scholarly journals WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom’s Taxonomy

2016 ◽  
Vol 11 (04) ◽  
pp. 142 ◽  
Author(s):  
Kithsiri Jayakodi ◽  
Madhushi Bandara ◽  
Indika Perera ◽  
Dulani Meedeniya
Keyword(s):  
Language Processing ◽  
Evaluation Process ◽  
Bloom's Taxonomy ◽  
Cosine Similarity ◽  
Stop Word ◽  
Bloom’S Taxonomy ◽  
Similarity Algorithm ◽  
Question Category ◽  
Rule Set ◽  
Derived Rules

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom’s taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used before generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom’s taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of the evaluation process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.

Six Sigma learning evaluation model using Bloom’s Taxonomy

2018 ◽  
Vol 9 (1) ◽  
pp. 156-174 ◽  
Author(s):  
Gabriela Fonseca Amorim ◽  
Pedro Paulo Balestrassi ◽  
Rapinder Sawhney ◽  
Mariângela de Oliveira-Abans ◽  
Diogo Leonardo Ferreira da Silva
Keyword(s):  
Six Sigma ◽  
Conceptual Knowledge ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Bloom's Taxonomy ◽  
Content Type ◽  
Learning Evaluation ◽  
Bloom’S Taxonomy ◽  
Innovative Learning ◽  
Training Quality

Purpose This paper aims to propose a learning evaluation model for Green Belts and Black Belts at the training level. A question bank has been developed on the basis of Bloom’s learning classification and applied to a group of employees who were being trained in Six Sigma (SS). Their results were then used to decide on the students’ approval and to guide the instructor’s plan of teaching for the next classes. Design/methodology/approach An action research has been conducted to develop a question bank of 310 questions based on the revised Bloom’s Taxonomy, to implement the evaluation model, and to apply it during the SS training. Findings The evaluation model has been designed so that the students do not proceed unless they have acquired the conceptual knowledge at each step of the DMAIC (Define, Measure, Analyze, Improve and Control) roadmap. At the end of the evaluation process, the students’ results have been analyzed. The number of mistakes in all stages of DMAIC was equal, implying that the training was uniform the entire roadmap. However, the opposite happened in each of the Bloom’s Taxonomy levels, showing that some skills need to be better stimulated by the instructor than others. Research limitations/implications The learning evaluation model proposed in this paper has been applied to a group of 70 employees who were being trained in SS at a Brazilian aircraft manufacturer. The data have been analyzed using Microsoft Excel® and Minitab® 17 Statistical Software. Originality/value Despite the abundance of courses offering the SS Green Belt and Black Belt certifications, there is no standard evaluation to ensure the training quality. Thus, this paper proposes an innovative learning evaluation model.

scholarly journals Generating Indonesian Question Automatically Based on Bloom’s Taxonomy Using Template Based Method

2018 ◽  
pp. 145-152 ◽  
Author(s):  
Selvia Ferdiana Kusuma ◽  
Rinanza Zulmy Alhamri
Keyword(s):  
Learning Process ◽  
Evaluation Process ◽  
Bloom's Taxonomy ◽  
Field Evaluation ◽  
Final Evaluation ◽  
Generation Process ◽  
New Materials ◽  
Question Generation ◽  
Identification Process ◽  
Bloom’S Taxonomy

In education field, evaluation is needed to know the extent to which the learning process has been done. The evaluation process can be done through the provision of questions with varying degrees of difficulty. However, making questions with varying degrees of difficulty is not easy. Someone must understand the whole new materials to make the question. If there are a lot of materials, it takes a little time to change them to be a question. Therefore, it is necessary to automate the question generation process, in order to facilitate and accelerate the question generation process. This research introduces a template-based method to generate questions based on New Bloom's Taxonomy. There were 4 stages in this research, dataset collection, pattern identification process, question generating process & classification, and final evaluation process result. The dataset consists of 60 samples of paragraphs that derived from 9 courses of study courses Informatics Engineering. The 60 paragraphs produced 278 sentences and 654 questions. The proposed method is capable of producing an accuracy of 81.65% to generate questions using New Bloom's Taxonomy classification. So it can be concluded that the proposed method can be used to generate questions with varying difficulty levels in accordance with New Bloom's Taxonomy.

scholarly journals Differential Hiring using a Combination of NER and Word Embedding

