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.
We tested whether prior chemistry knowledge moderated the impact of teacher enthusiasm on students' chemistry learning during video lectures.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
Our findings suggest that LLM could support educators in designing their courses by providing reasonable suggestions for LOs.
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.
This study investigates the learning of technological competence in formal education collaborative maker projects focusing on designing and making electronic textiles (e-textiles).
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
Vocabulary learning is an essential dimension of foreign language learning. The learners have a huge responsibility in this task. Among other factors, time constraints and an overall lack of maintained motivation on the part of students makes it especially challenging. Consequently, improving vocabulary learning requires strategies to motivate learners and encourage autonomous learning. Technology-Assisted Vocabulary Learning (tavl) aims to provide solutions to this set of problems.
We aim to provide an overview of the different types of TAVL tools developed and studied since 2015 with regard to (1) tool features, (2) observed variables and research methods used in the studies and (3) impact on learners' vocabulary acquisition, motivation and engagement.
We conducted a systematic review of the literature between 2015 and 2023 on TAVL. After delineating our field of study and research query, we followed a rigorous methodology to extract relevant papers to answer our research questions. A total of 44 papers of different languages and academic levels were selected for this review.
We report a wide variety of features provided by TAVL tools in various contexts. We observe an overall positive influence of TAVL tools on learners' vocabulary learning, motivation, flow and engagement. We also discuss potential biases in the studies, which do not allow generalisation of the results. We conclude with recommendations for developers and researchers, including the use of more standardised experimental protocols with comparable measures, the integration of collaborative and social features, and a more central role for teachers and academic learning contexts.
Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some instructors from creating additional problems or lead to inadequate test coverage, potentially resulting in misleading feedback on student solutions. Such limitations may reduce student access to the well-documented benefits of timely feedback when learning programming.
We evaluate the effectiveness of using Large Language Models (LLMs), as part of a larger workflow, to automatically generate test suites for CS1-level programming problems.
Each problem's statement and reference solution are provided to GPT-4 to produce a test suite that can be used by an autograder. We evaluate our proposed approach using a sample of 26 problems, and more than 25,000 attempted solutions to those problems, submitted by students in an introductory programming course. We compare the performance of the LLM-generated test suites against the instructor-created test suites for each problem.
Our findings reveal that LLM-generated test suites can correctly identify most valid solutions, and for most problems are at least as comprehensive as the instructor test suites. Additionally, the LLM-generated test suites exposed ambiguities in some problem statements, underscoring their potential to improve both autograding and instructional design.
Reyes, R. L., Isleta, K. P., Regala, J. D., & Bialba, D. M. R. (2024). Enhancing Experiential Science Learning With Virtual Labs: A Narrative Account of Merits, Challenges, and Implementation Strategies. Journal of Computer Assisted Learning, 40(6), 3167–3186.
Upon review, it was determined that the project was entirely funded by the National University (NU), Philippines, and their policy mandates listing only NU as the affiliation for research conducted under their funding. Therefore, the affiliation for Dr. Ronald L. Reyes should be corrected to:
Science and Mathematics, College of Education, Arts and Sciences, National University, Sampaloc, City of Manila, Philippines.
This correction pertains solely to the administrative detail of author affiliation and does not affect the findings, interpretations or scientific content of the study, which remain unchanged. No changes are necessary for the affiliations of the co-authors.
We apologise for this error.
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