scholarly journals Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

2022 ◽  
Vol 12 (2) ◽  
pp. 2014
Author(s):  
Kamilia Hosny ◽  
Abeer El-korany
Keyword(s):  
Machine Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Student Assessment ◽  
Assessment Method ◽  
Assessment Model ◽  
Clustering Methods ◽  
Proposed Model ◽  
Data Driven Approach

<p>Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.</p>

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

The Learning Style-Based Adaptive Learning System Architecture

2015 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

scholarly journals Development of a Decision Support Model Based on Machine Learning for Applying Greenhouse Gas Reduction Technology

2020 ◽  
Vol 12 (9) ◽  
pp. 3582
Author(s):  
Sungwoo Lee ◽  
Sungho Tae
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Greenhouse Gas ◽  
Ghg Emissions ◽  
Assessment Method ◽  
Economic Benefits ◽  
Environmental Benefits ◽  
Assessment Model ◽  
Decision Support Model ◽  
Ghg Reduction

Multiple nations have implemented policies for greenhouse gas (GHG) reduction since the 21st Conference of Parties (COP 21) at the United Nations Framework Convention on Climate Change (UNFCCC) in 2015. In this convention, participants voluntarily agreed to a new climate regime that aimed to decrease GHG emissions. Subsequently, a reduction in GHG emissions with specific reduction technologies (renewable energy) to decrease energy consumption has become a necessity and not a choice. With the launch of the Korean Emissions Trading Scheme (K-ETS) in 2015, Korea has certified and financed GHG reduction projects to decrease emissions. To help the user make informed decisions for economic and environmental benefits from the use of renewable energy, an assessment model was developed. This study establishes a simple assessment method (SAM), an assessment database (DB) of 1199 GHG reduction technologies implemented in Korea, and a machine learning-based GHG reduction technology assessment model (GRTM). Additionally, we make suggestions on how to evaluate economic benefits, which can be obtained in conjunction with the environmental benefits of GHG reduction technology. Finally, we validate the applicability of the assessment model on a public building in Korea.

scholarly journals SCIENCE EDUCATION ADAPTIVE LEARNING SYSTEM AS A COMPUTER-BASED SCIENCE LEARNING WITH LEARNING STYLE VARIATIONS

2018 ◽  
Vol 17 (4) ◽  
pp. 711-727 ◽  
Author(s):  
Zulfiani Zulfiani ◽  
Iwan Permana Suwarna ◽  
Sujiyo Miranto
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Science Learning ◽  
Learning System ◽  
Learning Material ◽  
Learning Approaches ◽  
Slow Learners ◽  
Computer Based ◽  
Adaptive Learning System

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers’ limitation in serving all students’ learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL) as a computer-based science learning media by accommodating students’ learning style variations. The research method used is a mixed method at junior high schools in Tangerang Selatan. The final product of the research is a special learning media appropriate to students’ visual, aural, read/write and kinesthetic learning styles. The uniqueness of the media is its form of integrated science materials, accommodating fast and slow learners, and appropriate to their learning styles. ScEd-Adaptive Learning System as a developed computer-based science learning media was declared as good and valid by four media experts and five learning material experts. ScEd-ALS for kinesthetic style has a high effectivity to improve students learning mastery (100%), consecutively aural (63%), read/write (55%), and visual (20%). This media development can be continued with the Android version or iOS to make it more operationally practical. Keywords: adaptive learning system, science learning media, computer-based instruction, learning style.

scholarly journals Android-Based Expert System Application to Determine Math Learning Styles of Elementary School Students in Surakarta

2020 ◽  
pp. 65-70
Author(s):  
Rayung Wulan ◽  
Achmad Sarwandianto ◽  
Nur Alamsyah ◽  
Aulia Ar Rakhman Awaludin
Keyword(s):  
Expert System ◽  
Learning Styles ◽  
Student Learning ◽  
Learning Style ◽  
System Application ◽  
Student Assessment ◽  
Internal Consistency Reliability ◽  
School Students ◽  
Student Learning Style ◽  
Student Learning Styles

This Android-based expert system application was created to assist teachers in analysing student learning styles and identification of student learning styles that are easy to transfer knowledge in schools. The instrument used was the results of a questionnaire to measure learning style variables and student assessment variables in receiving mathematics lessons. The Android-based expert system application was designed based on student learning style questionnaire, the questionnaire was validated, and internal consistency reliability, set the instrument items and then collected in the rules and decision trees. The results of the questionnaire were taken from 6 elementary schools in Surakarta. The inference method used in this calculation is the forward chaining method, looking at the results of the decision tree as outlined in the expert system application. The Android-based expert system application is very effective and efficient in analysing student learning styles.

scholarly journals Online Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic

2018 ◽  
Vol 7 (3.13) ◽  
pp. 76
Author(s):  
L Alfaro ◽  
C Rivera ◽  
J Luna-Urquizo ◽  
E Castañeda ◽  
F Fialho
Keyword(s):  
Fuzzy Logic ◽  
Learning Styles ◽  
Learning Style ◽  
User Behavior ◽  
Backpropagation Neural Network ◽  
User Interactions ◽  
Learning Platform ◽  
Proposed Model ◽  
E Learning ◽  
Learning Platforms

