Social Context-Aware Recommendation for Personalized Online Learning

Intayoad, Wacharawan; Becker, Till; Temdee, Punnarumol

doi:10.1007/s11277-017-4499-2

Cited by 44 publications

(27 citation statements)

References 18 publications

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“…Considering the difference in students' learning capabilities and knowledge backgrounds, Aher and Lobo (2013) propose an improved algorithm using the combination of clustering technique and association rule algorithm, which performs well compared to other methods. A social context-aware personalized recommendation system is developed by taking learner's learning style and knowledge background into consideration (Intayoad et al, 2017).…”

Section: Course Video Recommendation 1737mentioning

confidence: 99%

“…However, these studies only considered some numerical features (eg, course relevance score) when recommending courses; a few studies extracted other hidden features from courses (eg, textual features extracted from course titles, and visual features extracted from course videos) using deep learning methods. Moreover, though some studies tried to make recommendation by integrating social information into existing methods (Intayoad, Becker, & Temdee, 2017), a few of the previous studies inferred learner's preference from his/her play record or view record. This study can fill the existing research gaps.…”

Section: Introductionmentioning

confidence: 99%

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Course video recommendation with multimodal information in online learning platforms: A deep learning framework

Xu¹,

Zhou²

2020

Brit J Educational Tech

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With the rapid development of online learning platforms, learners have more access to various kinds of courses. However, they may find it difficult to make choices due to the massive number of courses. The main contribution of our research is the design of a course recommendation framework which extracts multimodal course features based on deep learning models. In this framework, different kinds of information of course, such as course title, and course audio and course comments, are used to make proper recommendation in online learning platforms. Moreover, we utilize both explicit and implicit feedback to infer learner’s preference. Based on real‐world datasets, our empirical results show that the proposed framework performs well in course recommendation, achieving an AUC score of 79.03%. This framework can provide technical support for course video recommendation, thus helping online learning platforms to manage course resources and optimize user learning experience.

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Section: Course Video Recommendation 1737mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Course video recommendation with multimodal information in online learning platforms: A deep learning framework

Xu¹,

Zhou²

2020

Brit J Educational Tech

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“…There are also studies of learning path planning algorithms based on other methods: From the perspective of contextaware computing, Wacharawan Intayoad et al [28] focused on the social context of the interaction between learning objects and learners and raised a context-aware learning path planning algorithm to promote effective personalized online learning for each learner. The algorithm uses K-nearest neighbors and decision trees to classify the collected social context, uses association rules to recommend suitable learning paths and can plan suitable learning paths for different groups of online learners.…”

Section: Related Workmentioning

confidence: 99%

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Shou

et al. 2020

IEEE Access

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Studying learning path planning can help find useful implicit learning behavior patterns from learners' online learning behavior data, which is conducive to helping beginners or learners with low participation to reasonably arrange the learning sequence of online knowledge points. This paper proposes a learning path planning algorithm based on collaborative analysis of learning behavior through collaborative data analysis of online learning behaviors. The algorithm, based on the learner's online learning behavior data set, first establishes the concept interaction degree model of knowledge points and the directed learning path network, and proposes a local structure similarity measurement method between the knowledge nodes of the directed learning path network. Second, based on the learner's Kullback-Leibler divergence (KLD) matrix, a learning behavior similarity calculation method on the basis of eigenvector matrix similarity is proposed, which is used to perform cluster analysis on learners with similar learning behaviors and to analyze the personalized optimal learning path of each kind of learners. Finally, the clustering algorithm and the evaluation index of the directed complex network have both verified the advantages of the algorithm. This paper employs the online behavior data set and online test data set obtained from the e-learning platform to conduct an empirical analysis of the learning path planning algorithm proposed hereof. The results show that the learner's learning effect has been improved, verifying the validity and reliability of the algorithm. INDEX TERMS local structural similarity on the knowledge node of the directed learning path network, similarity measure on learning behavior, concept interaction degree of knowledge points, individualized optimal learning path

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“…By using all correct classifications (TP+TN) to divide by all example data (TP+TN+EP+FN) forms the verified accuracy. TP presents True Positive, TN presents Ture Negative, FP presents False Positive and FN presents False Negative [29]. And the equation of precision and recall for the model are shown in equation 3and equation 4respectively.…”

Section: Model Verificationmentioning

confidence: 99%

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

Temdee

Tania

2019

2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)

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Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe disease for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors of CVDs should be addressed. Obesity, as one of risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors.

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Social Context-Aware Recommendation for Personalized Online Learning

Cited by 44 publications

References 18 publications

Course video recommendation with multimodal information in online learning platforms: A deep learning framework

Course video recommendation with multimodal information in online learning platforms: A deep learning framework

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

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