violations and online aggression. In this study, we report on the results of an educational intervention that aimed to improve students’ ability to respond adaptively to these challenges - a concept we discuss as digital resilience. The intervention was co-designed with the non-profit organization My Digital Tat2 and administered to 566 elementary school students. Results showed that the intervention improved students’ digital skills, self-efficacy with technology, intentions of being an upstander to support peers online, and willingness to seek help for difficult situations from trusted adults. Compared to their baseline, students were not only better prepared to evaluate digital information and stand up for themselves online, but also more willing to give and receive support for digital challenges. Our results speak to the potential for research-practice partnerships to create developmentally appropriate interventions that equip students with the skills, self-efficacy, and support structures needed to harness the benefits of technologies, while being informed and resilient to its dangers.

The COVID-19 pandemic forced teachers to suddenly change their teaching mode from face-to-face to emergency remote teaching creating the biggest disruption in the history of education. Despite facing significant challenges such as a lack of proper information technology training, teaching unprepared students, and infrastructural barriers, there is a gap in research on primary school teachers’ readiness to integrate technology. This study aims to address this gap by utilizing the technology acceptance model to explore the relationships between primary school teachers’ computer self-efficacy beliefs, attitudes, and intentions to incorporate computer-based technology during times of disruption. Participants were N = 144 Greek, primary school teachers, who taught in virtual settings during the pandemic. Findings from structural equation modeling showed that self-efficacy beliefs, perceived ease of use, and perceived usefulness are strong predictors of teachers’ attitudes towards computer use. The study adds to the scarce literature on the crucial role of teachers’ self-efficacy beliefs as drivers of their intention to use technology during emergency remote teaching. Additionally, it highlights the need to consider the unique circumstances in which technology is used and provides insights that can inform the design of effective interventions and policies.

Experiments have failed to support the matching hypothesis that students’ learning style preferences should be matched to instructional modality to optimize learning. These studies have generally been restricted to considering sensory modality learning style dimensions. We extend this extant work by examining the matching hypothesis with regard to another learning styles model, one that distinguishes between active/reflective learning preferences (Felder & Silverman, 1988). Participants preferring each learning style were assigned to one of two versions of a digital-based Biology lesson (textbook chapter). The interactive version contained interactive exercises consisting of text and visuals. In the non-interactive version, the interactivity was removed, and participants watched while the identical exercises were completed by the computer. We assumed that an active learning style would align with the interactive lesson, whereas a reflective learning style would better align with the non-interactive lesson. For two learning tests (definition recall and multiple-choice questions) the nature of the lesson (interactive vs. non-interactive) did not interact with participants’ learning style. For a test that targeted the content for which the interactive exercises were designed, learning performance was better when the lesson format mismatched the preferred learning style. The results importantly enrich the experimental evidence countering the matching hypothesis. The current finding is particularly strong because it does not rely on a null effect. Further, the current findings significantly extend the experimental literature from a focus on the modality-specific matching hypothesis to a broader consideration of learning-styles that includes an activity-based (active/reflective) learning style model.