Discovering Bloom Taxonomic Relationships between Knowledge Units Using Semantic Graph Triangularity Mining

Inferring Bloom's Taxonomy among knowledge units is important and challenging. This paper proposes a novel method that can identify the revised Bloom's Taxonomy levels among knowledge units in the semantic cognitive graph (SCG) by using a graph triangularity. The method determines significant relationships among knowledge units by utilizing triangularity of knowledge units in the computer science domain. We share an experiment that evaluates and validates the method on three textbooks. The performance analysis shows that the method succeeds in discovering the hidden associations among knowledge units and classifying them.

[1]  Marti A. Hearst.  Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[2]  Terry Scott.  Bloom's taxonomy applied to testing in computer science classes , 2003 .

[3]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[4]  Qinghua Zheng,et al.  Mining learning-dependency between knowledge units from text , 2011, The VLDB Journal.

[5]  Javed I. Khan,et al.  Conceptualize the Domain Knowledge Space in the Light of Cognitive Skills , 2015, CSEDU.

[6]  Ah-Hwee Tan,et al.  Mining semantic networks for knowledge discovery , 2003, Third IEEE International Conference on Data Mining.

[7]  J. Stanley,et al.  Book Review: Taxonomy of Educational Objectives, The Classification of Educational Goals, Handbook I: Cognitive Domain , 1957 .

[8]  John Lyons,et al.  Linguistic Semantics: An Introduction , 1995 .

[9]  Danqi Chen,et al.  A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.

[10]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[11]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[12]  Benjamin Kuipers,et al.  Walk the Talk: Connecting Language, Knowledge, and Action in Route Instructions , 2006, AAAI.

[13]  Ursula Fuller,et al.  Is Bloom's taxonomy appropriate for computer science? , 2006, Baltic Sea '06.

[14]  Francky Trichet,et al.  Axiom‐based ontology matching , 2009, Expert Syst. J. Knowl. Eng..

[15]  Benjamin S. Bloom,et al.  A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .

[16]  Dave Oliver,et al.  This Course Has A Bloom Rating Of 3.9 , 2004, ACE.

[17]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[18]  Sreenivas Gollapudi,et al.  Data mining for improving textbooks , 2012, SKDD.

[19]  Benjamin S. Bloom,et al.  Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .

[20]  Mary J. Pickard.  THE NEW BLOOMS TAXONOMY: AN OVERVIEW FOR FAMILY AND CONSUMER SCIENCES , 2007 .

[21]  Oren Etzioni,et al.  Identifying Relations for Open Information Extraction , 2011, EMNLP.

[22]  James Allan,et al.  Automatic Retrieval With Locality Information Using SMART , 1992, TREC.

[23]  Oren Etzioni,et al.  Open Language Learning for Information Extraction , 2012, EMNLP.

[24]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[25]  Phillip Bonacich,et al.  Eigenvector-like measures of centrality for asymmetric relations , 2001, Soc. Networks.

[26]  Oren Etzioni,et al.  What Is This, Anyway: Automatic Hypernym Discovery , 2009, AAAI Spring Symposium: Learning by Reading and Learning to Read.