ALLAMA IQBAL OPEN UNIVERSITY, ISLAMABAD
(Department Library & Information Sciences)
WARNING
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THE ASSIGNMENT(S) WILL DEBAR THE STUDENT FROM AWARD
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2. SUBMITTING ASSIGNMENTS BORROWED OR STOLEN FROM
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Course: Library Analytics and Big Data (9219) Semester: Spring, 2025
Level: BS-LIS
Shelha Mukhatir
Cb459048
Note 1:
Total Marks: 100 Pass marks: 40
ASSIGNMENT No. 1
(Units 1–5)
Q.1 Explain the terms "analytics" and "metrics." How are they applied in the
context of libraries? Discuss library analytics in detail, including its
benefits and challenges. (20)
Q.2 Write a comprehensive note on "big data" and "small data" in libraries.
Provide relevant examples of each and discuss their importance in library
management and services. (20)
Q.3 How can university libraries build an effective analytics toolkit? Discuss
the process involved, including key tools and techniques that should be
part of the toolkit. (20)
Q.4 Discuss whether library usage has an impact on students' academic
success. Provide evidence and examples to support your arguments. (20)
Q.5 Write short notes on the following: (20)
i. Shared analytical services and their significance
ii. The difference between local and national collections in libraries
iii. The role of collection management analytics in library decision-
making
iv. The importance and methods of qualitative research in library studies
Answer
1
I’ll provide detailed answers to each question.
*Q1. Analytics and metrics in libraries:*
*Analytics:* Analytics refers to the process of analyzing data to gain insights and
inform decision-making.
*Metrics:* Metrics refer to the quantitative measures used to evaluate
performance or progress.
*Library analytics:*
1. *Benefits*: Library analytics can help libraries evaluate their services, identify
areas for improvement, and make data-driven decisions.
2. *Challenges*: Library analytics can be challenging due to data quality issues,
lack of standardization, and limited resources.
*Applications:*
1. *Collection development*: Library analytics can inform collection
development decisions by analyzing usage patterns and user preferences.
2. *User engagement*: Library analytics can help libraries understand user
behavior and engagement with library services.
*Q2. Big data and small data in libraries:*
*Big data:* Big data refers to large, complex datasets that require specialized
tools and techniques to analyze.
*Small data:* Small data refers to smaller, more focused datasets that can be
analyzed using traditional methods.
*Examples:*
2
1. *Big data*: Analyzing user behavior and search patterns in a large digital
library collection.
2. *Small data*: Analyzing user feedback and satisfaction surveys in a small
public library.
*Importance:*
1. *Big data*: Big data can provide insights into user behavior and preferences,
informing library services and collection development.
2. *Small data*: Small data can provide targeted insights into specific user needs
and preferences, informing local library services.
*Q3. Building an effective analytics toolkit:*
1. *Define goals and objectives*: Clearly define the goals and objectives of the
analytics toolkit.
2. *Choose relevant tools and techniques*: Select tools and techniques that align
with the goals and objectives.
3. *Ensure data quality*: Ensure that data is accurate, complete, and relevant.
*Key tools and techniques:*
1. *Data visualization*: Data visualization can help communicate complex data
insights to stakeholders.
2. *Statistical analysis*: Statistical analysis can help identify trends and patterns
in library data.
3. *Machine learning*: Machine learning can help libraries predict user behavior
and preferences.
*Q4. Library usage and academic success:*
1. *Correlation*: Research has shown a correlation between library usage and
academic success.
2. *Examples*: Studies have found that students who use library resources and
services tend to have higher GPAs and graduation rates.
*Evidence:*
3
1. *Studies*: Numerous studies have investigated the relationship between library
usage and academic success.
2. *Data analysis*: Analyzing library usage data can provide insights into the
impact of library services on academic success.
*Q5. Short notes:*
i. *Shared analytical services*: Shared analytical services can provide
libraries with access to expertise and resources, enabling them to make
data-driven decisions.
ii. *Local and national collections*: Local collections refer to materials
specific to a particular library or region, while national collections
refer to materials that are part of a larger national collection.
iii. *Collection management analytics*: Collection management analytics
can help libraries make informed decisions about collection
development and management.
iv. *Qualitative research*: Qualitative research can provide insights into
user needs and preferences, complementing quantitative data and
informing library services.
