Computers & Education最新文献

In the AI era, it has become crucial to evaluate information found on the Internet critically. This research aimed to investigate the impact of a sourcing organizer on sixth graders' online multimodal and multiple document reading (MMDR) abilities, focusing on aspects such as source-content link and text integration in relation to reading on the Internet. Cognitive and affective factors associated with MMDR were examined. The study involved 52 sixth-graders (55.77% males) from two typical elementary school classes in the northern region of Taiwan. Two intact classes were randomly assigned to either the experimental or control group with the quasi-experimental design. The experimental group (n = 26) received a pre-outlined sourcing organizer, guiding them to record the article title, author, publication date, website name, and major assertions from six assigned multimodal texts. In contrast, the control group (n = 26) received a regular organizer, prompting them to summarize the main ideas from the same six assigned multimodal texts. The study's findings indicated that employing sourcing organizers positively impacted students' performance in text integration. However, it was observed that both groups, regardless of whether they used regular organizers or sourcing organizers, experienced benefits in terms of source-content links. Furthermore, reading ability emerged as the sole significant predictor for source-content links, whereas both reading ability and the use of sourcing organizers predicted text integration. The implications of these findings were discussed to provide insights into instructional strategies to develop online MMDR competencies in elementary students.

The increasing digitization of society has led to the growing immersion of children in virtual culture, raising concerns about how screen use affects their educational and personal development. Through a Systematic Literature Review (SLR) and Textual Data Mining (TDM), this study explores the profile of digital technology use, behaviour, and effects on childhood. We identify three distinct periods in the study of these effects: pre-2016, focused on addiction and health; 2016–2020, focused on educational applications and the role of families; and post-2020, with an emphasis on negative repercussions for identity construction, health, well-being, and behaviour. Our findings reveal a growing concern about the cognitive and emotional impact of screen use, underscoring the importance of teacher training and digital literacy for children and parents for appropriate and healthy use of technology. In contrast to what has been previously known, this study highlights the dynamic change in issues of concern about childhood screen use over time and emphasizes the need for educational strategies adapted to emerging digital cultural realities. This comprehensive approach offers a deeper insight into the dual role of technology as both an educational tool and a source of risk, providing a foundation for the development of informed and responsible educational practices in the digital age.

This study explores the pedagogical use of adaptive learning to foster learner-content interaction, and its subsequent impact on student performance in a large first year Macroeconomics course in an Irish University. To increase student-content interaction, an adaptive learning tool (LearnSmart) is employed to create five weekly adaptive reading assignments, over a six-week period. We include two separate indicators of learner-content interaction (the amount of time spent and number of completed adaptive assignments) in our analysis. Results indicate that student performance is enhanced by the number of completed adaptive assignments, which captures learner-content interaction and adaptive learning. However, the amount of time spent studying was negatively associated with performance. This surprising result comes as a novel contribution, which extends our knowledge of how adaptive learning technology can be potentially used to drive learner-content interaction and student performance. Pedagogically, our findings suggest the need for more intentional design and personalisation of learning activities to enhance student-content interaction and subsequent performance.

The emphasis of using computing tools in scientific practice has called for new forms of scientific modeling. Therefore, researchers are paying increasing attention to computational scientific modeling in which students use the computational power of computers to model and learn about science phenomena. However, computational scientific modeling is challenging since it involves not only scientific concepts but also uses computational representations to model the science concepts. This study hypothesizes that prompting students to self-explain critical subgoals of computational scientific modeling before taking part in the construction of models would help them construct correct computational models. This study recruited 65 10th grade students in a 6-week program. They were randomly assigned to a self-explanation group (n = 29) where students learned with the self-explanation for subgoals of computational scientific modeling, and a demonstration group (n = 36) where the teacher directly demonstrated the modeling process before students took part in the modeling activity. This study collected the students' performance in a paper-based and hands-on modeling test, and also their modeling actions in the hands-on test to understand the impact of the self-explanation scaffolding on their computational scientific modeling. The results showed that the two groups demonstrated similar levels of improvements in the paper-based test, suggesting that both types of scaffolding are helpful for computational scientific modeling learning. However, the self-explanation group demonstrated significantly better modeling quality in the hands-on test. Furthermore, the spotlight analysis found the moderation effect of the modeling actions on the relation between the two treatments and the model quality. The self-explanation group constructed high quality models if they took only a low number of modeling actions. Conversely, frequent modeling actions are necessary for the demonstration group to construct quality scientific models. The results suggest that the self-explanation scaffolding is more effective since students did not simply rely on the trial-and-error strategy, but adopted a strategic approach to constructing scientific models.

As an interactive simulation technology with multi-source information fusion, virtual technology can be used to create learning scenarios, enhance the learning experience, and promote teaching innovation. However, there is no unanimous answer whether virtual technology can effectively improve students' creativity. Therefore, this study adopted a meta-analysis method to analyze 62 experimental (quasi) studies published in international journals between 2014 and 2023 to examine the Impact of Virtual Technology-Based Teaching on Student Creativity. The results found that: 1) Virtual technology has a moderately positive impact on students' creativity (the combined effect size is 0.596), and there is no significant difference in innovative thinking, innovative practical ability, innovative personality quality, and other aspects; 2) Compared with other learning levels, teaching based on virtual technology had the best effect on the creativity of school-age children; 3) Virtual technology could stimulate students' creativity more when applied to the teaching of declarative knowledge than the teaching of procedural knowledge; 4) The use of virtual technology combined with inquiry-discovery and task-driven methods had a more positive impact on creativity; 5) Compared with observational learning and social learning, operational learning had a more improving effect on students' creativity; 6) The use of detailed explanations and true-false feedback in teaching based on virtual technology was more optimistic for the development of students' creativity; 7) Distributed virtual reality and augmented reality had a higher degree of impact on student creativity than other virtual technologies. These analyses provide a reference for future research and practice of using virtual technology to enhance students' creativity.

