Beyond Bloom's Taxonomy: Integrating “searching as learning” and e‐learning research perspectives

Reynolds, Rebecca; Meyers, Eric M.; Ghosh, Souvick; Novin, Alamir

doi:10.1002/pra2.2018.14505501093

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of SAL was first pointed out in the Dagstuhl seminar (2013) [14] and formulated a strong research agenda for follow-up studies. Reynolds et al [16] figured out that e-learning and information retrieval may provide useful boundaries and definitions for SAL.…”

Section: Searching As Learningmentioning

confidence: 99%

“…Rieh et al [1] assessed learning from online searching behavior and develop a search system that supports SAL. Ghosh et al [16] studied the relationship between search and learning by taking learning as the result of the information search process. Proao-Rí et al [20] conceptualized searching as a learning path and focus on helping learners to plan their SAL path.…”

Section: Searching As Learningmentioning

confidence: 99%

See 1 more Smart Citation

Extracting Searching as Learning Tasks Based on IBRT Approach

Zhang

2022

Applied Sciences

View full text Add to dashboard Cite

With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.

show abstract

Section: Searching As Learningmentioning

confidence: 99%

Section: Searching As Learningmentioning

confidence: 99%

Extracting Searching as Learning Tasks Based on IBRT Approach

Zhang

2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Most of the studies belong to the latter category. These studies do not explicitly use the word ‘learning’, although implicitly they deal with this phenomenon.” Further, according to Reynolds et al (2018), searching and learning are not isolated but co‐occurring events, and the learning outcomes are often influenced by the cognitive complexity of the search tasks (Krathwohl, 2002), the choice of information sources (Rath et al, 2018), the affordances offered by search devices (chatbots, personal assistants), and the environment (Ghosh and Shah, 2019). The searchers’ topic knowledge, topic interest, and perceived task difficulty also affect the behavioral patterns and learning goals.…”

Section: Introductionmentioning

confidence: 99%

Search Systems and Artificial Intelligence: Enhancing Searching as Learning Approaches to Counter Misinformation

Ghosh,

Gwizdka,

Lewandowski

et al. 2023

Proceedings of the Association for Information Science and Tech

Self Cite

View full text Add to dashboard Cite

Searching as a learning process implies that learning occurs during a search process and might happens incidentally, influenced by the context the search takes place and the system that is used. Searching and learning are not isolated but co‐occurring events. Research investigates how search systems can be improved to foster learning processes, integrate information literacy enhancing methods and support user's sense‐making of information. Regarding the advancement of AI algorithms and their implementation in search systems, the concept of searching as a learning process can help to better understand human‐computer interactions and future information‐seeking processes. The panel advances current research on search systems for learning in non‐formal settings, with a focus on investigating the relation between searching and learning processes that influence people's understanding, assessing and use of information. It will focus on the contributions of information science research and the expectations of future searching behavior with respect to emerging advances in AI.

show abstract

Adaptive learning: toward an intentional model for learning process guidance based on learner’s motivation

Bayounes

Saâdi

Kinshuk

2022

Smart Learn. Environ.

View full text Add to dashboard Cite

The goal of ITS is to support learning content, activities, and resources, adapted to the specific needs of the individual learner and influenced by learner’s motivation. One of the major challenges to the mainstream adoption of adaptive learning is the complexity and time involved in guiding the learning process. To tackle these problems, this research proposes an intentional model that adopts Map formalism to support personalized learning guidance by considering learner’s motivation. For that, the intentional model adopts the ARCS model of motivation. The proposed model couples the learner’s intention with the learning strategies and provides a multitude of paths between learner intentions. Based on the learner’s motivation and the corresponding learning mode or the individual learning style, the ITS can adaptively support the learner to achieve his/her intention through the selected strategy. The first results show that this study’s provide implications for both theoretical research and the practical development of ITSs that will help education professionals make full use of ITSs. In the initial tests, the process model has met what was expected, however, more studies with experiments must be carried out.

show abstract

Beyond Bloom's Taxonomy: Integrating “searching as learning” and e‐learning research perspectives

Cited by 3 publications

References 14 publications

Extracting Searching as Learning Tasks Based on IBRT Approach

Extracting Searching as Learning Tasks Based on IBRT Approach

Search Systems and Artificial Intelligence: Enhancing Searching as Learning Approaches to Counter Misinformation

Adaptive learning: toward an intentional model for learning process guidance based on learner’s motivation

Contact Info

Product

Resources

About