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Deep Learning Approach and Information Literacy

Prasanna Ranaweera is a Senior Lecturer attached to the National Institute of Library & Information Sciences
(NILIS), University of Colombo, Sri Lanka, and served as the Director of the same Institute from 2014 -2020.
His academic background includes a BA Honors, a Masters in Arts, Masters in Library and Information
Sciences and a Diploma in Information Literacy from the University of Victoria, Wellington, New Zealand. He
carried out his PhD studies under the area of Information Literacy in the Faculty of Computer Sciences at the
University of Malaya, Malaysia. He has presented over twenty conference papers on the subject of information
literacy, for local and international conferences, including IFLA and IASL. He has also published a number
of Journal Articles in the LIS refereed journals while publishing books in the LIS subject area.

Introduction
Learning and teaching are mainly based on three domains, namely cognitive domain, psychomotor
domain and affective domain. In simple terms Knowledge, Skills and attitudes. The Deep learning
approach helps students extensively to learn how to create knowledge from the available data and
information. Learning can be categorized into two sections, namely, surface learning and deep learning.
Surface learning happens with teacher centered rote and memorizing passive learning techniques.
Consequently, students present or reproduce the same content that they memorized together with their
classroom lessons learnt by using the rote learning practice. But deep learning happens with the student-
centered resource based educational principles. A deep learning approach has been described as a
student’s intention to understand content together with the processes of relating and structuring ideas,
looking for underlying principles, weighing relevant evidence, and critically evaluating knowledge (Biggs
et al. 2001). The educational structure in the surface learning approach might direct the students to create
and present the same information contained in the available information or data. In contrast, the education
experts have recognized that the constructive approach of learning, which is linked with the deep learning,
is the most effective way. In the Constructivist approach the learner plays a major role in constructing
knowledge from information and data; and the teacher or lecturer plays the role of facilitator.

Surface learning vs. Deep learning

Deep learning is described as an approach to learning that will develop a learner’s genuine understanding.
The levels of skills elaborated in the Bloom’s Taxonomy are Remembering, Understanding, Applying,
Analyzing, Evaluating, and Creating. Surface learning covers the first two levels of skills; namely,
remembering and understanding, while deep learning covers apply, analyze, evaluate, and create stages.
To be deep learners the students should be able to access, evaluate, use and communicate information in
an efficient and effective manner. Information literate students are capable of doing these tasks
successfully. Further, the information literate students can solve problems, think and act critically and
creatively, while reasoning and making decisions effectively. According to the Bologna declaration,
successful learning and studying in higher education should involve students in deep learning (Asikainen
st
2014). The deep learning approach is the main root of the given 21 century skills; which are critical
thinking, creative thinking, collaboration, communication, information literacy, media literacy,
technology literacy, problem solving, decision making, lifelong learning, learning to learn and empathy.
Deep learning is achieved by student centered, project based, competency-based teaching learning
methods and product and process assessment methods. Deep learning and the lifelong learning
capabilities are based on the information literacy skills that the students are taught.

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