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Use of web search engines and personalisation in information searching for educational purposes

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Abstract

Introduction: Students increasingly depend on Web search for educational purposes. This causes concerns among education providers as some evidence indicates that in higher education, the disadvantages of Web search and personalised information are not justified by the benefits. Method: One hundred and twenty university students were surveyed about their information-seeking behaviour for educational purposes. We also examined students’ information access while using Web search, through twenty-eight one-on-one study sessions. Analysis: Survey participants ranked their preference towards different information resources on a 5-point Likert scale. Given equal exposure to the first five standard pages of the search results during the study sessions, students’ explicit and implicit feedback was used to evaluate the relevance of the search results. Results: First, most participating students declared that they use Google search engine as their primary or only information-seeking tool. Second, about 60% of the clicked result links during the study sessions were located in pages 2+ of the search results without personalisation influencing the relevance of the top-ranked search results. In real-life scenarios pages 2+ of the search results receive only ~10% of the clicks. Students also expressed more satisfaction with the relevance of non-personalised over personalised search results. These differences presented a missed information opportunity, an opportunity bias, for students.

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... Educational research is considered to be an integral teaching field of University Departments of Education. Bibliography research is one of the most important elements of empirical educational research and is achieved through common search engines, such as Google Scholar, that is quite popular in academic library literature (Cothran 2011;Salehi et al. 2018;Shen 2012;Soules 2015). Learning how to use research tools, such as search engines for research, is very important for students (Keshavarz et al. 2016;Liaw and Huang 2003;Salehi et al. 2018;Parissi et al. 2019). ...
... Bibliography research is one of the most important elements of empirical educational research and is achieved through common search engines, such as Google Scholar, that is quite popular in academic library literature (Cothran 2011;Salehi et al. 2018;Shen 2012;Soules 2015). Learning how to use research tools, such as search engines for research, is very important for students (Keshavarz et al. 2016;Liaw and Huang 2003;Salehi et al. 2018;Parissi et al. 2019). Apart from the educational procedure, preservice teachers learn how to use technology in the important area of empirical educational research. ...
... Apart from the educational procedure, preservice teachers learn how to use technology in the important area of empirical educational research. Students learn how to use tools for bibliography research, such as Google search engine and Google Scholar, for more specific academic information (Salehi et al. 2018;Soules 2015;Parissi et al. 2019). In this framework it is very important to investigate the usefulness and ease of use of an Information System (IS) for the directly involved students (Davis 1989;Davi et al. 1989;Cordes 2014). ...
Article
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Teaching empirical educational research in higher education involves implementing a very useful web tool for bibliography/scientific literature research, a search engine specifically employed for scientific papers, such as "Google Scholar". The aim of this study is to examine undergraduate students' acceptance of technology, through their intention to adopt and use a specific search engine for research purposes. To accomplish this goal, a questionnaire was administered to 225 students from two Universities in Greece. The study was based on TAM (Technology Acceptance Model), reinforced by four external determinants (perceived self-efficacy, subjective norms, facilitating conditions and technological complexity), that contributed to the indirect prediction of the behavioral intention to use the particular search engine. The results of the survey confirm that the main factors of TAM, perceived ease of use and perceived usefulness are significant determinants of students' behavioral intention to use Google Scholar. Moreover, perceived self-efficacy, subjective norms, facilitating conditions and technological complexity have an indirect significant effect on behavioral intention. All these factors explain almost 60% of students' behavioral intention to use this search engine. The results of this survey could be beneficial to the enrichment of good educational practices for the additional training of teachers, as well as to the improvement of the students' skills in the implementation of this specific research tool. Besides, more stakeholders, such as librarians, or even human resources of big companies that construct and support similar systems, such as search engines, could also benefit from the present research.
... Se usaron las bases de datos ERIC, BASE, y el buscador Google Académico. Su elección obedeció a la cantidad de publicaciones que cubren y a su popularidad probada para la investigación en educación (Salehi et al., 2018). Los artículos debían cumplir los siguientes criterios de inclusión: ...
