Computers & Education最新文献

Collective intelligence is a theoretical construct that focuses on results stemming from a workgroup dealing with complex tasks. Our goal was to determine whether a group of pre-service teachers presented with a case of adolescent cyberbullying could improve their capacity for action. The experiment was carried out with 221 pre-service teachers at the University of Zaragoza, Spain, and using Collective Learning. The cyberbullying case we presented could be resolved via a series of questions in successive phases. Results showed an increase in learning throughout all phases, with a remarkable difference between the average score of the first phase and the last one. By the end of the experiment, the answers which had been most highly valued by the total set of participants turned out to be of considerable quality. We discuss the potential of collective intelligence as a tool for innovating and improving teaching-learning processes in university training.

In the past decade, drones have become another cutting-edge technology for educators, especially those in STEM-related domains. Accordingly, there is a significant need to thoroughly examine how drones are integrated into current pedagogical practices. This study scopes the domain of drone-based learning based on a collection of forty-eight articles identified via systematic searches across the Web of Science (WoS) databases. The analytical framework for coding is underpinned by the Substitution-Augmentation-Modification-Redefinition (SAMR) model. The review explored trends, domains and pedagogical activities, research approaches, learners and learning objectives, variables and aspects of interest, and most importantly, the integration levels of drones into current pedagogical practices. The findings highlight that drones are predominantly utilized in short-term, intermittent, and collaborative learning activities, particularly within STEM-related fields. Notably, the analysis reveals a prevalent use of drones to transform learning, mainly at the Modification and Redefinition levels of the SAMR framework. Regarding drone types, off-the-shelf drones are primarily used for applying-oriented learning and are evenly distributed across the Augmentation, Modification, and Redefinition levels. Conversely, custom-built drones are typically utilized for creating-oriented tasks and are most often associated with the highest SAMR level, i.e., Redefinition. Building upon these findings, the present work underscores the importance of addressing the novelty effect associated with drone-based learning, exploring strategies for sustaining student engagement over time, and investigating the cognitive benefits of intermittent drone use in educational settings. The collaborative nature of drone-based activities is also emphasized, calling for more process-oriented research to understand how drones influence collaborative learning.

English as a foreign language (EFL) writing is vital in interpersonal and cross-cultural communication. Writing refers to a complicated, dispersed, dialogic, and mediated process of discovery and creation, generally considered to be one of the most challenging skills for EFL learners. Nowadays, some students struggle to create words that resonate with readers since they lack real-life experience and empathy cultivation. Virtual reality (VR) not only provides realistic experiences but is also known as the “ultimate empathy machine.” Therefore, this study proposed an empathetic VR-based learning (E-VRL) approach to provide learners with access to authentic experiences and further promote learner empathy. To assess the validity of this approach, 63 junior high school students were recruited to participate in a quasi-experiment, and were randomly divided into an experimental group (EG) which used the E-VRL approach and a control group (CG) which used the VR-based learning (VRL) approach. The results showed that the E-VRL approach improved students’ cognitive empathy and dialogic empathy more than the VRL approach. Additionally, it was found that students in the EG outperformed those in the CG in terms of the use of ideas and content, word choice, and voice in their writing. This study proves the efficacy of the presented E-VRL approach, and provides valuable insights for future research on the design of VR-based EFL writing activities and VR environments.

Emotions play an important role in the learning process. With intelligent technology support, identification and intervention of learners’ cognition have made great achievement, but the care of emotion has been in the absence for a long time. In recent years, the use of affective computing technology to solve affective loss in online education has become a key research topic. To date, a growing number of studies have investigated automated emotion recognition (AER) in online environments. However, AER has been mainly studied from the perspective of computer science focusing on technical characteristics of developing AI technology while its pedagogical value and educational application has been overlooked. Therefore, this systematic literature review aimed to bring together educational and technical aspects of AER. Following PRISMA methodology, a comprehensive search of AER research from 2010 to 2024 in three databases (Web of Science, Science Direct and IEEE Xplore) identified 117 studies that met inclusion criteria. The articles were coded for report characteristics, educational characteristics (tech platform, pedagogy, assessment, content), technical characteristics (emotion model, emotion category, emotion measurement channel, database, algorithm model) and outcome characteristics (technical result, educational application). We found that the primary purpose of these studies was to develop and evaluate systems for AER, rather than implementing these systems in real online learning environments. Furthermore, our findings indicated a lack of integration between computer science and educational science in the realm of AER. Despite the fact that most algorithm models demonstrated high accuracy in AER, the interpretability of the results was significantly constrained by the quality of the databases used, along with the scarcity of studies focusing on the effective and real-time application of AER results. These findings provide essential guidance for shaping future research and development pathways in this field.

