Study the Information Seeking Actions in a Digital Engineering College Environment

The pursuit of knowledge is a fundamental human activity. Though this may have always been the case, it is more widespread in the modern, so-called "information society" (Moore,1997). When it comes to running and managing a business, information is a vital resource. Managerial tasks including planning, organizing, leading, and regulating rely heavily on timely access to accurate information. Human and material resources must be managed effectively for any project to be completed successfully. This is impossible unless decision-makers have access to complete, timely, and relevant data. Humans are often considered to have been born naive or uneducated, and as such, they are expected to seek out information. People's information behaviors include how they define their information requirements, where they look for information, how they choose what they find, and how they put that knowledge to use. Information seeking is the intentional pursuit of knowledge in response to an unmet need. A person looking for anything could use either analog information sources.[1] Understanding people's information-seeking and -use habits is essential for addressing their information requirements. Furthermore, this information may help uncover previously unknown information behaviors and user profiles, which may be utilized to refine and create brand-new information models. In addition, librarians and other information workers need a deeper understanding of Information-seeking behavior, needs, and usage in order to effectively serve their users.[2-3] According to Wilson, the study of people's propensity to seek out information is a viable subfield of applied research, where funding is often allocated for projects with clear practical applications in system design and development. The information seeker's actions may be better understood if we consider a distinct set of reasons. This is a field of fundamental study, and although the information gleaned from it could be useful in the real world, it is not required to have any. Many of these studies focused on the library resources accessed, but not necessarily the library services required, and the vast majority of them relied on surveys and questionnaires to collect descriptive data on the information-seeking behavior of diverse groups.[4-5] Users' requirements must serve as the only basis for all information management actions. Depending on their roles, responsibilities, and tasks, each user type has a unique set of information requirements. People from all walks of life and all levels of government, as well as business owners, academics, students, professionals in a variety of fields, factory employees, farmers, and the ordinary public, make up the user base. This user population has very different information requirements. Careful evaluation of information requirements is required prior to the process of matching information needs with sources of information. [6-7] Information seekers, information demands, information supply gaps, and the sorts of information needed to accommodate a wide range of user needs are all subjects of current research.

knowledge. [8-9]

In this study, a questionnaire was handed out to participants. In this research, a structured questionnaire was employed to elicit the necessary data from the population under investigation. With these goals in mind and the relevant literature at hand, we developed a questionnaire to assess the information-seeking habits and practices of the staff and students at the sampled engineering universities. Additionally, a suitable sampling procedure was used to choose the sample from the population of interest. Later on, a structured questionnaire was employed to obtain the necessary data from engineering college students and teachers in Jalgaon. The acquired data was then collated and subjected to statistical tests to determine whether or not the study's hypotheses might be accepted or rejected.

In order to better understand how engineering students and faculty utilize digital library resources, we need to learn more about how they go about finding the information they need. The current study used the Survey Method of research since it was the most acceptable and fit for the topic at hand. In brief, the first section of the questionnaire is concerned with general library information and background, the second section covers information seeking behavior skills and searching techniques, the third section discusses e-resources and the extent to which they are used, and the fourth section discusses familiarity with and competence in the use of information and communication technologies. The participants in this research were chosen using a stratified random sampling technique. The population of interest was divided into strata by department, then by semester, and finally by department, branch, and category, from which a representative sample was drawn. Students and professors from chosen Jalgaon engineering institutes filled out a questionnaire to provide the bulk of the necessary information. patterns and online-search strategies of both students and teachers at engineering schools in Jalgaon. In the 2020-21 school year, Jalgaon was home to around 219 engineering schools. Only the most prominent subfields, such as Civil, Mechanical, Electrical, Electronic, Electronic & Communication, and Computer Science, will be considered. The rural colleges that are conveniently placed in rural areas were also given the attention they deserved. From the whole population, a representative sample was drawn using a stratified random sampling technique.

Jalgaon has 52 colleges; 22 were chosen using a proportional selection method; 14 came from the Amoda area, which has 34, and 8 came from the Bhokar region, which has 18, respectively.

The data, analyses, and hypothesis testing all followed the following battery of statistical tests for maximum confidence in the findings. To examine the relationship between two variables, the spearman correlation coefficient test was used.

Non-parametric statistics such as Kendall's coefficient of concordance (also known as Kendall's W) are useful tools for gauging the degree to which different raters of the same data agree with one another on the relative rankings of different groups of objects. The Kendall rank correlation coefficient is used to measure the closeness of rankings for a total of N items or people over several (k) sets of rankings. We often calculate Spearman's correlation coefficient when there are only two sets of rankings between two variables of 'N'objects, but Kendall's coefficient of concordance (W) is thought to be an accurate measure of assessing the degree of relationship among three or more sets of rankings.

Whether there are more than two groups, and you want to know if there is a statistically significant difference in the mean score, you may use the one-way ANOVA test.

A majority of the people in the sample group are men, as shown in Table -1; women make up 36.76 percent of the sample group.

