A modified ant colony algorithm for personalized learning path construction

Vanitha, V.; Krishnan, Prajindra Sankar

doi:10.3233/jifs-190349

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Fortunately, there have been many research foundations in this regard, and the evolutionary algorithm has become an effective method to find the near-optimal personalized learning paths in the complex and changing online learning environment by virtue of self-organization, self-adaptation and self-learning. Currently, the ant colony optimization (ACO) [13], genetic (GA) [14] and particle swarm optimization (PSO) [8] algorithms have been applied to find personalized optimal learning paths for learners in e-learning platforms, and have achieved good experimental results. For example, Niknam M proposed a bionic intelligent learning path recommendation system based on the meaningful learning theory and using the ACO algorithm, which could find appropriate learning paths for learners and improve them according to learners' needs continuously and dynamically [15].…”

Section: Learning Path Recommendation Based On Combinatorial Optimiza...mentioning

confidence: 99%

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

Chen

Liu

et al. 2023

Mathematics

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To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.

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Section: Learning Path Recommendation Based On Combinatorial Optimiza...mentioning

confidence: 99%

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

Chen

Liu

et al. 2023

Mathematics

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“…IEEE LOM is used to model LO Tarus et al, ( 2018a , 2018b ) Collaborative Filtering with Pattern Mining Context awareness, Sequential access patterns Compared to the author's earlier work, the SPM is replaced with a Generalized Sequence Pattern (GSP) algorithm to understand the sequential access patterns of the learners. Their approach performs better in sparse conditions by using contextual information and sequential patterns in the absence of learner ratings Vanitha and Krishnan ( 2019 ) Population-based Learning Objectives, Cognitive capability, Emotional state, Performance LOs are represented as nodes in the artificial ant model, and ants are modeled as learners. Learning goals, emotional state, cognitive capacity, and learners' success are part of their learner model Venkatesh and Sathyalakshmi ( 2020 ) Hybrid (Collaborative Filtering, Content-Based) Learner behavior, Content features Suggested personalized bee recommender for e-learning (PBReL) based on artificial bee colony (ABC) optimization and uses K-means clustering to generate a recommendation structure.…”

Section: Analysis Of Literature On Content Recommender Systemsmentioning

confidence: 99%

“…When the data are represented using an ontological framework, an ontological similarity measure is used. The research works with input data Imran et al (2016), , Venkatesh and Sathyalakshmi (2020), Murad et al (2020), Vanitha and Krishnan (2019) Jaccard Coefficient Senthilnayaki et al (2015), Niu (2018), and Cosine Similarity Albatayneh et al (2018), , Tarus et al (2017), Wan and Niu (2018), Nafea et al (2019), Bourkoukou et al (2017), Joy et al (2019, Venkatesh and Sathyalakshmi (2020), and Riyahi and Sohrabi (2020) Ontological Similarity Saleena and Srivatsa (2015) Pearson Correlation Coefficient Tarus et al, (2018aTarus et al, ( , 2018b, Nafea et al (2019), Riyahi andSohrabi (2020), and Learner Parameters-based Similarity Dwivedi et al (2018), as learning style/learner preferences used Pearson Coefficient, Cosine Similarity, Euclidean Distance, or Jaccard Coefficient as the similarity measure.…”

Section: Recommendation Techniquesmentioning

confidence: 99%

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Renumol

2021

J. Comput. Educ.

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In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

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“…en the features extracted by the two convolution kernels are fused for the recommendation. is method has achieved good results in the field of item recommendation [7]. Shen et al constructed an e-learning recommendation system by using convolutional neural networks to extract features from text information [8].…”

Section: Introductionmentioning

confidence: 99%

Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

Liu

2022

Scientific Programming

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With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively.

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A modified ant colony algorithm for personalized learning path construction

Cited by 16 publications

References 14 publications

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

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