Journal of Computer Assisted Learning最新文献

Background

There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle.

Objectives

Guided by community of practice, this paper examines the integration of GenAI into an online professional learning community (OPLC) to facilitate knowledge co-construction among GenAI, novice teachers and experienced teachers.

Methods

We used a mixed-methods approach that included topic modelling and sentiment analysis on the quantitative side and content analysis for the qualitative data.

Results

We identified the top three latent themes in the OPLC's discourse—(1) generating instructional material, (2) assessment, and (3) pedagogy—and six distinct teacher-GenAI interaction profiles. For novice teachers, these included ‘engaged AI explorers’, ‘selective satisfiers’ and ‘silent strategists’; and among experienced teachers, we discerned ‘careful critics’, ‘reflective realists’ and ‘cautious contemplators’. Novice teachers exhibited technological adaptivity, while experienced ones engaged reflectively with content and focused more on students, and GenAI proved effective at providing instructional materials.

Conclusions

The findings demonstrate how GenAI can contribute to knowledge co-construction, as a facilitator of rather than a replacement for human interaction.

Background

Online videos featuring human-generated drawing are increasingly popular in education. However, their effectiveness may vary depending on learners' prior knowledge, and further research is needed to confirm their advantages over other common instructional videos.

Objectives

The primary goal of this study is to investigate the impact of instructional video formats on learners' outcomes and experiences, and to further explore the interaction between video formats and prior knowledge.

Methods

In a 2 × 3 between-subjects design, 144 college students were divided into two groups based on their (high vs. low) prior knowledge levels and then randomly assigned to different video format conditions. Students listened to oral explanations from the instructor while viewing either dynamic instructor hand-drawn diagrams (instructor-generated visuals), dynamic PowerPoint diagrams (PPT visuals), or static diagrams identical to the PPT visuals (static visuals). Finally, we measured learners' outcomes (i.e., retention and transfer scores) and experiences (i.e., emotion and cognitive load).

Results

Viewing dynamic visuals have a significant facilitating effect on learning compared to static visuals. Moreover, the expertise reversal effect was confirmed. Specifically, for learners with low prior knowledge, the retention test scores and learning experiences of students viewing instructor-generated dynamic visuals outperformed those viewing dynamic PPT visuals and static visuals. For learners with high levels of prior knowledge, no significant differences are observed between the groups. Finally, we found that students' emotional experiences significantly mediated the relationship between instructor-generated visuals and learning outcomes.

Conclusions

These results indicate that the choice of instructional videos should align with learners' prior knowledge and emphasises the importance of learning experiences.

Background

Student engagement has been conceptualised and operationalised in various learning environments. However, there is currently a lack of established scales to measure student engagement in synchronous online learning. One possible reason is the existence of the conceptual and structural ambiguity regarding student engagement.

Objective

With its context situated in Saudi Arabian students' synchronous online learning of Chinese as a foreign language (L2 Chinese), this study attempts to find out the best representation and measure of synchronous online engagement based on four competing statistical modelling methods, including confirmatory factor analysis (CFA), exploratory structural equation modelling (ESEM), bi-factor CFA (B-CFA) and bi-factor ESEM (B-ESEM).

Methods

A total of 167 Saudi Arabian students voluntarily participated in this online questionnaire-based study. A 19-item online questionnaire was adaptively developed to measure students' engagement in synchronous online learning of L2 Chinese from behavioural, cognitive, affective and social dimensions. CFA, ESEM, B-CFA and B-ESEM were employed to determine the optimal structure model for representing students' synchronous online engagement in L2 Chinese learning.

Results and Conclusion

The results revealed that the B-ESEM model was the best structure model for both measuring and accounting for the generality and specificity of students' engagement in synchronous online learning of L2 Chinese. More specifically, student online engagement was found to be a general unity with four distinctive subdimensions including behavioural, cognitive, affective and social engagement. This study not only reconciles the construct inconsistency of student engagement in the literature from the synchronous online learning perspective but also provides an optimal representation for measuring students' synchronous online engagement in L2 learning.

Background

The rise of intelligent chatbots powered by artificial intelligence and machine learning has ignited interest in their potential for revolutionising second language (L2) acquisition and foreign language learning (FLL). While their potential seems vast, understanding their actual impact on learning outcomes requires comprehensive analysis. Also required is the continuous evaluation of this technology due to the rapid pace of advancements in chatbots, highlighting the need for a fresh look using the latest research. This systematic review aims to address this need by investigating the effectiveness of modern chatbots in L2 learning and FLL, offering valuable insights for language educators and researchers.

Objectives

This study systematically reviews existing research to assess the usefulness of chatbots in L2 acquisition and FLL, identifying key areas of improvement and future research directions.

Methods

Following the PRISMA methodology, 33 relevant studies published between 2020 and May 2024 were analysed. Thematic analysis was employed to identify key themes related to chatbot applications, language skills impacted, and learning outcomes achieved.

Results

The findings clearly reveal specific language skills (i.e., speaking) and language knowledge (i.e., vocabulary) that have a significant impact on enhancing students' learning outcomes by using chatbots, while pointing out insufficient research in this area. The results also show major benefits of chatbots for FLL, such as individualised learning, immediate feedback, enhancing willingness to communicate, and increased interaction.

Conclusions

This systematic review highlights specific practical implications and suggestions that have a direct impact on the usefulness of chatbots in FLL and L2 acquisition, and a need to investigate further variables such as age, language skills, personality traits, or teacher's role with respect to the use of chatbots in foreign language classes. Further implications are also raised in terms of learning design considerations when such a technology is incorporated in the FLL classroom.

