Examining the use of text and video resources during web-search based learning—a new methodological approach

Pardi, Georg; Hienert, Daniel; Kammerer, Yvonne

doi:10.1080/13614568.2022.2099583

Cited by 5 publications

(4 citation statements)

References 58 publications

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“…Further, since we only investigated users' initial and hypothetical selection preferences, it remains unclear whether the preference for a particular resource modality remains stable across the completion of a search task. Hence, more research is needed to examine to which degree the results of the present research can be generalized to more realistic settings with search results being presented on SERPs (e.g., Haas & Unkel, 2017) or even when examining an open web search (e.g., Pardi et al, 2022). Within such a naturalistic setting, it would be possible to investigate which resource modality users prefer during multiple‐search sessions of different knowledge types.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that the preference for a resource modality was moderated by the knowledge type underlying the learning task (see Section 2.2.3). Furthermore, Pardi et al (2022) investigated how learners used different resource modalities during a free web search-based learning setting. With the help of eye-tracking, they investigated to which degree the different resource modalities were used while learning about the formation of thunderstorms and lightning.…”

Section: Resource Modalitymentioning

confidence: 99%

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The influence of knowledge type and source reputation on preferences for website or video search results

Pardi

Gottschling

Kammerer

2023

Asso for Info Science & Tech

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Searching for information on the web has become an essential task in our society. This article presents a preregistered experimental study that investigates how the knowledge dimension (conceptual, procedural) and relevance of displaying spatiotemporal changes (high, low) for a given search task affect searchers' preference for different resource modalities (websites, videos). Additionally, effects of source reputation (high, low) were examined. Participants were confronted with 16 learning tasks and respective mock‐up video or website search results and had to indicate the likelihood of selecting the respective search results. The learning tasks varied regarding their knowledge dimension and their degree of spatiotemporal changes. Search results varied regarding source reputation and resource modality. Study results showed that search results with low source reputation were more likely to be selected when they were video results as compared to website results. Furthermore, for learning tasks with a high degree of spatiotemporal changes, a preference for videos over website results was found, while a low degree of spatiotemporal changes did not lead to modality preferences. To conclude, both knowledge dimension and degree of visuospatial changes of learning tasks seem to be promising classifications to consider for understanding users' source and modality selection during web‐based learning.

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Section: Discussionmentioning

confidence: 99%

Section: Resource Modalitymentioning

confidence: 99%

The influence of knowledge type and source reputation on preferences for website or video search results

Pardi

Gottschling

Kammerer

2023

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pardi et al [18] introduced a methodological approach for capturing and analyzing the processing and utilization of text, images, and video content during the learning process based on web search. The authors asked 108 college students to search a natural science topic on the web and recorded their eye movements and navigation behaviour while they were doing so, then the software "reading protocol" was used to automatically map the students' fixations to text, images, and video content that they had fixated upon on any information resource retrieved.…”

Section: Introductionmentioning

confidence: 99%

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Xiao¹,

Yang²,

Zhang³

2023

Int. J. Emerg. Technol. Learn.

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Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially, and the requirement of students for learning resource retrieval is on the rise. However, the existing methods of learning resource retrieval generally overlook the overall knowledge systems that the students have already possessed, so it’s impossible for them to predict the students’ learning path or perform deviation adjustment. In view of these issues, this paper aims to study a new learning resource retrieval method based on multi-knowledge association mining. At first, the paper introduces the application of the Knowledge Graph Embedding (KGE) technology in learning resource retrieval, proposes the problem of learning resource retrieval, and points out the goal of learning resource retrieval. Then, a breadth-first soft-matching search algorithm is introduced to attain the multiple association paths between students and learning resources, and a retrieval module is constructed based on association path to further learn the association paths between students and learning resources within the framework of feature learning, and to predict the probability of interactions between students and learning resources. At last, this paper evaluates the association paths and uses experimental results to verify the validity of the proposed learning resource retrieval method.

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“…Pardi et al [22] introduces a new methodology for capturing and analyzing the processing and use of texts, images, and video contents in web search-based learning. To calculate the degree of overlap, the words in the articles searched by the participants are traced back to the words encountered in the recording of fixed texts or viewing videos.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

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Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

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Examining the use of text and video resources during web-search based learning—a new methodological approach

Cited by 5 publications

References 58 publications

The influence of knowledge type and source reputation on preferences for website or video search results

The influence of knowledge type and source reputation on preferences for website or video search results

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

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