Individual Learning Style identification is an essential aspect in the development of intelligent or adaptive e-Learning platforms. Traditional methods are based on the application of questionnaires or psychological tests, which may not be the most appropriate in all cases. The proposed model is based on the analysis of user behavior through the study of their interactions within an e-Learning platform, using a multilayer Backpropagation Neural Network and Fuzzy Logic concepts, for the preprocessing of the inputs and the categorization of the outputs.                                                                              

scholarly journals Evaluation of Assessment Method to Incorporate Graduate Attributes in Building Surveying Module

2016 ◽  
Vol 4 (2) ◽  
pp. 118-171
Author(s):  
Sandeep Chowdhry ◽  
Celine Garnier
Keyword(s):  
Learning Styles ◽  
Learning Strategies ◽  
Academic Staff ◽  
Data Gathering ◽  
Assessment Method ◽  
Assessment Model ◽  
Graduate Attributes ◽  
Assessment Strategy ◽  
Assessment Approach ◽  
Current Assessment

This study aims at finding out whether an alternate assessment strategy in Sustainable Developments module to improve the student’s employability skills and qualities. The exploratory studies, quantitative and qualitative questions acted as a data gathering instruments. The findings showed a need to change the current assessment strategy for Sustainable Developments module, proposed a new assessment approach and evaluated it. The research infers the studied institution should encourage academic staff to get familiar with the effective learning strategies, students learning styles and how to assess an assessment plan with graduate attributes model. A suggested direction for further research is to create an assessment model based on the students learning styles, assessment strategies and the workload information.

scholarly journals Personalization of an educational scenario of a learning activity according to the learning styles model David Kolb

2020 ◽  
Vol 5 (3) ◽  
pp. 099-108
Author(s):  
Anoir Lamya ◽  
Zargane Kawtar ◽  
Erradi Mohamed ◽  
Khaldi Mohamed
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Research Work ◽  
Learning System ◽  
Learning Activity ◽  
Learning Context ◽  
Intermediate Space ◽  
Training Courses

The personalization of learning remains a very important subject in research particularly with the progression of technology, it refers to a pedagogical approach that is located in an intermediate space where teaching and learning come together with devices personalized and adapted training courses for the different learner profiles in a social learning context. We offer a general approach to the personalization of teaching scenarios during the different types of teaching activities and taking consideration the learning styles of the learners and based on the Kolb learning style model. Our research work aimed at developing a personalized and adaptive learning system to meet the needs of learners and adequate with their preferences and profiles all throughout the learning process offered by the system by making a correspondence with the suitable pedagogical scenario with each profile and each activity.

scholarly journals Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

2019 ◽  
Vol 14 (3) ◽  
pp. 419-436 ◽  
Author(s):  
Yuhe Wang ◽  
Peili Qiao ◽  
Zhiyong Luo ◽  
Guanglu Sun ◽  
Guangze Wang
Keyword(s):  
Control System ◽  
Reliability Assessment ◽  
Quantitative Information ◽  
Assessment Method ◽  
Assessment Model ◽  
Rule Base ◽  
Industrial Control System ◽  
Industrial Control ◽  
Proposed Model ◽  
Belief Rule Base

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.

scholarly journals An Analysis of Personalized Learning Opportunities in 3D VR

2021 ◽  
Vol 3 ◽  
Author(s):  
Ildikó Horváth
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Test Performance ◽  
Information Acquisition ◽  
Performance Metrics ◽  
Test Group ◽  
Personalized Learning ◽  
Task Type ◽  
Control Group

Due to its constantly developing technological background, VR and AR technology has been gaining increasing popularity not just in industry or business but in education as well. Research in the field of Cognitive Infocommunications (CogInfoCom) shows that using existing digital technologies, online collaboration and cooperation technologies in 3D VR supports cognitive processes, including the finding, processing, memorization and recalling of information. 3D VR environments are also capable of providing users with a much higher level of comprehension when it comes to sharing and interpreting digital workflows. The paper presents a study carried out with the participation of 90 students. The aim of this study is to investigate how the application of 3D VR platforms as personalized educational environments can also increase VR learning efficiency. Besides considering participants’ test performance, metrics such as results on visual, auditory and reading-based learning tests for information acquisition, as well as responses on Kolb’s learning styles questionnaires are taken into consideration. The participants’ learning styles, information acquisition habits were also observed, allowing us to create and offer a variety of learning pathways based on a variety of content types in the 3D VR environment. The students within the study were divided into two groups: a test group receiving personalized training in the MaxWhere 3D VR classroom, and a control group that studied in a general MaxWhere 3D VR space. This research applies both quantitative and qualitative methods to report findings. The goal was to create adaptive learning environments capable of deriving models of learners and providing personalized learning experiences. We studied the correlation between effectiveness of the tasks and Kolb’s learning styles. The study shows the major importance of choosing the optimal task type regarding each Kolb learning style and personalized learning environment. The MaxWhere 3D spaces show a high potential for personalizing VR education. The non-intrusive guiding capabilities of VR environments and of the educational content integrated in the 3D VR spaces were very successful, because the students were able to score 20 percent higher on the tests after studying in VR than after using traditional educational tools. Students also performed the same tasks with 8-10 percent faster response times.

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