Advancing information literacy in higher education: A predictive approach to analysis learning behaviour
Information literacy is an essential skill for
self-learning and lifelong learning, and it is a
fundamental ability for college students to
adjust to the social demands of today. Using the
rich and varied information literacy learning
behavior features to do the learning impact
prediction analysis is a useful method of
exposing the information literacy teaching
mechanism. By building a predictive model of
the learning impact based on information
literacy learning behavior characteristics, this
article examines the features of college students'
learning behaviors and investigates the
predicted learning effect. Data on information
literacy learning from 320 college students from
a Chinese university was used in the trial. The
qualities of information thinking and learning
effect are significantly correlated, according to
an analysis of college students' information
literacy learning behavior using the Pearson
algorithm. To categorize and forecast the
learning impact of college students' information
literacy, supervised classification algorithms
such Decision Tree, KNN, Naive Bayes, Neural
Net, and Random Forest are employed. In terms
of learning effect classification prediction, the
Random Forest prediction model is found to
perform the best.92.50% accuracy, 84.56%
precision, 94.81% recall, 89.39% F1-score, and
0.859 Kapaa coefficient are the values obtained.
In order to improve the quality of information
literacy instruction, optimize educational
decision-making, and support the long-term
development of exceptional and creative talent
in the information society, this paper proposes
differentiated intervention suggestions and
management decision-making references for
college students' information literacy
instruction.The results of our research on the
direction and way of thinking behind the
sustainable development of information literacy
training were promising.
"Advancing information literacy in higher education: A predictive approach to analysis learning behaviour", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a842-a860, March-2025, Available :https://ijsdr.org/papers/IJSDR2503093.pdf
Volume 10
Issue 3,
March-2025
Pages : a842-a860
Paper Reg. ID: IJSDR_300984
Published Paper Id: IJSDR2503093
Downloads: 000184
Research Area: Science and Technology
Country: Chennai, Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
