Computers & Education最新文献

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).

Engaging children in describing real-life scenes provides an effective approach to fostering language production and developing their language skills, enabling them to establish meaningful connections between their language proficiency and authentic contexts. However, for such learning tasks, there has been a lack of research focusing on promoting self-directed language learning by using artificial intelligence techniques, primarily due to the challenges of handling multimodal information involved in such tasks. To address this gap, this study introduced a two-stage automated evaluation method that employed emerging cross-modal matching AI techniques. Firstly, an automated scoring model was developed to evaluate the quality of students' responses to scene description tasks. Compared with manually assigned human scores, our model scored students' descriptions accurately, as evidenced by a small testing mean absolute error of 0.3969 for a total score of 10 points. Based on the scoring results, immediate feedback was then provided to students by generating targeted comments and suggestions. The goal of this feedback was to assist students in progressively improving their descriptions of daily-life scenes, thereby enabling them to practice their language skills independently. To assess the effectiveness of the feedback, a comprehensive investigation was conducted involving 157 students from middle schools in China, and both qualitative and quantitative experimental data were collected from the students. It is found that the quality of students' descriptions was improved significantly with the assistance of immediate feedback. On average, students achieved an increase of 1.48 points in their scores after making revisions based on the feedback. In addition, students reported positive learning experiences and expressed favorable opinions regarding the language learning tasks with the automated evaluation. The findings of this study have significant implications for future research and educational practice. They not only highlighted the potential of emerging cross-modal matching AI techniques in automatically evaluating learning tasks involving multimodal data but also suggested that providing immediate targeted feedback based on automated scoring results can effectively promote students’ self-directed language learning.

Prior research has suggested that participation in peer feedback activities, specifically the giving and receiving of comments, and the implementation of suggested ideas helps to improve writing quality. However, it is not always the case that received feedback is implemented. Currently, there is uncertainty regarding how the total number of comments received during a peer feedback activity interacts with the number of implemented comments and subsequent text quality. Therefore, the present study examined the online peer feedback sessions of 186 graduate students enrolled in a scientific writing course at a Korean university. It aimed to investigate the possible moderating effect of the total number of comments on the relationship between implementation of comments and student writing performance. The results showed that there is no relationship between the number of received comments and student writing performance. Furthermore, no significant correlation was observed between the number of implemented comments and student writing performance. However, importantly, a moderating effect of the total number of comments on the relationship between implementation of comments and student writing performance was found. In other words, the more total comments students receive, the less their implementation will be associated with significant improvement of writing quality. This result is significant because it suggests that in online collaborative learning contexts, there is an upper limit of peer comments, above which the original author can no longer effectively implement them to improve writing performance.

ChatGPT, a state-of-the-art artificial intelligence (AI) chatbot, has gained considerable attention as a transformative yet controversial tool for enhancing teaching and learning experiences. Several reviews and numerous articles have been written about harnessing ChatGPT in education since its release on November 30, 2022. Besides summarising its strengths, weaknesses, opportunities, and threats (SWOT) as identified in previous systematic reviews of ChatGPT research, this systematic review aims to develop a new understanding of its influence on student engagement by synthesising the existing related research using a three-dimensional framework comprising behavioural, emotional, and cognitive aspects. We searched relevant databases and included 72 empirical studies published within one year of ChatGPT's initial release. The findings reveal robust but narrowly focused evidence related to behavioural engagement (i.e., work with ChatGPT) and disengagement (i.e., academic dishonesty). The evidence related to the emotional aspect is mixed, with instances of both engagement (e.g., satisfaction and interest/fun) and disengagement (e.g., disappointment and worry/anxiety). There is broad but weak evidence regarding cognitive engagement (e.g., increased understanding and positive self-perception) and disengagement (e.g., reduced critical thinking and overreliance). Our review uncovers several under-explored indicators of student engagement, pointing to the need for further research. Specifically, future studies could focus on students' study habits and attendance (behavioural engagement), social interaction (emotional engagement), and self-regulation and critical thinking (cognitive engagement) in ChatGPT-supported learning environments.

Blended learning offers a learner-centred approach that employs both in-class learning and digital technology to facilitate online learning. Such an approach is especially advantageous to adult-learners in higher education as it meets their educational needs. However, adult-learners’ participation in blended learning programmes remains challenging due to a general lack of online interaction, and no clear teaching strategies that address this concern. Literature relating to adult-learners’ educational needs and online interaction was consulted in order to design teaching strategies that foster adult-learners’ online interaction. The aim of this study is to further validate these teaching strategies, hence a multiple case study was carried out using a mixed method approach. As such, eight teachers and sixteen students from four courses across three universities in Belgium and the Netherlands were interviewed. Additionally, a questionnaire testing a pre-defined set of variables was distributed to 84 students. The results lead to a set of validated teaching strategies that help teachers to further develop their professional skills and expertise. The teaching strategies can be grouped into three categories, namely 1) the teacher's online presence, 2) collaborative learning activities and preparatory learning activities, and 3) the distribution of learning content and learning activities across online and in-class learning. An elaborate set of validated teaching strategies is included. This study aids towards teacher professional development and adds evidence-based knowledge to teaching strategies and instructional frameworks for adult-learners in higher education.

Teaching/learning strategies, adult-learning, cooperative/collaborative learning, teacher professional development, online learning.