Most respondents in this survey possess master's degrees in technology (M.Tech), followed by bachelor's degrees in technology (B.Tech), and only a small percentage of the faculty members in this study have doctoral degrees (Ph.D.). From what has been said, we may deduce that the vast majority of the people in the sample are postgraduates.

The informational needs of students and teachers are distinct, and both groups need a variety of sources. From among the 680 responses, Table-21 details the many forms of information sought after by students and teachers. There are 539 staff and students in need of academic resources, or 79.26% of the entire population. Nearly six-in-ten students and teachers (403) have a general information need, more access to information on government programs and policies. There are still 272 teachers and students who need the most up-to-date data. Comparatively fewer pupils and teachers need access to financial data (272-40%) or environmental data (127-18.67%). Since most respondents are academics, it stands to reason that they would prefer academic knowledge over other types of information.

The research was conducted in part to ascertain how extensively engineering college undergrads and their professors make use of available informational resources. The materials utilized by the students and teachers in the study are listed in the table above, with responses ranging from "very often" to "never." When calculating a score, we first

and finally we order the results according to the mean value, as shown in the table below.

Using a spearman coefficient correlation test, we can show that there is a very significant positive correlation (r=.973) between students' and faculty members' levels of familiarity with and usage of electronic information resources at engineering institutions. This shows that both students and faculty at engineering schools have similar levels of familiarity with and usage of digital libraries and archives. Table 6: The Mean, Standard Deviation, and t-Test Results Between Male and Female Library Users of Engineering Colleges Studied With Regard To Their E-Resources Knowledge

Table 7: Examining the differences in e-Resources knowledge across students in urban and rural engineering schools using mean, standard deviation, and t-test It has been shown that the mean awareness of electronic resources among urban and rural users of engineering college libraries is 51.39% and 54.64%, respectively, with a standard deviation of 14.05% and 14.83%. Since the p-value is less than 0.05, we reject the null hypothesis and accept the alternative hypothesis that there is a statistically significant difference between male and female users of the library of Engineering colleges with respect to level of Awareness of Electronic Resources scores (t=3.325, d.f=678). Users from urban areas had a higher degree of awareness of electronic resources than users from rural areas, according to a survey of students at engineering institutions in the Indian state of Jalgaon. In conclusion, it seems that engineering students in metropolitan areas have a higher degree of familiarity with electronic resources than their rural counterparts.

The mean scores for men and women who utilize the library at engineering schools are 46.16 and 48.59, with a standard deviation of 8.82 and 9.99, respectively. Since the p-value is more than 5%, we reject the null hypothesis that there is no (t=1.212, d.f=678). Results showed no statistically significant difference in information-seeking behavior pattern scores between male and female users of the libraries at the studied engineering schools. Information-seeking behavior pattern scores are comparable amongst male and female engineering college library users, it has been shown. Table 9: Mean, standard deviation, and t-test results for information-seeking behavior pattern scores from library patrons in urban and rural areas of Engineering institutions in Jalgaon state

Users from urban areas have a mean score of 44.64, while those from rural areas have a mean score of 42.39; the standard deviation for both groups is 11.05 and 10.83, respectively. Since the p-value is less than 0.05, we reject the null hypothesis and accept the alternative hypothesis that there is a statistically significant difference between urban and rural users of the libraryof Engineering colleges under study with respect to Information seeking behavior pattern scores (t=2.325, d.f=678). There is a statistically significant gap in the information-seeking behavior pattern scores of urban and rural library users at Engineering schools, according to this data. Users of engineering libraries in urban areas had higher information-seeking behavior pattern scores than their rural counterparts.

1. Christian Matt, Manuel Trenz et al, (2019). ‗The Digitization of the Individual: Conceptual Foundations and Opportunities for Research‘. 2. Collins English Dictionary. (2020). School. In Collinsdictionary.com. Retrieved December 30, 2020. Outline‘. 4. David Nicholas, (2017). ‗The impact of the Internet on information seeking in the media‘, ASLIB Proceedings. 5. Sharma, A. (2018). "Information Seeking Behavior of Engineering College Students in a Digital.‖ 6. Dr DattatrayaKalbande, (2019). ‗Information Seeking Behaviour of Research Students of Dr.Babasaheb Ambedkar Marathwada University, Aurangabad‘. 7. Dr Jamna Saini, (2017). ‗Advantages and Disadvantages of Digitization in Library System‘. 8. Dr Olayinka BabayemiMakinde, Prof GlenroseVelileJiyane& Dr Tinashe Mugwisi, (2019). ‗Factors and Challenges Affecting the Information Seeking Behaviour of Scienceand Technology Researchers‘. 9. Elizabeth Jean Brumfield, (2018). ‗Using Online Tutorials to Reduce Uncertainty in Information Seeking Behaviour‘. 10. Emmanuel EbikaboweiBaro& George O Onyenania, (2019). ‗Information Seeking Behaviour of Undergraduate Students in Humanities in Three Universities in Nigeria‘. 11. Florence GonodBoissin&Docsi, (2016). ‗Information Seeking Behaviour and Use of the Internet by French General Practitioners: A Qualitative Study‘.

PhD Student, Kalinga University, Raipur