Article
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... In this sense, it seems in particularly alarming that for any decision about what type of search engine to use, Google has the strongest dominance, with a global market proportion of over 92% (StatCounter, 2022). At the same time, different studies highlight the potential threat of search engines' biases, especially in terms how they may shape people's opinions, as individuals seem to over-rely on Google search results (Ballatore, 2015;Salehi et al., 2018). Hence, a skilled search strategy that limits the risk of search engines' biases might entail using multiple search engines, as this might decrease the outsized effects that one search engine's algorithm can have when pre-selecting and ranking search results. ...
Article
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Teachers need to be able to inform and justify their teaching practice based on available research knowledge. When searching for research knowledge, the Internet plays a crucial role as it allows teachers to search for and access evidence long after their own education at university. On the Internet, however, educational information can have varying levels of scientific groundedness (e.g., science articles or blogs from colleagues), and research indicates that (pre-service) teachers struggle to find, select, and evaluate online educational information. It is precisely for this reason that it is important to educate (pre-service) teachers on how to competently source online information. This study describes pre-service teachers’ search strategies when sourcing online educational information about the topic “students’ use of mobile phones in class.” It sheds light on their use of 1) basic or advanced search strategies and 2) the role of Internet-specific epistemological beliefs (ISEBs). N = 77 pre-service teachers conducted a realistic search on the Internet and selected those web items (WI) that they perceived relevant for justifying whether mobile phones should be used in class. Their sourcing behavior was screen-recorded and analyzed. Most selected WI were found via search engines of Google LLC (91.4%). Advanced search strategies were defined as 1) using two or more search engines (performed by 62.3% of participants), 2) adapting search terms and/or formulating new search terms (90.9%), 3) selecting at least one WI that was not listed among the first four ranks on the first search engine results page (54.7%), and 4) checking for the trustworthiness of the author/source (14.3%) or the quality of the content (13%). Binary logistic regressions were used to analyze the relationship between ISEBs and 1) search strategies and 2) science-relatedness of WI as dependent variables. The predictor ISEB did not contribute to the models, meaning that differences in participants’ ISEBs did not significantly relate to their search strategies nor to the science-relatedness of WI, all β ≤ |.36|, Wald ≤ .64, p ≥ .43. The role of pre-service teachers’ search strategies is discussed with respect to teachers’ evidence-informed reasoning and its implications for teacher education.
... We selected the search engine startpage.com, as it delivers Google results while anonymizing search requests, and is therefore not subject to a user-specific filter bubble (Salehi et al., 2018). In the following paragraphs, the selection of frameworks with which the FiNN framework is compared is described in more detail. ...
Article
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Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods, and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox (https://github.com/neurophysiological-analysis/FiNN) and its documentation (https://neurophysiological-analysis.github.io/FiNN/) are freely available. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.
... Google (search engine) seems to lull and worsen the literacy situation without a definite solution. Googling manipulates and deprives deep learning abilities (Salehi & Ashman, 2018). Ease of searching includes when it does not match, changing keywords into an experience that continues, but does not leave an immersive experience as part of the learning process. ...
Article
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This study seeks to see how students use search engines in learning and uncover the side effects of googling for students during a pandemic. This study combines a phenomenological approach to explore the condition of students while using a search engine and digital ethnography by looking at historical search data. The researcher conducted interviews with high school students in Yogyakarta from various backgrounds and traced the digital footprint of their search accounts. The research results show that googling for students changes learning activities into searching for information. During the pandemic, googling replaces the role of the teacher and becomes a student’s study buddy. Learning becomes an individual activity, “Do It Yourself” becomes a jargon for learning, and the easiest way to learn is to use a search engine. However, googling has hidden dangers that slowly reduce learning abilities, stunting students’ critical thinking. Behind the ease of googling, there are side effects of using search engines that are not realized: (1) Googling eliminates long-term memory, (2) makes students indolent to read in-depth, (3) increases academic dishonesty and (4) reduced critical thinking.