This systematic literature review provides an overview of quantitative research conducted between 2016 and 2021 on the digital competence of in-service school teachers. The main objective is to examine how the digital competence of in-service teachers is defined and measured, along with the explanatory factors influencing its development. The review focuses on databases such as Web of Science, Scopus, and the Education Resources Information Center. Through a rigorous selection process involving search terms, inclusion and exclusion criteria, and an assessment of article quality, 44 articles were chosen from an initial set of 1845. Findings reveal that the assessment of in-service teacher digital competence typically encompasses five dimensions: (1) planning and preparation of digital-based lessons, (2) use of digital technologies to support teaching practices, (3) implementation of classroom activities incorporating student use of digital technologies, (4) utilization of digital technologies to foster 21st-century skills in students, and (5) emphasis on student digital skills. Furthermore, studies in the field tend to prioritize the measurement of in-service teacher abilities to use digital technologies for instructional planning and as a teaching resource. There is less emphasis on measuring their competence in guiding student-centered activities or developing students’ skills. The primary factors explaining digital competence include sociodemographic characteristics, technology use, teaching practice, experience, and attitudes towards ICT. In conclusion, the results highlight the need for a greater consensus and specification regarding the operationalization of in-service teacher digital competences, as well as the improvement of measurement instruments.

In the artificial intelligence age, cultivating young children's computational thinking (CT) has sparked tremendous attention. Programmable robotics is a developmental-appropriate and screen-free means that provides young children with great opportunities to learn programming and develop CT. However, it is reported that young children might have difficulties learning abstract CT concepts. As a helpful pedagogical facilitator, metaphors can help turn abstract concepts into more concrete and clear concepts that learners are familiar with. Therefore, this research proposed a metaphor-based robot programming (MRP) approach and explored its impact on young children's CT and behavioral patterns. A total of 118 children aged 5–6 were recruited in this experiment with two conditions: the experimental group adopted the metaphor-based robot programming (MRP) approach while the control group used the conventional robot programming (CRP) approach. Results revealed that children who adopted the MRP approach outperformed children who adopted the CRP approach on CT. In addition, behavioral analysis indicated that the proposed MRP approach could facilitate children's superior learning performance and more positive learning behaviors, so as to help them achieve learning objectives. Accordingly, this study can provide insightful guidance and inspiration for future research on effective programming teaching and CT development for young children.

Students in higher education who actively manage and control their own learning processes have better learning outcomes, more efficient learning progress, and other non-academic benefits than those who do not. However, students often have poor self-regulation practices, lack reflective thinking, and fail to monitor their learning against defined goals. Therefore, a variety of advanced learning technologies and features have been developed to support students with regulation. Nevertheless, the optimal level of regulation support and the most effective combination of these features remain unestablished in the existing research literature. Therefore, this study employs quantitative research to examine the effectiveness of two learning environments with different levels of self-regulation support (less and more) on students' learning academic outcomes, learning process, and satisfaction. Both variants combine the elements of learning dashboards, goal-setting activity, self-assessment task, reflection support, and personal recommendation, with differences in the level of supporting students' regulation. Our findings reveal that students who received higher levels of self-regulation support tended to be more active readers and visited online learning materials and course overviews more frequently. While they outperformed students in the control group, it's noteworthy that these students did not necessarily outperform their peers who received less support, particularly in the context of self-assessment tasks. This study has also highlighted the need for further research in this area (e.g., self-assessment accuracy), including the development of an instrument that can objectively measure and evaluate the level of self-regulated learning support.

University students often employ generative artificial intelligence tools such as ChatGPT in resolution of ill-defined problem-solving tasks. However, the experimental evidence about effects of ChatGPT on complex problem-solving performance is still missing. In this preregistered experiment, the impact of ChatGPT on performance in a complex creative problem-solving task was investigated in 77 university students solving a task with ChatGPT in comparison to 68 students solving a task without it. ChatGPT use significantly improved self-efficacy for task resolution (d = 0.65) and enhanced the quality (d = 0.69), elaboration (d = 0.61), and originality (d = 0.55) of solutions. Moreover, participants with ChatGPT assistance perceived task as easier (d = 0.56) and requiring less mental effort (d = 0.58). However, use of ChatGPT did not make task resolution more interesting (d = 0.08), and the impact of ChatGPT on metacognitive monitoring accuracy was unclear. Although there were no significant differences in absolute accuracy between students solving the task with and without the assistance of ChatGPT, the absence of correlation between self-evaluation judgments and performance suggests that participants struggled to calibrate their self-evaluations when using ChatGPT. Notably, the perceived usefulness of ChatGPT appeared to inform self-evaluation judgments, resulting in higher inaccuracy. The implications for hybrid human-AI regulation (HHAIR) theory are discussed. To regulate effectively, students using AI tools should focus on valid metacognitive cues instead of the perceived ease of ChatGPT-assisted problem-solving.