Journal of Computer Assisted Learning最新文献

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.

Background

Video lectures which include the teachers' presence have become increasingly common. As teacher enthusiasm is a nonverbal cue in video lectures, more and more studies are focusing on this topic. However, little research has been carried out on the interactions between teacher enthusiasm and prior knowledge when learning from video lectures.

Objectives

We tested whether prior chemistry knowledge moderated the impact of teacher enthusiasm on students' chemistry learning during video lectures.

Methods

One hundred and forty-two Grade 7 (low-prior chemistry knowledge) and Grade 9 (high-prior chemistry knowledge) Chinese students engaged with this research. Each group of students was randomised into viewing a video lecture with either a low or high degree of teacher enthusiasm. Outcomes were assessed by attention allocation, learning performance, cognitive load, learning satisfaction and student engagement.

Results and Conclusions

Our findings revealed significant benefits of teacher enthusiasm and also showed that prior chemistry knowledge could moderate the impact of teacher enthusiasm on students' attention and cognitive outcomes (cognitive load and transfer). Visual attention mediates the effects on transfer. For students with low prior knowledge, there is more focus on the learning content, lower extraneous cognitive load, and higher transfer scores when watching videos with high levels of teacher enthusiasm; however, students with high prior knowledge do not show these differences. In addition, high prior chemistry knowledge had a significant beneficial impact on the motivational outcomes of the students (satisfaction and engagement).

Implications

The results suggest that teacher enthusiasm in a video lecture may affect students' chemistry learning, and students' prior chemistry knowledge should be considered when choosing whether to display teacher enthusiasm.

Background

Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large language models (LLMs), we expect feedback as part of learning systems to transform, especially for the context of programming. In the past, it was challenging to automate feedback for learners of programming. LLMs may create new possibilities to provide richer, and more individual feedback than ever before.

Objectives

This article aims to generate specific types of feedback for introductory programming tasks using LLMs. We revisit existing feedback taxonomies to capture the specifics of the generated feedback, such as randomness, uncertainty and degrees of variation.

Methods

We iteratively designed prompts for the generation of specific feedback types (as part of existing feedback taxonomies) in response to authentic student programs. We then evaluated the generated output and determined to what extent it reflected certain feedback types.

Results and Conclusion

This study provides a better understanding of different feedback dimensions and characteristics. The results have implications for future feedback research with regard to, for example, feedback effects and learners' informational needs. It further provides a basis for the development of new tools and learning systems for novice programmers including feedback generated by AI.

Background

In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education.

Objectives

The objective of this study is to evaluate if it is feasible for a state-of-the-art LLM to support curricular design by proposing lists of high-quality LOs.

Methods

We propose a simple LLM-powered framework for the automatic generation of LOs. Two human evaluators compare the automatically generated LOs to the human-crafted ones in terms of their alignment with course goals, meeting the SMART criteria, mutual overlap, and appropriateness of ordering.

Results

We found that automatically generated LOs are comparable to LOs authored by instructors in many respects, including being measurable and relevant while exhibiting some limitations (e.g., sometimes not being specific or achievable). LOs were also comparable in their alignment with the high-level course goals. Finally, auto-generated LOs were often deemed to be better organised (order, non-overlap) than the human-authored ones.

Conclusions

Our findings suggest that LLM could support educators in designing their courses by providing reasonable suggestions for LOs.

Background

Implementing maker education in schools is on the rise, fuelled by its potential to move formal education towards a creative, technology-driven 21st century learning culture. In maker education, collaborative learning takes place through and around various digital and traditional technologies, which provide the means for students' creative activities. However, research is scarce on how maker education promotes students' technological competence and how students develop different features of that competence.

Objectives

This study investigates the learning of technological competence in formal education collaborative maker projects focusing on designing and making electronic textiles (e-textiles).

Methods

The data consisted of student teams' discourse from classroom video data, in which five 7th-grade student teams (3–6 students/team, 13- to 14-year-olds) were engaged in designing and making e-textiles. Their discourse was categorised into five technology dimensions—crafting, designing, engineering, programming, and documenting—and epistemic network analysis was used to model the co-occurrences of the dimensions.

Results and Conclusions

All five technology dimensions were present in four of the five teams, but the learning outcomes—that is, the emphasis and connections of the dimensions—varied between the teams. Learning was promoted when technology was used as means for joint construction of knowledge objects but hindered when viewed only as tool for task execution. The results indicate that collaborative maker projects enable the learning of comprehensive technological competence including knowledge and skills related to both digital and traditional technologies. However, implementing a maker project in schools is insufficient to guarantee that all technology dimensions will be addressed.

Background

Social–emotional practices are crucial in today's educational landscape, fostering students' emotional intelligence, resilience and interpersonal skills. Integrating these practices into the gamified flipped classroom approach creates an enriched learning environment. However, there exists a notable research gap regarding the specific implementation and empirical evaluation of social–emotional learning (SEL) strategies within the gamified flipped classroom model specifically tailored for higher education settings.

Objectives

This study examined the effects of integrating SEL practices into a gamified flipped-classroom approach (GFLA) on students' emotional regulation, learning achievement, communication, and collaboration tendencies.

Methods

A mixed-methods quasi-experimental design was employed, involving 36 students enrolled in a study skills course at a university in Vietnam. Participants were assigned to either the SEL-enhanced GFLA (SEL-GFLA) group or the GFLA group. The SEL-GFLA group experienced SEL practices such as meditation breathing, compliment before feedback and optimistic closure.

Results and Conclusions

The findings, analysed using independent t-tests, ANCOVA and thematic analysis, revealed that integrating SEL practices into the gamified flipped classroom approach positively influenced students' emotion regulation, learning achievement, communication tendency, learning competence support and emotional appraisal support. These results have implications for educators and policy makers, highlighting the benefits of incorporating SEL practices into instructional approaches to enhance students' socio-emotional and academic outcomes.

Background

This study examines the evolution of English speaking proficiency test methods, which have traditionally relied on face-to-face interactions to assess communicative language competence. Recently, computer-based language tests have also been used on a larger scale, albeit with concerns about their impact on measurement.

Objectives

This paper investigates the potential of the metaverse in the context of Oral Proficiency Interviews (OPI) for evaluating the speaking proficiency of English learners. Specifically, the research centres on the utilisation of one of the recently emerging metaverse platforms (Zepeto) to conduct OPI tests based on the comparison of the results obtained from the traditional face-to-face tests and an online video conferencing platform (Zoom).

Methods

A total of 60 EFL young pupils participated in three different speaking tasks via the three modes given in a counter-balanced order. The test scores of the three modes were rated and analysed to examine the degree to which different test modes had an effect on them. A questionnaire with 10 items (plus one sub-question) was used to investigate participants' experiences and perceptions of three speaking assessment modes.

Results and Conclusions

The results show that test-takers performed significantly better in the metaverse test mode than in the other two modes, while no significant difference was found between the face-to-face and online video conferencing modes. The sub-scores of speaking test criteria such as task completion, pronunciation, language use and fluency were also analysed for comparison, and similar results were found except for the sub-scores of the language use criteria. Responses to the questionnaire also showed that the metaverse platform was the most helpful and preferred mode.

Implications for Practice

Based on the findings, this study suggests that the new testing modes incorporated with virtual environments have great potential to be alternatives to traditional testing modes.