Classification of Students' Mathematics Learning Achievement on Bloom's Taxonomy-Based Serious Game Using Ordinal Logistic Regression

2020 ◽  
Author(s):  
I.N. Sukajaya ◽  
Darin Sabrina ◽  
I. Suharta
Keyword(s):  
Logistic Regression ◽  
Mathematics Learning ◽  
Bloom's Taxonomy ◽  
Ordinal Logistic Regression ◽  
Serious Game ◽  
Learning Achievement ◽  
Bloom’S Taxonomy

scholarly journals Aplikasi Ranah Kognitif Anderson & Krahthwohl dalam Pengajaran dan Pembelajaran Pantun di Sekolah Dasar

2021 ◽  
Vol 3 (3) ◽  
pp. 110-118
Author(s):  
Ros Anita Kartini Mohamed ◽  
Abdul Halim Ali ◽  
Muhammad Nasir
Keyword(s):  
Elementary Schools ◽  
Primary Schools ◽  
Teaching And Learning ◽  
Bloom's Taxonomy ◽  
Affective Domain ◽  
School Level ◽  
Cognitive Domain ◽  
Educational Objectives ◽  
Bloom’S Taxonomy

Taksonomi adalah klasifikasi benda mengikut ciri-ciri tertentu. Taksonomi dalam bidang pendidikan digunakan untuk mengklasifikasikan tujuan pendidikan, penyusunan penilaian dan kurikulum. Bloom telah mengkategorikan tiga ranah dalam pembelajaran, yaitu; ranah kognitif, ranah afektif dan ranah psikomotor. Taksonomi Bloom fokus pada terminologi (1) pengetahuan; (2) pemahaman; (3) penerapan; (4) analisis; (5) sintesis; dan (6) evaluasi. Sedangkan terminolginya berubah dengan adanya Taksonomi Revisi pada tahun 2001 oleh Anderson & Krathwohl dengan terminologi (1) mengingat; (2) memahami; (3) mengaplikasikan; (4) menganalisis; (5) menilai; dan (6) mencipta. Terminologi ini berubah dengan mempertimbangkan keperluan holistik agar lebih mudah dalam penerapannya oleh guru di sekolah. Fokus utama makalah ini adalah membahas ranah kognitif Revisi Anderson & Krathwohl 2001 dan penerapannya dalam pengajaran dan pembelajaran pantun di sekolah dasar.   Anderson & Krahthwohl Cognitive Applications in Teaching and Learning Pantun in Elementary Schools Abstract: Taxonomy is the classification of things according to certain characteristics. Taxonomy in education is used to classify educational objectives, assessment and curriculum preparation. Bloom has categorized three domains in learning, namely; cognitive domain, affective domain and psychomotor domain. Bloom’s taxonomy focuses on the terminology of (1) knowledge; (2) understanding; (3) application; (4) analysis; (5) synthesis; and (6) evaluation. While the terminology changed with the introduction of the Revised Taxonomy in 2001 by Anderson & Krathwohl with the terminology (1) recalling; (2) understand; (3) apply; (4) analyze; (5) evaluate; and (6) create. This terminology changes by considering the holistic need to make it more relevant in its application by teachers at the school level. The main focus of this paper is a discussion on the cognitive domain of the 2001 Anderson & Krathwohl Revision and its application in the teaching and learning of verse in primary schools. Keywords: Bloom's Taxonomy, Cognitive Area, Poetry, Revised Taxonomy.

scholarly journals A NEW APPROACH OF LEARNERS' ASSESSMENT USING BLOOM'S TAXONOMY-BASED SERIOUS GAME

Kursor ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 51 ◽  
Author(s):  
I.N Sukajaya
Keyword(s):  
Elementary School ◽  
Alternative Assessment ◽  
Bloom's Taxonomy ◽  
User Acceptance ◽  
Serious Game ◽  
Cognitive Domain ◽  
Acceptance Test ◽  
Bloom’S Taxonomy ◽  
5Th Grade ◽  
Elementary School Learners