... We selected the search engine startpage.com, as it delivers Google results while anonymizing search requests, and is therefore not subject to a user-specific filter bubble (Salehi et al., 2018). In the following paragraphs, the selection of frameworks with which the FiNN framework is compared is described in more detail. ...
Preprint
Full-text available
Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN ( Fi nd N europhysiological N etworks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods, and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox ( https://github.com/neurophysiological-analysis/FiNN ) and its documentation ( https://neurophysiological-analysis.github.io/FiNN/ ) are freely available. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.
... Studies focused on HE students' strategies for searching online information have shown the search behavior of untrained students often tends to follow some general patterns [16,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. These include [20]: i) 'foraging', i.e., there is no explicit research plan, and students base their task responses on the online material they come across in an arbitrary search, ii) 'Google dependence', i.e., students do not use any other search tools other than this search engine, iii) 'Rudimentary search heuristic', and iv) 'habitual topic changing' after rather superficial skimming. ...
Article
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Many active, inquiry-based learning activities nowadays rely upon the students' capacity to perform efficient information research on the Internet. Study and Research Paths (SRPs) have been proposed to model inquiry learning, and successfully used as teaching formats in different areas. In an SRP the search for an answer to a generating question (Q 0) leads to a sequence of derived questions and answers, which are modeled using a Q-A map. We have investigated the benefits of using SRPs and Q-A maps to improve Web-based inquiry learning. We designed an SRP for a course on Information and Communication Technologies (ICT), belonging to an Engineering degree. The class-session SRPs revolved around Q 0 questions such as 'What is a «firewall»?', 'What are the main features of 5G?' etc. Our results, based on the analysis of six courses conducted between 2015/16-2020/21, show that the SRP is an enriching tool for learning ICT: content expands beyond that of the traditional course and is maintained up-to-date. Students are engaged and motivated by the active-search activity. In addition, the SRP helps in improving the students' skills in searching and selecting information on the Internet. The Q-A maps served both the students, to structure their Web inquiry, and the teacher, to monitor the learning study process.
... The search features most needed to support complex information needs are ones allowing the user to iterate on the results, to expand and to narrow. Faceted search options (restricting by document type or publication date, for example) address some of these needs to narrow results; however, further transparency about options to selectively switch-off personalisation (Salehi et al., 2019), for example, is also called for, as is re-ranking (sorting) of results by user-specified parameters. Second, an intelligent search mode that supports in-the-moment user learning about search and development of search skills would move users from novice skillsets to more abilities needed to manage complex information needs. ...
Article
In recent years, leading website search engines have abandoned vital search features supporting complex information needs, evolving instead for the marketplace and for users seeking speedy answers to easy questions. The consequences are troubling, for researchers and for information science educators, with concerns ranging from the very relevance of search results and the unknowing of what is missing, to the novice searcher’s waning ability to frame potent queries and to learn ways to refine results. We report on a grounded theory study of search experiences of information professionals and graduate students (n = 20) that contributes a holistic understanding of web searching, using its findings both to frame what is lacking in the design evolution of search engines for complex information needs and to outline a way forward. One goal of the study was to evaluate an established model of web searching, called Net Lenses, a theoretical framework shown to be highly relevant during the study’s grounded theory secondary literature review. The original Net Lenses research used phenomenography to identify variation in the web search experiences of university students (n = 41), evidencing four categories according to the characteristics of searcher awareness, approach to learning, response to obstacles and search outcomes. This study validated the model and led to an expanded version, Net Lenses 2.0, with five categories of search experience, reflecting the complex information needs of more advanced searchers. This resultant Net Lenses 2.0 model is discussed with its implications for search engine design, for advanced searchers and also for learning-to-search modes, much needed by searchers seeking to develop their abilities. The study’s implications coalesce in a call to action for more inclusive search interface design, and an agenda is put forth for how information researchers, educators and literacy advocates can move forward in their intersecting domains.
