Toward a Complete E-learning System Framework for Semantic Analysis, Concept Clustering and Learning Path Optimization

2012 ◽  
Author(s):  
Vincent Tam ◽  
Edmund Y. Lam ◽  
S.T. Fung
Keyword(s):  
Semantic Analysis ◽  
Learning System ◽  
Path Optimization ◽  
System Framework ◽  
Learning Path ◽  
Concept Clustering ◽  
E Learning ◽  
Analysis Concept

scholarly journals PRE-PROCESSED LATENT SEMANTIC ANALYSIS FOR AUTOMATIC ESSAY GRADING

Kursor ◽  
2017 ◽  
pp. 175 ◽  
Author(s):  
Ruth Ema Febrita ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Learning System ◽  
Semantic Meaning ◽  
Lexical Database ◽  
Java Programming ◽  
E Learning ◽  
Manual Processing ◽  
Essay Grading ◽  
High Order Thinking

In education, essay is considered as the best tool to evaluate student’s high order thinking and understanding. In the other hand, manual processing and grading essay answers by a teacher need much time and tending to subjectivity grading. Meanwhile automatic essay grading in e-learning system find the difficulties in comparing model or key answer to student’s answer because student’s can answer the question with so various way. That means a right answer also can be so various, for they have same semantic meaning. This paper proposed automatic essay grading using Latent Semantic Analysis. But before the texts being scored, they will be pre-processed using stop words removal and synonyms checking. Calibration process implemented for dealing with the various possible right answer and help to simplify the term matrix. Implementation of this approach using Java Programming Language and WordNet as lexical database for searching the synonyms of every given words. The accuracy obtained by this method is 54.9289%.

Generating Personalized Navigation Learning Path Based on Knowledge Map

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17
Author(s):  
Xiang Jun Huang ◽  
Chao Zhang ◽  
Qing Hua Zheng
Keyword(s):  
User Satisfaction ◽  
Information Overload ◽  
Rapid Development ◽  
Learning System ◽  
Knowledge Map ◽  
Learning Efficiency ◽  
Learning Path ◽  
Learning Instruction ◽  
E Learning ◽  
On Line

With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.

Learning Path Recommendation System for Programming Education Based on Neural Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 36-64 ◽  
Author(s):  
Tomohiro Saito ◽  
Yutaka Watanobe
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommendation System ◽  
Current Knowledge ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Programming Education ◽  
Learning Path ◽  
E Learning ◽  
Trained Teachers

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.

scholarly journals A New Learning Path Model for E-Learning Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 20-40
Author(s):  
David Brito Ramos ◽  
Ilmara Monteverde Martins Ramos ◽  
Isabela Gasparini ◽  
Elaine Harada Teixeira de Oliveira
Keyword(s):  
Student Behavior ◽  
Group Formation ◽  
Learning Systems ◽  
Learning System ◽  
Path Model ◽  
New Approach ◽  
Learning Path ◽  
E Learning ◽  
Learning Paths ◽  
Collaborative Activities

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.

scholarly journals The choice of customisation strategies in training: An overview of parameters and their systematisation

2021 ◽  
pp. 170-186
Author(s):  
Marina Anikieva
Keyword(s):  
Learning Process ◽  
Learning System ◽  
Learning Goals ◽  
Individual Student ◽  
Educational Strategies ◽  
Learning Materials ◽  
Educational Activities ◽  
Learning Path ◽  
Large Numbers ◽  
E Learning

This paper discusses the factors that determine the customisation of e-learning programmes. The process of customisation depends on many parameters, such as the objectives of the programme, the quantity and order of the learning materials, the personality and abilities of the student, and the resources within the learning system. Curriculum developers are able to put together these parameters in varying combinations, reflecting differing educational strategies. Because of this possibility it has become important to study how one can determine an appropriate strategy or learning path for any individual student. This is becoming particularly relevant because curriculum developers have to consider large numbers of already developed learning courses, modules, and technologies. One of the approaches to addressing this problem is the classification, or taxonomy, of customisation parameters. This paper reviews published material from highly-rated journals dealing with customisation of learning. As a result of this review the groups of customisation parameters are identified and a generalised scheme of grouped parameters, and their sequence, corresponding to the inner logic of the learning process are developed. This taxonomy allows the educational activities to be arranged so that learners can achieve their learning goals more efficiently.

scholarly journals Automatic Essay Scoring in E-learning System Using LSA Method with N-Gram Feature for Bahasa Indonesia

2018 ◽  
Vol 164 ◽  
pp. 01037
Author(s):  
Rico Setiadi Citawan ◽  
Viny Christanti Mawardi ◽  
Bagus Mulyawan
Keyword(s):  
Learning Outcomes ◽  
Semantic Analysis ◽  
Evaluation Process ◽  
Educational Process ◽  
Learning System ◽  
Assessment Process ◽  
E Learning ◽  
Essay Scoring ◽  
N Gram ◽  
Form Type

In the world of education, e-learning system is a system that can be used to support the educational process. E-learning system is usually used by educators to learners in evaluating learning outcomes. In the process of evaluating learning outcomes in the e-learning system, the form type of exam questions that are often used are multiple choice and short stuffing. For exam questions in the form of essays are rarely used in the evaluation process of educational because of the difference in the subjectivity and time consuming in the assessment process. In this design aims to create an automatic essay scoring feature on e-learning system that can be used to support the learning process. The method used in automatic essay scoring is Latent Semantic Analysis (LSA) with n-gram feature. The evaluation results of the design features automatic essay scoring showed that the accuracy of the average achieved in the amount of 78.65 %, 58.89 %, 14.91 %, 71.37 %, 64.49 % in the LSA unigram, bigram, trigram, unigram + bigram, unigram + bigram + trigram.

scholarly journals Optimal Hierarchical Learning Path Design With Reinforcement Learning

2020 ◽  
Vol 45 (1) ◽  
pp. 54-70
Author(s):  
Xiao Li ◽  
Hanchen Xu ◽  
Jinming Zhang ◽  
Hua-hua Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Model Free ◽  
Learning Path ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Efficient Learning ◽  
Skills Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

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