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.

Literature Survey for Applications of Artificial Neural Networks

2022 ◽  
pp. 669-682
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Towards Increasing the Efficiency of E-Learning Systems Using Recommendation System Approach

2020 ◽  
pp. 35-55
Author(s):  
Zameer Gulzar ◽  
L. Arun Raj ◽  
A. Anny Leema
Keyword(s):  
System Approach ◽  
Recommendation System ◽  
Learning Systems ◽  
Learning Objectives ◽  
Learning System ◽  
E Learning ◽  
Flexible Delivery ◽  
Algorithmic Techniques ◽  
Information Optimization ◽  
Almost All

Data mining approaches have been tried in e-learning systems for information optimization and knowledge extraction to make decisions. In recent years, the recommendation system has gained popularity in every field be it e-commerce, entertainment, sports, healthcare, news, etc. However, in e-learning system, the recommender systems were not effectively utilized in comparison to other domains and thus emerged as a bottleneck for almost all e-learning systems for not offering flexible delivery of the learning resources. Current e-learning systems lack personalization features, and the information is presented in a static way despite their varying learning objectives and needs. The aim of recommender system is to personalize the information with respect to learner interest. The objective of this study is to highlight various algorithmic techniques that can be used to improve information retrieval process to provide effective recommendations to learners for improving their performance and satisfaction level.

scholarly journals Code Edit Recommendation Using a Recurrent Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9286
Author(s):  
Seonah Lee ◽  
Jaejun Lee ◽  
Sungwon Kang ◽  
Jongsun Ahn ◽  
Heetae Cho
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Software Evolution ◽  
Rule Mining ◽  
Mining Technique ◽  
Recommendation Accuracy ◽  
The Given

When performing software evolution tasks, developers spend a significant amount of time looking for files to modify. By recommending files to modify, a code edit recommendation system reduces the developer’s navigation time when conducting software evolution tasks. In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN). CERNN forms contexts that maintain the sequence of developers’ interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution task. We evaluated our method by comparing it with the state-of-the-art method MI-EA that was developed based on the association rule mining technique. The result shows that our proposed method improves the average recommendation accuracy by approximately 5% over MI-EA (0.64 vs. 0.59 F-score).

scholarly journals Deep-Sequence–Aware Candidate Generation for e-Learning System

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1454
Author(s):  
Aziz Ilyosov ◽  
Alpamis Kutlimuratov ◽  
Taeg-Keun Whangbo
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
Learning System ◽  
Generation Process ◽  
Heterogeneous Information ◽  
Learning Platform ◽  
Wide Range ◽  
E Learning ◽  
Sequential Information ◽  
User History

Recently proposed recommendation systems based on embedding vector technology allow us to utilize a wide range of information such as user side and item side information to predict user preferences. Since there is a lack of ability to use the sequential information of user history, most recommendation system algorithms fail to predict the user’s preferences more accurately. Therefore, in this study, we developed a novel recommendation system that takes advantage of sequence and heterogeneous information in the candidate-generation process. The principle underlying the proposed recommendation model is that the new sequence based embedding layer in the model catches the sequence pattern of user history. The proposed deep-learning model may improve the prediction accuracy using user data, item data, and sequential information of the user’s profile. Experiments were conducted on datasets of the Korean e-learning platform, and the empirical results confirmed the capability of the proposed approach and its superiority over models that do not use the sequences of the heterogeneous information of users and items for the candidate-generation process.

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.

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