Background

Video conferencing technology has moved online education into a new stage of real-time video interaction. However, shortcomings such as students' lack of concentration and substantive engagement during video conferencing greatly limit the improvement of online learning effectiveness. According to social presence theory and the community of inquiry model, the on-camera presence of instructors and students may effectively enhance the learning outcomes in video conferencing.

Objective

The effects of instructors' and students' on-camera presence on social presence, learning satisfaction and learning performance in video conferencing were investigated.

Methods

We selected 244 university students as participants and employed a between-subject experimental design with two levels of instructors' camera (on/off) and two levels of students' camera (on/off).

Results and Conclusions

(1) Learning performance was better when students turned on their cameras compared to when they did not. (2) The level of social presence was higher when students turned on their cameras than when they did not. The interaction effect between instructors' and students' camera use was significant, indicating that when instructors turned off their cameras, students' social presence was higher when they themselves turned on their cameras compared to when they did not. However, when instructors turned on their cameras, students' use of cameras did not significantly impact social presence. (3) Learning satisfaction was higher under the condition where students turned on their cameras compared to when they turned them off. It is recommended that students appear on camera during video conferencing, as this can increase social presence and learning satisfaction, ultimately improving their learning performance.

Background

In recent years, computer science education has emerged as a necessary part of school curricula for students of all ages. With such momentum in this direction, it is essential that program designers, educators, and researchers ensure that computer science education is designed to be inclusive, effective, and engaging for all students.

Objective

Accordingly, this paper reports on the design and implementation of an inclusive digital learning platform and accompanying curriculum for scaffolding and integrating coding into writing instruction for elementary-aged students (approximately ages 9–12). In this paper, we report on teachers' uses of the Compose and Code (CoCo) platform and curriculum, how students used its features, and its influence on students' computational thinking skills and attitudes about coding.

Method

Data analysed in this mixed-methods study come from 11 teachers and 595 students in Grades 3–6. Data sources included teacher reflections and interviews, an assessment of computational thinking for students, and a coding attitudes survey for students. Quantitative data were analysed descriptively and using paired sample t-tests. Qualitative data were analysed inductively using open coding to determine emergent categories.

Results and Conclusion

Findings indicate that (1) a majority of students effectively used the CoCo platform to plan their work and code in Scratch, with a smaller percentage using the self-evaluation and self-monitoring features, (2) teachers indicated overall positive perceptions of the CoCo platform and curriculum, with strong support for using it in the future, (3) students' computational thinking skills improved over the course of the project, with results indicating a large effect size (g = 1.24), and (4) student attitudinal results were mixed, providing insights to the barriers that students face when learning to code. Overall, this study indicates that the CoCo platform and curriculum show promise as a scaffolded, structured, and integrated tool for teaching elementary computer science to elementary grade students.

Background

Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology.

Objective

This systematic review study aims to synthesise the relevant work to reflect the current state of eye tracking use in mobile-assisted learning research over the past 20 years.

Method

Fifty-two articles were identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis approach, and analysed based on the technology-based learning model in five key dimensions: publication information, research domains, technologies, methods, and eye tracking-related subdimensions.

Results and Discussion

The findings indicate a growth in eye tracking studies from 2004 to 2023, with visual and cognitive science and educational technology being the most studied domains. Tablets were the most utilised medium, along with a usage shift towards tracking glasses. Experiment durations, participant numbers, and education levels varied across studies, but reading tasks were common. Questionnaires and tests were the main additional data collection methods, and fixation was the most frequently used category of eye tracking measures. Most studies provided no information on their defined interest areas, from which they extracted eye tracking data, and employed parametric or non-parametric procedures to compare group means for data analysis. The studies primarily explored task performance, learning strategies, and learning tools, while multimedia learning, linguistic processing, and emotion received limited attention.

Implications

The systematic review highlights research limitations of current practices for attention and suggests potential avenues for future eye tracking efforts in mobile-assisted learning.

Background

ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need for deeper insights into the factors influencing adoption.

Objectives

This study aims to investigate higher education learners' adoption intentions towards AIGC technology, with a focus on understanding the underlying reasons and future prospects for its application in education.

Methods

The research is divided into two phases. First, an exploratory analysis involving practical activities and interviews develops an action decision framework for AIGC adoption. Second, a confirmatory analysis using fuzzy-set qualitative comparative analysis on 233 valid questionnaires identifies six configurations associated with high adoption intentions, emphasising the roles of AI literacy and perceived behavioural control.

Results and Conclusions

The study reveals key factors influencing AIGC adoption, including the importance of AI literacy and perceived behavioural control. It provides actionable insights for educators and learners to prepare for and effectively integrate AIGC technology, ensuring equitable and adaptive educational practices.

Background

Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks enhanced preservice teachers' diagnostic reasoning in a digital case-based simulation. However, the effectiveness of the simulation with the different feedback types and the generalizability to field settings remained unclear.

Objectives

We tested the generalizability of the previous findings and the effectiveness of a single simulation session with either feedback type in an experimental field study.

Methods

In regular online courses, 332 preservice teachers at five German universities participated in one of three randomly assigned groups: (1) a simulation group with NLP-based adaptive feedback, (2) a simulation group with static feedback and (3) a no-simulation control group. We analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality.

Results and Conclusions

Compared with static feedback, adaptive feedback significantly enhanced justification quality but not judgement accuracy. Only the simulation with adaptive feedback significantly benefited learners' justification quality over the no-simulation control group, while no significant differences in judgement accuracy were found.

Our field experiment replicated the findings of the laboratory study. Only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy. Under field conditions, learners require adaptive support in simulations and can benefit from NLP-based adaptive feedback using artificial neural networks.