Predicting students' problem-solving success in computer-based simulations at an early stage allows adaptive educational systems to provide learners with personalized support. In this paper, we predict students' problem-solving success by applying a machine-learning model, the random forest, to produce a binary classification (more vs. less successful students). During a business-related problem scenario that lasted 55 min, early behavioral data (during the first 5, 10, and 20 min) such as mouse clicks and keyboard strokes (approx. 29,800 early-window clickstreams and keystrokes during the first 20 min) of 234 trainees were recorded, mirroring the students' problem-solving behavior. We used the n-gram sequence mining technique, which was originally introduced within the emerging disciplines of natural language processing, text mining, and machine learning and has proven to be effective, particularly in the examination of online behavior. We trained the random forest model with training datasets that included all features (bigrams), as well as selected features (the most predictable bigrams explaining inter-group differences). Our results show that early predictions based on the first 10 and 20 min contained sufficient information to accurately predict problem-solving success, while predictions that are too early (based on the first 5 min) do not. As the size of the initial time window expanded, the classification performance improved. Moreover, the selection of the most predictable features improved the models' performance for all three time intervals. The model that was trained with only selected robust features that occurred in the first 20 min achieved the highest ROC AUC score of almost 0.70. This result falls within the range of accuracy scores observed in similar studies. From the instructor's perspective, predictions help in the early identification of weak students and can provide them with personalized learning prompts. For more successful students, tasks can be enriched adaptively.

The integration of technology into learning is a crucial capability for students to address future challenges. However, East Asian teachers exhibit a relatively lower level of support for students’ technology use compared with the OECD average. The present study tries to uncover the foundational factors influencing teacher-supported student use of technology in five East Asian countries (economies) by analyzing Teaching and Learning International Survey (TALIS) 2018 data.

The multilevel structural equation modeling was adopted to analyze the sample data, which combined the teacher-level and school-level factors. Additionally, teacher-level variables are aggregated to the school level to examine contextual effect. Results indicate that teachers' professional collaboration in lessons, team innovativeness, and self-efficacy in student engagement positively and significantly correlated with teacher-supported student use of technology. Notably, an opposing association is observed between teachers’ perceptions of disciplinary climate and technology use in Shanghai and Singapore. However, in comparison to teacher-level variables, school-level and contextual variables, particularly fundamental school features such as material resources, budgets, and number of enrolled students, marginally explain associations. Importantly, these associations demonstrate variations across the five east Asian countries (economies), attributable to the fact that these economies have developed their educational technology policies grounded in technology determinism, emphasizing the necessity of equipping schools with the latest devices. Consequently, this study posits that there is a requisite shift in policy evolution from technology determinism to technology instrumentalism. This entails a pragmatic focus transition from merely distributing devices to the integration of technology within instructional practices.

The use of social media in the classroom has the potential to improve learning outcomes for both lecturers and university students. This paper presents a framework for how social media can be used as a tool in the learning process according to educational objectives. The framework is intended to demonstrate to researchers and lecturers how to activate social media platforms effectively as a learning tool; how several platforms can be employed across subjects; and how to unify social media resources with the physical and virtual learning environment. To support these areas and provide an innovative and sustainable way to transform teaching and learning, a social media (Web 2.0) taxonomy was developed. The purpose of the social media taxonomy is to assist lecturers in planning all the steps of learning, teaching, and assessing a course or topic, whilst considering the learning goals reflected in national and local curriculum standards. The Web 2.0 taxonomy focuses on three dimensions named as Consciousness, Cognition, and Creativity [3C]. Each dimension consists of its categories in the metacognitive domain: Consciousness, viewing, Cognition, posting, interacting, and analysing, and Creativity, evaluating, and curating. The categories are used to hierarchize instructional objectives, ranging from lower-order thinking to higher-order thinking skills. Each level of the taxonomy is built on the previous learning level by moving from the concrete to the abstract and from the simple to the complex. Thus, when students have completed all these levels, they can be considered to have mastered certain learning objectives, equipping them with innovative 21st-century skills.

Rapid development of artificial intelligence applications and widespread digital transformation are driving the need for employees to learn digital skills. The body of research on digital skills that working professionals need to thrive in an uncertain and ever-evolving workforce is fast accumulating. Although there have been literature reviews on the nature and types of such digital skills, an integrative overview of how digital skills are acquired remains lacking. This systematic literature review seeks to close the gap by focusing on digital skilling. A total of 39 journal articles published between January 2010 and June 2022 were identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Based on thematic coding of the articles, eleven digital skilling approaches were identified and conceptually organized into four categories. Findings regarding the contextual factors affecting digital skilling and impacts of digital skilling indicate an emerging framework of digital skilling. Emerging interests and opportunities for future research related to virtual worlds, learning analytics, and blockchain are discussed.

New technologies and subjects are increasingly being added to the agenda of children's education for increasingly younger groups. Such advances are also tied to questions of learning and the educational designs that make this possible. This study follows the evolution of context- and age-appropriate designs during a programming robot project for 1-2-year-olds, a novel technology for children and teachers. The study examines learning design phases featuring instructional, guided play, and free play activities and uses mixed-methods techniques to examine several aspects of children's educational interaction. Results show how all three educational designs offer different potentials for children's learning. Teacher scaffolding supports extended programming actions but limits children's exploratory actions, and guiding play increases time on task. Simultaneously, children's free play increases the frequency of simple programming actions and leads to unexpected learning potentials beyond the guided activities. It is discussed how diverse learning experiences can join in programs balancing instructional needs of novel technologies and subjects with early childhood's exploratory and playful modes of learning.