Generative artificial intelligence (GAI) and automatic speech recognition (ASR) have ushered in promising tools for foreign language learning, notably GAI chatbots. This study investigated the impact of GAI chatbots on elementary school English as a foreign language (EFL) learners' speaking skills, focusing on two interaction configurations—individual and paired. Eighty-five elementary school EFL learners participated in a three-week summer program, engaging in daily 45-min interactions with CoolE Bot. The participants were randomly assigned to three groups: (1) individual interaction with CoolE Bot (I-Bot group), (2) paired interaction with CoolE Bot (P-Bot group), and (3) interaction with teachers and peers in a conventional English classroom (No-Bot group). In each class, participants in the Bot group received worksheets with a topic, prompts, and vocabulary to guide their interactions with CoolE Bot, while those in the No-Bot group also received worksheets for comparable activities. Quantitative (English-speaking tests) and qualitative data (semi-structured interviews) were collected and analyzed. Results revealed that the I-Bot and P-Bot groups' post-test speaking skills were significantly higher than those of the No-Bot group. CoolE Bot significantly improved the speaking skills of EFL learners. Both individual and paired interactions with CoolE Bot demonstrated positive effects, with no significant differences between groups. Interviews highlight CoolE Bot's adeptness in coherent interaction, charismatic conversational style with a human-like voice, diverse topic discussions tailored to learners' interests, and supportive functions. The participants found GAI chatbot-assisted EFL speaking enjoyable, motivating, and engaging appreciating its cartoonish, human-like characters, conversational style, and voice. Additionally, CoolE Bot fostered rapport and a supportive environment enhancing learners' confidence and reducing anxiety regarding EFL speaking. Individual interactions encourage personalized engagement and self-directed learning, whereas paired interactions involve social dynamics, shared learning experiences, and mutual resolution of language challenges.

Co-viewing video lectures, where students watch lectures simultaneously with one or more remote peers and engage in interpersonal communication on learning tasks online, is becoming increasingly popular. However, there is a dearth of studies examining students' behavioral and cognitive patterns during the co-viewing process. This study employed eye-tracking and screen recording methods to examine undergraduate students' learning processes in co-viewing video lectures. It specifically focused on how students used video lectures and engaged in discussions to complete a design task. Lag sequential analysis of learning behaviors revealed that, compared to low-performers, high-performers were more engaged in behavioral sequences involving clarifying learning tasks. Furthermore, epistemic network analysis of conversations revealed that high-performers demonstrated stronger connections between deep-level knowledge and a high level of uptake in design skills. In contrast, low-performers exhibited more connections between superficial-level knowledge and a low level of uptake in these skills. The findings suggest that in co-viewing video lectures, it's crucial to engage students in clarifying behavioral sequences. Furthermore, the co-occurrence of deep-level knowledge, design skills, and a high level of uptake in shared ideas significantly enhances learning. Our research on learning processes suggests important strategies for students during co-viewing of video lectures: teachers are encouraged to design effective interactive activities and create suitable support interventions to enhance student learning performance in video lectures.

Adaptive teaching is regarded to address students' heterogeneity in schools and to individually support their learning. Technology may help to teach adaptively. However, it is still unclear whether realizing adaptive teaching with technology is a feasible teaching practice in real-world classrooms. More importantly, it is an open question which boundary conditions constrain the feasibility of adaptive teaching with technology. We realized a four-year co-design-project in which researchers and teachers co-constructively developed adaptive teaching units (duration: 3–4 weeks) by deliberately integrating technology across secondary education. To assess the feasibility of technology-enhanced adaptive teaching, we followed a sequential-explanatory mixed-methods approach during the co-design-process. We combined a quantitative study (N = 183), investigating students' learning gains and potential moderators, and a qualitative study by means of semi-structured teacher interviews (N = 3) on implementation conditions of the adaptive teaching units. We observed that the teaching units were particularly beneficial for students with low prior knowledge and when they were highly adaptive to students’ needs. Fine-grained qualitative findings indicated that formative assessments and consolidation phases were crucial for the feasibility of technology-enhanced adaptive teaching, as well as a parsimonious use of technologies. These findings indicate that the feasibility of adaptive teaching with technology depends on boundary conditions, highlighting the need for further support to unfold the potential of adaptive teaching.

