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The paper presents two automated approaches: labelled Latent Dirichlet Allocation (LDA) and BERT-based multi-class text classification. Experiments on 3000+ questions evaluate accuracy and key factors for determining cognition.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nSyaamantak Das  \nCentre for Educational Technology, Indian Institute of Technology Kharagpur, India  \nORCID: 0000-0001-9896-3312  \nShyamal Kumar Das Mandal  \nCentre for Educational Technology, Indian Institute of Technology Kharagpur, India  \nORCID: 0000-0002-4088-3173  \nAnupam Basu  \nDepartment of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India  \nNational Institute of Technology Durgapur, India  \nORCID: 0000-0002-1960-9225  \nReceived: 6 April 2020 Accepted: 21 May 2020  \nAbstract  \nCognitive learning complexity identification of assessment questions is an essential task in the domain of education, as it helps both the teacher and the learner to discover the thinking process required to answer a given question. Bloom’s Taxonomy cognitive levels are considered as a benchmark standard for the classification of cognitive thinking (learning complexity) in an educational environment. However, it was observed that some of the action verbs of Bloom’s Taxonomy are overlapping in multiple levels of the hierarchy, causing ambiguity about the real sense of cognition required. The paper describes two methodologies to automatically identify the cognitive learning complexity of given questions. The first methodology uses labelled Latent Dirichlet Allocation (LDA) as a machine learning approach. The second methodology uses the BERT framework for multi-class text classification for deep learning. The experiments were performed on an ensemble of 3000+ educational questions, which were based on previously published datasets along with the TREC question corpus and AI2 Biology How/Why question corpus datasets. The labelled LDA reached an accuracy of 83% while BERT based approach reached 89% accuracy. An analysis of both the results is shown, evaluating the significant factors responsible for determining cognitive knowledge.  \nKeywords: multi-class text classification, labelled LDA, pre-trained BERT (Bidirectional Encoder Representations from Transformers), question classification  \nINTRODUCTION  \nIn the field of education, it is essential to construct a cognitively well-balanced question paper. One of the standard approaches to solve this issue is the usage of Bloom’s Taxonomy (Bloom et al., 1956), created by Benjamin Bloom in the 1950s. Bloom’s Taxonomy classifies educational objectives and learning outcomes into multiple cognitive levels based on the complexity of the thinking behavior, which is required for successful completion of learning. The six levels are classified based on previous knowledge and skills such as (i) knowledge / remembering (ii) comprehension / understanding, (iii) applying, (iv) analyzing, (v) evaluating / synthesis, and (vi) creating. Each of these levels consists of several action verbs that depict the  \nthinking required. E.g. Define, Analyze, Compare etc. However, it was observed in the work of (Stanny, 2016) that these words (Bloom’s Taxonomy action verbs - (BTAV)) often co-occur in multiple levels, causing an ambiguity about the true sense of cognition. To overcome this problem, the proposed paper utilizes the effectiveness of multi-class text classification algorithms in machine learning and deep learning-based approaches as a methodology. The proposed paper uses the above methodologies to classify a given question to it’s most appropriate Bloom’s Taxonomy cognitive level.  \nAssessment questions consist of text sentences that are not cognitively structured. They also vary in multiple types. For example- (i) a question can be based on WH words such as What, Why, How etc. E.g., What factors could lead to the rise of a new species?; (ii) -a question can consist of only Bloom’s Taxonomy action verbs. E.g., Explain some of the important ideas of the above section in your own words.; (iii) -a question can contain both WH and cognitive level action verbs. E.g., Why do you thin","cbCaip3hy9oVzm6y","https://ap.wps.com/l/cbCaip3hy9oVzm6y","pdf",467233,14,"English","# Abstract\n# Introduction\n## Bloom’s Taxonomy and cognitive ambiguity\n## Assessment questions as unstructured short text\n## Multi-class classification and topic modelling with LDA\n## Dataset sources and evaluation setup","[{\"question\":\"Why is identifying cognitive learning complexity important for assessment questions?\",\"answer\":\"It helps teachers and learners understand the thinking process required to answer a question, enabling cognitively balanced question papers.\"},{\"question\":\"What problem does the paper address with Bloom’s Taxonomy action verbs?\",\"answer\":\"Action verbs from Bloom’s Taxonomy can overlap across multiple cognitive levels, making the true sense of required cognition ambiguous.\"},{\"question\":\"How do the proposed methods classify questions into cognitive levels?\",\"answer\":\"One method uses labelled Latent Dirichlet Allocation (LDA) for machine learning, and the other uses the BERT framework for multi-class text classification in deep learning.\"}]","Identification of Cognitive Learning Complexity of Assessment Questions - Using Multi-class Text Classification | PDF",1792089826,35]