scholarly journals Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course

2017 ◽  
Vol 13 (6) ◽  
Author(s):  
Chungho Su
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Learning Path

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

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 Adaptive Learning Model Based on Ant Colony Algorithm

2019 ◽  
Vol 14 (01) ◽  
pp. 49 ◽  
Author(s):  
Rongxia Li
Keyword(s):  
Adaptive Learning ◽  
Ant Colony Algorithm ◽  
Learning Model ◽  
Learning Objects ◽  
Ant Colony ◽  
Learning Path ◽  
Knowledge Levels ◽  
Actual Learning ◽  
Learning Frameworks ◽  
Adaptive Online Learning

To better respond to people’s demands for multimedia learning, appropriate learn-ing paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves exist-ing learning frameworks to offer a set of knowledge paths that can cater to dif-fer7ent preferences, tastes, and knowledge levels of learners, no need for them to be aware of this. Based on the improved ant colony algorithm, an adaptive learn-ing system model that can satisfy learners’ demands is built herein with reference to the foraging approach of ants to traverse the paths, thereby to find the best learning path, while the classification method for some learning objects can de-termine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.

scholarly journals Mining Web Analytics Data for Information Wikis to Evaluate Informal Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 125
Author(s):  
Heba M. Ismail ◽  
Boumediene Balkhouche ◽  
Saad Harous
Keyword(s):  
Learning Environment ◽  
Informal Learning ◽  
Learning Theory ◽  
Recommendation System ◽  
Constructivist Learning ◽  
Web Analytics ◽  
Constructivist Learning Theory ◽  
Learning Path ◽  
Similar Information ◽  
The Impact

Information wikis and especially Wikipedia have become one of the most attractive environments for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently con-struct knowledge without being required to follow a predefined learning path in line with the constructivist learning theory. Link-based navigation and keyword-based search methods used on Wikipedia and similar information wikis suffer from many limitations. In our paper, we present an effective recommendation system that provides easier and faster access to relevant content on Wikipedia to support informal learning. In addition, we evaluate the impact of personalized content recommendations on informal learning from Wikipedia and show how web analytics data can be used to get an in-sight on informal learning in similar environments.

scholarly journals Adaptive Recommender System for an Intelligent Classroom Teaching Model

2019 ◽  
Vol 14 (05) ◽  
pp. 51 ◽  
Author(s):  
Hanhui Lin ◽  
Shaoqun Xie ◽  
Zhiguo Xiao ◽  
Xinxin Deng ◽  
Hongwei Yue ◽  
...  
Keyword(s):  
Information Technology ◽  
Adaptive Learning ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Learning Resources ◽  
Learning Resource ◽  
Teaching Process ◽  
Teaching Resources ◽  
Individualized Teaching ◽  
Intelligent Classroom

The development of information technology has facilitated the use of the intelligent classroom model supported by information technology to improve the college students’ comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation was proposed. First, the entire teaching process was divided into three stages which were used to combine teachers’ teaching and students’ learning. Then the key problems of the learning resources recommendation system was studied and a learning resource recommendation based on TR-LDA (Teaching Resources-Latent Dirichlet Allocation) was proposed and how to be achieved. Finally, the proposed intelligent classroom model was verified in practical teaching. Results show that the intelligent classroom model with adaptive learning resources recommendation can help to improve students’ learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.

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.

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