In the Massive Online Open Course (MOOC) forum, learning engagement encompasses three fundamental dimensions—cognitive, emotional, and behavioral engagement—that intricately interact to jointly influence students' learning achievements. However, the interplay between multiple engagement dimensions and their correlations with learning achievement remain understudied, particularly across different academic disciplines. This study adopts an automated configurational approach that integrates bidirectional encoder representation from transformers (BERT) and fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of learning engagement, their connections with learning achievement, and variations across disciplines. Our analysis reveals a nuanced profile of learners' learning engagement, indicating the high-achieving individuals demonstrated more frequent posting and commenting behaviors and the high-level cognitive engagement than low-achieving individuals. Second, our analysis revealed multiple configurations where the coexistence or absence of factors at different levels of the cognitive, behavioral, and emotional dimensions significantly impacted learning achievement. Learners who conducted posting and replying behaviors, expressed positive emotions, and engaged in deep cognitive engagement tended to achieve superior learning outcomes. Third, there were significant differences in behavioral and emotional engagement among learners across different academic disciplines. Specifically, pure discipline learners were more inclined to engage in posting behaviors than the applied discipline learners. Across academic disciplines, positive emotions correlated strongly with higher achievement. These findings deepen our understanding of the multifaceted characteristics of learning engagement in MOOCs and highlight the importance of disciplinary distinctions, providing a foundation for educators and designers to optimize learners’ MOOC effects and tailor learning experiences in diverse disciplinary contexts.

With the increasing prevalence of digital devices such as smartphones, tablets and e-readers, more and more reading is happening in digital formats – also in classrooms across the world. The present study focuses on lower secondary school students and their reading comprehension and attitudes toward reading on paper and screens. The study uses an innovative methodological approach were eye-tracking technology is used to observe ten carefully sampled eight grade students with different reading levels. The students read a selection of texts and answered questions from the Norwegian national reading assessment on comparable versions on paper and on screen. By analyzing eye movement data (reading transitions), including more than 25000 fixations, in combination with text reading comprehension outcomes, students' cued retrospective reporting from reading, and interview data we have obtained detailed and comprehensive data on students' reading comprehension, reading behavior, and reading experiences across different media. A key result is that reading on screen leads to more shallow processing and can hinder reading comprehension. Importantly, our results from the students' cued retrospective reporting of their eye tracking, showed that they were unaware of their reading behavior and didn't reflect much on reading in different media. These findings have implications for the increasing shift to digital learning environments in the educational context. It is important to recognize the difference between reading processes, and policymakers and practitioners cannot assume that these processes are the same across individuals and different delivery modes.

The recent Covid-19 pandemic made universities rethink their traditional educational models, shifting, in some cases, to pure online or hybrid models. Hybrid settings usually involve onsite (i.e., in the classroom) and online (e.g., in a different classroom, at home) students simultaneously under the instruction of the same teacher. However, while these models provide more flexibility to students, hybridity poses additional challenges for the specific case of collaborative learning, likely increasing the teachers' orchestration load and potentially hampering fruitful interactions among learners. In order to gather empirical evidence on the impact of hybridity in collaborative learning, this paper reports a study conducted in a hybrid classroom where a Jigsaw collaborative pattern was implemented with the Engageli software. The study involved 2 teachers and 67 students enrolled in a computer science undergraduate course. Teachers' post-interviews, questionnaires and an epistemic network analysis (ENA) were used to produce study findings. Results show that teachers reported a medium-to-high orchestration load for implementing and setting up the collaborative activities in the hybrid classroom. Among the factors that contributed most to such load, teachers highlighted the creation and live management of groups and collaborative documents. Additionally, the ENA showed that teachers put much effort on monitoring group interactions and solving technical issues. Finally, we observed relevant differences on students' perceptions (e.g., satisfaction with the attention received by the teachers) based on the cohort sizes and on the students’ attendance modality (onsite vs. online).