... For future work, one can use machine learning tools to estimate the system parameters and make them more precise in the meanwhile that the load bal- (a) Parameters are off for 5% higher (b) Parameters are off for 10% higher (c) Parameters are off for 15% higher (d) Parameters are off for 20% higher (e) Parameters are off for 25% higher (f) Parameters are off for 30% higher ancing algorithm is working with the estimated parameters. The scheduling algorithms presented in this work can also be applied to a vast number of applications including but not limited to healthcare and super market models [61], [9], [17], [24], web search engines [51], [50], [5], [30], [64], electric vehicle charging [1], [18], [56], [15], [8], [2], [7], [44], [45] and so on. ...
Preprint
Parallel computing is the fundamental base for MapReduce framework in Hadoop. Each data chunk is replicated over 3 servers for increasing availability of data and decreasing probability of data loss. Hence, the 3 servers that have Map task stored on their disk are fastest servers to process them, which are called local servers. All servers in the same rack as local servers are called rack-local servers that are slower than local servers since data chunk associated with Map task should be fetched through top of the rack switch. All other servers are called remote servers that are slowest servers since they need to fetch data from a local server in another rack, so data should be transmitted through at least 2 top of rack switches and a core switch. Note that number of switches in path of data transfer depends on internal network structure of data centers. The First-In-First-Out (FIFO) and Hadoop Fair Scheduler (HFS) algorithms do not take rack structure of data centers into account, so they are known to not be heavy-traffic delay optimal or even throughput optimal. The recent advances on scheduling for data centers considering rack structure of them and heterogeneity of servers resulted in state-of-the-art Balanced-PANDAS algorithm that outperforms classic MaxWeight algorithm. In both Balanced-PANDAS and MaxWeight algorithms, processing rate of local, rack-local, and remote servers are assumed to be known. However, with the change of traffic over time in addition to estimation errors of processing rates, it is not realistic to consider processing rates to be known. In this work, we study robustness of Balanced-PANDAS and MaxWeight algorithms in terms of inaccurate estimations of processing rates. We observe that Balanced-PANDAS is not as sensitive as MaxWeight on the accuracy of processing rates, making it more appealing to use in data centers.
... Research in higher education has revealed that university students depend on the Internet to look for information to accomplish both academic and non-academic tasks, and that they do so mainly by using general-purpose search engines such as Google (Salehi, Du, & Ashman, 2018). Studies on the use of various search engines reveal that people of all ages have difficulties using them. ...
Article
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This study investigates the changes in university students’ perceptions towards the use of Web search engines after their exposure to a teaching intervention centered on the information problem-solving process. A total of 138 students of the Department of Educational Sciences and Early Childhood Education of the University of Patras were surveyed to measure perceived ease of use and usefulness of search engines and search engine self-efficacy. A questionnaire, part of which was based on the Technology Acceptance Model, was developed and distributed to respondents, both before and after the course, to measure their perceptions. The results revealed statistically significant improvement for the ease of use and usefulness of search engines, as well as for search engine self-efficacy.
... Research in higher education has revealed that university students depend on the Internet to look for information to accomplish both academic and non-academic tasks, and that they do so mainly by using general-purpose search engines such as Google (Salehi, Du, & Ashman, 2018). Studies on the use of various search engines reveal that people of all ages have difficulties using them. ...
Article
Full-text available
This study investigates the changes in university students’ perceptions towards the use of Web search engines after their exposure to a teaching intervention centered on the information problem-solving process. A total of 138 students of the Department of Educational Sciences and Early Childhood Education of the University of Patras were surveyed to measure perceived ease of use and usefulness of search engines and search engine self-efficacy. A questionnaire, part of which was based on the Technology Acceptance Model, was developed and distributed to respondents, both before and after the course, to measure their perceptions. The results revealed statistically significant improvement for the ease of use and usefulness of search engines, as well as for search engine self-efficacy.
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