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This study aimed to test the benefit of note-taking on pauses and learning in online educational videos. Participants (N = 72) were randomly assigned to one of two note-taking conditions (allowed or not) with the possibility to take pauses during a 10-minute online instructional video on the autonomic nervous system. The results did not reveal a significant correlation between note-taking and the number of pauses. Moreover, we observed no significant effect of note-taking on learning performance. However, prior knowledge and age affected significantly the relationship between pauses, note-taking and learning performance. We discuss the importance of prior knowledge and age for future research.

During class, taking notes helps the encoding process, especially when students reformulate what is presented. After class, notes can be used for revision. Previous studies, mostly conducted in laboratory contexts, showed that note-taking on paper seems to be more efficient than on computer, probably because students are more likely to reformulate on paper. The originality of this study is to investigate students’ perceptions and strategies depending on their note-taking method during but also after class. Data of 108 students showed that students on paper (30%) declare making more reformulations and less multitasking that students on computer (70%). After class, review sheets are written for a large majority of the students on paper, whatever the method they used during class. These results suggest that even if computer is the most used method during class, paper is still used after class, and with paper are associated better strategies.

Educational technologies with AI are designed to personalize students’ learning, but also to alleviate teacher’s workload. However, acceptability of such technologies among teachers may be impacted by factors such as fear of replacement or ethical concerns. The purpose of the current study is to investigate attitudes of teachers towards educational tools with or without AI. The main hypothesis is that technologies with AI would be more negatively judged than technologies without AI, and thus intention to use would be weaker for technologies with AI. Results show that teachers seem to accurately perceive the potential benefit of AI technologies for reduction of workload, without feeling threatened by a replacement in the future. Ethical concerns are higher for AI technologies, but intention to use is similar. Differences between primary and secondary school teachers are discussed.

Learning from videos is becoming an important part of educational activities. Whereas outlines have great potential, particularly during information seeking (IS), few studies have so far explored the effect of their presence in videos. The two aims of the present study were thus to 1) evaluate the influence of outlines on IS and learning, and 2) analyze the extent to which an IS task can optimize the use of outlines during learning. As outlines are known to have a positive impact on video representation during IS tasks, we predicted that performing an IS task prior to a learning task featuring an outline would result in better content representation by learners. A total of 103 participants were divided into four groups (2 × 2 factorial design): with or without an outline, and with or without IS questions before learning. Results indicated that the presence of an outline promotes topic recall in terms of both the number and organization of the topics recalled, and performing an IS task with an outline before learning promotes content memorization to a small extent. Results are discussed in terms of engagement and metacognitive processes.

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC).To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

Learning from videos is becoming an important part of educational activities, but video content may be difficult to process, mainly because its organization is not very salient in this medium. The learner's organization process should be enhanced by: (1) displaying the structure (ie, table of contents) in the video-based environment; and (2) proposing interactivity tools (interactive table of contents and cued timeline) to use this structure in a more efficient way. A total of 57 participants (higher education students) were divided into three groups (no table of contents, table of contents, and interactive table of contents) to assess the effects of displaying a structure on learning outcomes and behaviours. Results showed that tables of contents boosted topic recall, and interactivity allowed learners to perform more relevant reviewing behaviours. However, reviewing behaviours appear to be broadly timeline-driven, and learners did not actively use the interactive table of contents. No significant effect was found on other learning outcomes (ie, content memorization and comprehension). One suggestion is that a table of contents, especially when it is interactive, can be an organizational aid during the construction of a mental model, but learners may underestimate its usefulness.

Information seeking (IS) has become a critical activity in video-based environments. Up to now, the effects of support on information seeking (i.e., scaffolding) have seldom been assessed. The twofold aim of the current study was to (a) assess the effects of scaffolding on IS in videos and (b) determine the characteristics of the users' mental models after an IS activity with or without scaffolding. We divided 50 participants into two groups that either did or did not benefit from initial scaffolding during an IS task. Both groups then had to perform a localization task without any further access to scaffolding. Results showed that scaffolding the video by providing a table of contents and markers on a timeline helped students to engage in highly efficient IS, but they had less accurate mental representations of the video than those without scaffolding. The hypothesis that scaffolding provides a usable but external model was therefore supported.

Background: Taking notes during learning has benefits both during class (through writing things down to encode information) and after class (by using written notes as external storage for revision). Comparisons of note‐taking methods (i.e., using paper or a computer) have mainly shown that paper leads to better learning. However, previous studies have mostly been conducted in laboratory contexts. Aims: The current study investigates university students' perceptions of the efficacity of their own preferred note‐taking method, along with the strategies they employ. Sample: Data were collected from 108 university students. Methods: Students answered a questionnaire about their note‐taking strategies during initial note‐taking (in class) and revision (after class). Results: The results show that students who take notes on paper do not consider their method to be more effective, but they report engaging in more reformulation and less multitasking. Students who take notes on a computer are more likely to reformat their notes, and thus to reformulate at a later stage. For all students, review sheets are mostly done on paper. Conclusions: These results suggest that although students are not necessarily aware of the benefits of reformulation associated with handwriting on paper during initial note‐taking, when revising, they tend to choose handwriting and benefit from reformulation when aiming for deeper processing. Therefore, revision activities remain mainly paper‐based. [ABSTRACT FROM AUTHOR]

In this paper, I take the cultural effects of generative artificial intelligence (generative AI) as a context for examining a broader perspective of AI's impact on contemporary art notions. After the introductory overview of generative AI, I summarize the distinct but often confused aspects of art notions and review the principal lines in which AI influences them: the strategic normalization of AI through art, the representation of AI art in the artworld, academia, and AI research, and the mutual permeability of art and kitsch in the digital culture. I connect these notional factors with the conceptual and ideological substrate of the computer science and AI industry, which blends the machinic agency fetishism, the equalization of computers and humans, the sociotechnical blindness, and cyberlibertarianism. The overtones of alienation, sociopathy, and misanthropy in the disparate but somehow coalescing philosophical premises, technical ideas, and political views in this substrate remain underexposed in AI studies so, in the closing discussion, I outline their manifestations in generative AI and introduce several viewpoints for a further critique of AI's cultural zeitgeist. They add a touch of skepticism to pondering how technological trends change our understanding of art and in which directions they stir its social, economic, and political roles. [ABSTRACT FROM AUTHOR]

Contemporary art consumption is predominantly online, driven by algorithmic recommendation systems that dictate artwork visibility. Despite not being designed for curation, these algorithms' machinic ways of seeing play a pivotal role in shaping visual culture, influencing artistic creation, visibility, and associated social and financial benefits. The Algorithmic Pedestal was a gallery, practice-based research project that reported gallerygoers' perceptions of a human's curation and curation achieved by Instagram's algorithm. This paper presents a technical analysis of the same exhibit using computer vision code, offering insights into machines' perception of visual art. The computer vision code assigned values on various metrics to each image, allowing statistical comparisons to identify differences between the collections of images selected by the human and the algorithmic system. The analysis reveals statistically significant differences between the exhibited images and the broader Metropolitan Museum of Art digital collection. However, the analysis found minimal distinctions between human-curated and Instagram-curated images. This study contributes insights into the perceived value of the curation process, shedding light on how audiences perceive artworks differently from machines using computer vision. [ABSTRACT FROM AUTHOR]

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