scholarly journals A data-driven approach for the identification of misconceptions in step-based tutoring systems

2020 ◽  
Author(s):  
Joice Cazanoski Gomes ◽  
Patricia A. Jaques
Keyword(s):  
Problem Solving ◽  
Learning Environments ◽  
Intelligent Tutoring System ◽  
Clustering Algorithms ◽  
Linear Equations ◽  
Automatic Identification ◽  
Special Importance ◽  
Computing Platform ◽  
Data Driven Approach ◽  
Intelligent Learning Environments

Math errors are an important part of the learning process. For this reason, diagnosing them can help teachers and intelligent learning environments to choose the most appropriate type of assistance for the learner. In particular, the identification of learner misconceptions can be of special importance because they represent a misunderstanding of math concepts. In this context, this paper proposes the use of clustering algorithms to automatically identify algebra misconceptions from learners' algebra problem-solving steps in an intelligent learning environment. The computing platform is an intelligent tutoring system that assists students when solving linear equations step by step, by giving minimal and error feedback. The results showed that the model was able to identify some misconceptions already known in the literature, which illustrates the appropriateness of our approach. The automatic identification of misconceptions can help in the identification of new conceptual misunderstanding from large datasets of math problem solving, besides give valuable information for teachers and intelligent learning environments to adapt their instruction and assistance.

Assessing and tracking students’ problem solving performances in anchored learning environments

2007 ◽  
Vol 57 (4) ◽  
pp. 529-552 ◽  
Author(s):  
Brian A. Bottge ◽  
Enrique Rueda ◽  
Jung Min Kwon ◽  
Timothy Grant ◽  
Perry LaRoque
Keyword(s):  
Problem Solving ◽  
Learning Environments

scholarly journals ANALISIS KEMAMPUAN PEMECAHAN MASALAH PADA SOAL CERITA MATEMATIKA BERDASARKAN TEORI POLYA DITINJAU DARI ADVERSITY QUOTIENT

2020 ◽  
Vol 2 (1) ◽  
pp. 105-128
Author(s):  
Novita Nurul Aini ◽  
Mohammad Mukhlis
Keyword(s):  
Problem Solving ◽  
Data Collection ◽  
Mathematical Reasoning ◽  
Linear Equations ◽  
Learning Goals ◽  
Problem Solving Skills ◽  
Data Presentation ◽  
Validity Test ◽  
Descriptive Research ◽  
Qualitative Descriptive

One of the studen learning goals mathematics is mathematical reasoning for outcomes training student to solve the problems. One of the problems faced by students is word questions. There are several students responses in dealing with word question which is known as Adversity Quotient. This research aims to describe the students' problem solving skills in system of three-variable linear equations subject based on Polya's theory in terms of Adversity Quotient. This is a qualitative descriptive research with three subjects of students class X IPA 1 SMAN Arjasa Jember, there are one climber student, one camper student and one quitter student. These subjects took purposive sampling with consideration according to the results of questionnaire scores that meet each of the criteria of Adversity Quotient. Data collection techniques used were questionnaires, tests, interviews and observations. The validity test used is technical triangulation. Data analyzed through data condensation, data presentation and conclusion drawing. The results showed that student with the type of climber was able to meet all the indicators of problem solving in the problem of the word questions which included indicators of understanding the problem, planning the solution, carrying out the plan of solving and re-checking. Camper type student met all indicators of problem solving except at the re-checking stage. Quitter type student in completing word questions met the stage of understanding the problem and planning the solution, while the stage of carrying out the plan and re-checking is not fulfilled by the quitter student.

scholarly journals Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing

2020 ◽  
Author(s):  
Shreshth Tuli ◽  
Shikhar Tuli ◽  
Rakesh Tuli ◽  
Sukhpal Singh Gill
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Design Strategies ◽  
Potential Threat ◽  
Practical Applications ◽  
Computing Platform ◽  
Data Driven Approach ◽  
Cloud Computing Platform ◽  
The Government ◽  
Improved Model

AbstractThe outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policy to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This can be deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications. Predicted curves for some of the most affected countries can be seen at https://collaboration.coraltele.com/covid/.

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