This paper describes: a scenario, agent specification, mapping of knowledge domain, an implementation of Bloom's taxonomy-based serious game (BoTySeGa), and players' response against the game. The development of BoTySeGa is pursued to the availability of an alternative assessment tool for learning in elementary school. It considers aspects: game knowledge, subject matter of parallelogram for 5th grade elementary school learners, and cognitive domain of Bloom's taxonomy. BoTySeGa's level of challenge is structured accommodates cognitive domain of Bloom for elementary school learners (knowledge, comprehension, application). To make sure that all functions and features work well; we conducted user acceptance test against the game prototype. We also took players' response to BoTySeGa utilizing five-points Likert-type of questionnaire. The questions are distributed in 15 items. User acceptance testing involving 85 learners of 5th grade elementary school shows that BoTySeGa has fulfilled the learning assessment requirement. With the response score ranged from 5 to 75; it is found that the average score of players' response to the implementation of BoTySeGa in learning is 59.93. This response value falls within "Positive" category.

scholarly journals Perbandingan Analisis Diskriminan dan Analisis Regresi Logistik Ordinal dalam Prediksi Klasifikasi Kondisi Kesehatan Bank

2017 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Fajri Zufa ◽  
Sigit Nugroho ◽  
Mudin Simanihuruk
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Prediction Accuracy ◽  
Secondary Data ◽  
Ordinal Logistic Regression ◽  
Capital Adequacy ◽  
Ordinal Logistic Regression Analysis

The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score obtained, it can be said that discriminant analysis is better in predicting the classification of bank conditions in Indonesia than that of ordinal logistic regression analysis. Discriminant analysis has the average prediction accuracy of 80%, while ordinal logistic regression analysis has the average prediction accuracy of 74,38%.

scholarly journals Descriptive study of the kinds of questions asked by novice teacher on mathematics learning process

2017 ◽  
Vol 2 (1) ◽  
pp. 209
Author(s):  
Labiba Zahra ◽  
Tri Atmojo Kusmayadi ◽  
Budi Usodo
Keyword(s):  
Learning Process ◽  
Novice Teacher ◽  
Mathematics Learning ◽  
Descriptive Analysis ◽  
Bloom's Taxonomy ◽  
Structured Interview ◽  
Rhetorical Question ◽  
Revised Bloom's Taxonomy ◽  
Bloom’S Taxonomy ◽  
Main Activity

<p class="Abstract">This study described the kinds of a question asked by novice teacher on mathematics learning process of senior high school. The data were collected by passive participation observation and a semi-structured interview. The validity of data was obtained through the triangulation of method, triangulation of time and member check. The data analysis technique used in this study is descriptive analysis. The result of this study showed that at the preliminary activity, the question asked by the novice teacher based on objective only compliance question. At the main activity, the novice teacher asked compliance question, prompting question, probing question and sometimes rhetorical question. The kind of questions based on the cognitive dimension of Revised Bloom’s Taxonomy that was asked by novice teacher only remembering question. At the main activity, the novice teacher asked the question of remember, understand, apply, analyse and evaluate. At the closing activity, the teacher does not ask the question based on objective and Revised Bloom’s Taxonomy. </p>

scholarly journals Classification of Familial Hypercholesterolaemia Using Ordinal Logistic Regression

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Muhammad Hamizan Jamaludin ◽  
Yap Bee Wah ◽  
Hapizah Mohd Nawawi ◽  
Chua Yung-An ◽  
Marshima Mohd Rosli ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Familial Hypercholesterolaemia ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Information Criterion ◽  
Physical Symptoms ◽  
Ordinal Logistic Regression ◽  
Link Function ◽  
Lipid Clinic

Familial hypercholesterolaemia (FH) is a genetic disease that causes the elevation of low-density lipoprotein cholesterol (LDL-C), which subsequently leads to premature coronary heart disease (CHD). Features which have been reported to be associated with FH include lipids level, tendon xanthomata, and history of CHD. The Ordinal Logistic Regression model using the classification of FH patients with the Dutch Lipid Clinic Network Criteria (DLCN) as the dependent variable (where 1=Possible, 2=Probable, 3=Definite) was developed and evaluated for different types of link functions. The FH patients (n = 449) were recruited from health screening programmes conducted in hospitals and clinics in Malaysia from 2010 to 2018. Results indicate there is a significant association between FH categories with demographic factors (ethnicity and smoking) and physical symptoms (corneal arcus and xanthomata). The Ordinal Logistic Regression using Cauchit link function has lower Akaike Information Criterion (AIC) value, higher Nagelkerke's R-Square and classification accuracy compared to Probit and Logit link function, diastolic blood pressure, corneal arcus and xanthomata were found to be significant covariates of FH.

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