2020 ◽  
Vol 9 (1) ◽  
pp. 1344-1349
Keyword(s):  
Language Processing ◽  
Human Life ◽  
Named Entity Recognition ◽  
Word Embedding ◽  
Cosine Similarity ◽  
Entity Recognition ◽  
Named Entity ◽  
Similarity Algorithm ◽  
Almost All ◽  
Processing Techniques

In this data-driven world, AI is being used in almost all the tasks to automate processes and make human life more comfortable. One such industry where Artificial Intelligence (AI) importance is growing is the recruiting industry. This paper aims to propose a new and a better method to match the most suitable talent to jobs, which has been incorporated using two methods – suggesting top resumes to a job opening from a talent pool to a recruiter, recommending top jobs which match to a candidate based on the candidate's resume. Natural Language Processing techniques have been used in implementing this approach – Named Entity Recognition (NER), Word embedding model, and Cosine similarity using which a resume and job will be matched. The NER model is used to extract useful entities from documents, which is enhanced by the word2vec model by making the system more generic and the similarity is calculated using the cosine similarity algorithm.

scholarly journals Chatbot in Bahasa Indonesia using NLP to Provide Banking Information

2020 ◽  
Vol 14 (1) ◽  
pp. 91
Author(s):  
Abidah Elcholiqi ◽  
Aina Musdholifah
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Product Information ◽  
Cosine Similarity ◽  
User Input ◽  
Similarity Algorithm ◽  
Bahasa Indonesia ◽  
User Queries

FAQs are mostly provided on the company's website to inform their service and product. It's just that the FAQ is usually less interactive and presents too much information that is less practical. Chatbot can be used as an alternative in providing FAQ. In this study, chatbots were developed for BTPN in providing information about their products, namely Jenius. Chatbot developed utilizes natural language processing so that the system can understand user queries in the form of natural language. The cosine similarity algorithm is used to find similarities between queries and patterns in the knowledge base. Patterns with the highest cosine values are considered to be most similar to user queries. It's just that, this algorithm does not pay attention to the structure of the sentence so that it adds checking the structure of the sentence with the parse tree to give weight to the pattern. This chatbot application has been tested by 10 users and it was found that the suitability of the answers with user input was 84%. Therefore the chatbot developed can be used by BTPN to provide Jenius product information to consumers more interactively and practically.

Bloom's Taxonomy in Action

MedEdPORTAL ◽  
2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeanne Schlesinger ◽  
Adam Persky ◽  
Faculty Learning Community
Keyword(s):  
Bloom's Taxonomy ◽  
Bloom’S Taxonomy

READING EXERCISES FOR VIII GRADE STUDENTS BASED ON BLOOM’S TAXONOMY

2017 ◽  
Vol 6 (3) ◽  
Author(s):  
Intan Permata Sari And Indra Hartoyo
Keyword(s):  
Data Analysis ◽  
Lower Order ◽  
Bloom's Taxonomy ◽  
Higher Order Thinking ◽  
Thinking Skills ◽  
Higher Order ◽  
Critical Thinking Skills ◽  
Thinking Skill ◽  
Level Of Education ◽  
Bloom’S Taxonomy

This study is aimed at (1) analyzing reading exercises based Bloom’s taxonomy for VIII grade in English on Sky textbook. (2) Found the distribution of the lower and higher order thinking skill in reading exercises. (3) To reason for level reading exercises. After analyzed the data, the result of the data analysis also infers that the six levels of Bloom’s taxonomy in reading exercises weren’t applied totally. The creating skill doesn’t have distribution in reading exercise, and the understanding – remembering level more dominant than another levels. The distribution of the higher order thinking level was lower than the lower order thinking level and the six levels are not appropriate with the proportion for each level of education based Bloom’s taxonomy, such as the distribution of the creating level in the reading exercise must be a concern because no question that belong to the creating level. It was concluded that reading exercises in English on Sky textbook cannot improve students' critical thinking skills for VIII grade.

Tasks of microteaching in the context of Bloom's taxonomy

2020 ◽  
Vol 1(11) ◽  
pp. 183-186
Author(s):  
Renata Orosova ◽  
◽  
Katarina Petrikova ◽  
Volodymyr Starosta ◽  
◽  
...  
Keyword(s):  
Bloom's Taxonomy ◽  
Bloom’S Taxonomy
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