Automated Classification of Computing Education Questions using Bloom’s Taxonomy

Zhang, James; Wong, Casey Philip; Giacaman, Nasser; Luxton-Reilly, Andrew

doi:10.1145/3441636.3442305

Cited by 24 publications

(15 citation statements)

References 22 publications

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“…Many studies are carried out for text and document classification with different word embedding techniques but utilizing the word embedding technique for classifying Bloom's cognitive level is very rare. The recent work by Zhang et al [22] used a deep learning approach to auto-classify the single question set with a better result. There is no word embedding technique used in this approach.…”

Section: Literature Studymentioning

confidence: 99%

Auto Classification of Blooms Cognitive Domain Using Word Embedding Deep Neural Network Classifier

PRAKASH¹,

DHAYANITHI²

2022

Preprint

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Section: Literature Studymentioning

confidence: 99%

Auto Classification of Blooms Cognitive Domain Using Word Embedding Deep Neural Network Classifier

PRAKASH¹,

DHAYANITHI²

2022

Preprint

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“…In previous studies, several researchers have tried to solve this problem using automatic ML-based and NLP-based techniques. The literature highlights two major approaches in this domain namely, Keyword-Based [1], [2], [29] and Text-Classification-Based [13], [19], [30]- [33].…”

Section: Automatic Classification Of Clos and Examination Questions Into Bloom's Taxonomymentioning

confidence: 99%

“…Zhang et. al in [19], applied machine learning algorithms to classify questions related to the computing education into the Bloom's taxonomy. The total questions were 504 and manually annotated by the education experts.…”

Section: ) Text-classification-based Approachmentioning

confidence: 99%

“…For human, to differentiate above CLOs or questions are easy to identify in their respective Bloom level but for machine using simple keyword-based approach it is erroneous. Therefore, the recent studies [8], [17]- [19] employed alternative machine learning-based approaches to classify the CLOs and questions. However, to the best of our knowledge, still these studies suffer from low accuracy [18].…”

Section: Introductionmentioning

confidence: 99%

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Bloom’s Learning Outcomes’ Automatic Classification Using LSTM and Pretrained Word Embeddings

2021

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Bloom's taxonomy is a popular model to classify educational learning objectives into different learning levels for three domains including cognitive, affective and psycho motor. Each domain is further detailed into different levels. The cognitive domain includes knowledge, comprehension, application, analysis, synthesis and evaluation levels. In educational institutions, designing course learning outcomes (CLOs) as per different levels of Bloom and mapping of assessment items on designed CLOs is an important task -every semester, faculty and administrators read thousands of statements to complete the tedious task of such mapping of CLOs and assessment items into Bloom's levels for an improved student learning. This paper proposes LSTM based deep learning model to perform classification of CLOs and assessment items in different levels of Bloom in cognitive domain. Although, there has been some attempts in the literature to automatically assign Bloom's taxonomy category using keywords-based approach but it suffers from the problem of low accuracy and overlapping of keywords. Initially, when we performed keywords-based approach on our datasets we achieved the overall accuracy of 55% for classification of CLOs and assessment items into Bloom's taxonomy. The proposed model predicts Bloom's level for CLO and assessment question item, respectively. The proposed model is simple in terms of the architecture as compared to other deep learning models reported in literature and achieves classification accuracy of 87% and 74% on CLOs and assessment question items, respectively. The proposed model obtained 3% increase in overall accuracy comparing to an existing study for the same task. To the best of our knowledge, this is first attempt towards applying deep learning on classifying educational objectives in Bloom's levels.

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“…Teachers use this taxonomy, for example in computer science education (Whalley et al, 2006;Thompson et al, 2008;Oliver et al, 2004), and our inspiration is from this revised version of the cognitive taxonomy. Machine learning has been applied to automatically classify questions (Mohammed and Omar, 2020;Zhang et al, 2021;Nafa et al, 2016) into Bloom's Taxonomy levels, but the taxonomy has not been applied to analyze or improve machine learning models themselves. We use it to help our model think about what it knows.…”

Section: Related Workmentioning

confidence: 99%

Comprehension Based Question Answering using Bloom’s Taxonomy

Sahu¹,

Cogswell²,

Divakaran³

et al. 2021

Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

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Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.

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Automated Classification of Computing Education Questions using Bloom’s Taxonomy

Cited by 24 publications

References 22 publications

Auto Classification of Blooms Cognitive Domain Using Word Embedding Deep Neural Network Classifier

Auto Classification of Blooms Cognitive Domain Using Word Embedding Deep Neural Network Classifier

Bloom’s Learning Outcomes’ Automatic Classification Using LSTM and Pretrained Word Embeddings

Comprehension Based Question Answering using Bloom’s Taxonomy

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