Indian Institute of Technology Kharagpur (INDIA)
About this paper:
Appears in: EDULEARN16 Proceedings
Publication year: 2016
Pages: 4593-4596
ISBN: 978-84-608-8860-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2016.2109
Conference name: 8th International Conference on Education and New Learning Technologies
Dates: 4-6 July, 2016
Location: Barcelona, Spain
Specification of Learning Objectives in a curriculum is aimed at achieving the desired learning outcomes. The purpose of such specification is to have some standardization of the teaching-learning process so that the instructions provided meet the learning objectives. A clear, unambiguous Learning Objective improves the interaction between the instructor and the learners, and helps the learner to know what skills and knowledge i.e. the learning outcome, is expected of him or her after completion of the course. In order to help instructors in identifying the Learning Objectives and its corresponding cognitive level, Benjamin Bloom established a hierarchy of educational objectives for categorizing the level of questions that usually appears in educational courses, commonly referred as Bloom’s taxonomy. If the Learning Objectives are developed appropriately on the basis of Bloom’s taxonomy, it is expected that the learning outcomes of the syllabus will be satisfied. In this proposed work, we classify and map a given set of Learning Objectives to its appropriate Learning Materials based on Bloom’s Taxonomy of Cognitive domain.

In this paper, first we classify a given set of Learning Objectives to its appropriate level of Bloom’s Taxonomy of Cognitive domain. This is done by identifying the Bloom’s Taxonomy action verbs and its relation with the subject topic keywords. Then we check for each Learning Objective whether there is a Learning Material available from a given document set. Now for each Learning Objective, we rank the available Learning Material on the basis of the action verbs of Bloom’s Taxonomy of Cognitive domain as well as subject topic keyword, thereby creating a list of Learning Material documents ranked by the frequency of the number of Bloom’s Taxonomy action verbs and its related subject topic present in the text of the Learning Material.

In the end, we appropriately show a list of most related documents available for a given Learning Objective, that can satisfy the Bloom’s Taxonomy level of the given Learning Objective. Our proposed methodology for the above process is based on keywords extraction using rule based approach, machine learning and data mining to automate the entire process. We are currently using data sets of secondary school standard physics for testing purposes.
Learning Objective, Bloom's revised taxonomy of cognitive domain.