M-Learning Generations and Interview Study Results of a Mobile Context-Aware Learning Schedule Framework

2011 ◽  
pp. 28-55 ◽  
Author(s):  
Jane Yin-Kim Yau ◽  
Mike Joy
Keyword(s):  
Mobile Learning ◽  
Adaptive Learning ◽  
Interview Study ◽  
Learning Preferences ◽  
Context Aware ◽  
Postgraduate Students ◽  
Learning Contexts ◽  
Proposed Model ◽  
Study Results ◽  
Mobile Context

Mobile learning applications can be categorized into four generations – ‘non-adaptive’, ‘learning-preferences’-based adaptive, ‘learning-contexts’-based adaptive and ‘learning-contexts’-aware adaptive. The research on our Mobile Context-aware and Adaptive Learning Schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using these as the basis for selecting appropriate learning materials for students. The authors have adopted a pedagogical approach for evaluating this framework – an exploratory interview study with potential users consisting of 37 university students. The authors targeted primarily undergraduate computing students, as well as students within other departments and postgraduate students, so that a deep analysis of a wider variety of users’ thoughts regarding the framework can be gained. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Their data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario, and a proposed model for describing mobile learning preferences dimensions.

A Mobile Context-Aware Framework for Managing Learning Schedules

2009 ◽  
Vol 1 (4) ◽  
pp. 29-55 ◽  
Author(s):  
Jane Yin-Kim Yau ◽  
Mike Joy
Keyword(s):  
Data Analysis ◽  
University Students ◽  
Mobile Learning ◽  
Adaptive Learning ◽  
Learning Opportunities ◽  
Learning Preferences ◽  
Context Aware ◽  
Pedagogical Approach ◽  
Learning Contexts ◽  
Mobile Context

Mobile learning applications can be categorized into four generations: non-adaptive, learning-preferences based adaptive, learning-contexts-based adaptive and learning-contexts-aware adaptive. The research on our learning schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using them as the basis for selecting appropriate learning materials. We have adopted a pedagogical approach for evaluating this framework, an exploratory interview study with potential users consisting of 37 university students. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Our data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario and a proposed taxonomy for describing mobile learner preferences.

Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

2013 ◽  
pp. 218-237 ◽  
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Location Prediction ◽  
Context Aware ◽  
Resonance Theory ◽  
Self Organizing Maps ◽  
Adaptive Resonance ◽  
Proposed Model ◽  
On Line ◽  
Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

scholarly journals Implementasi m-learning berbasis Mobile Context Aware System (MCAS) dalam pembelajaran Geografi pada masa pandemi Covid-19

2021 ◽  
Vol 1 (5) ◽  
pp. 591-597
Author(s):  
Alfyananda Kurnia Putra ◽  
Muhammad Naufal Islam ◽  
Dian Ahmad Sasmito ◽  
Alfa Yusrotin
Keyword(s):  
Mobile Learning ◽  
Learning Process ◽  
Field Research ◽  
Learning Activity ◽  
Average Score ◽  
Context Aware ◽  
Literature Study ◽  
Context Aware System ◽  
Mobile Context ◽  
Method Approach

Learning during the Covid-19 pandemic caused learning activity to be online and causes student’s boredom in Geography. Therefore, teachers must integrating the technology in learning process, with mobile learning (M-Learning) based on mobile context aware systems (MCAS). The study purpose is to determine student’s opinions about implementation of MCAS based M-Learning during the pandemic. This research is a descriptive qualitative with a mix method approach used collection techniques field research and literature study. The results showed that students had a positive opinion regarding the implementation of MCAS based M-Learning during the pandemic, with an average score of 3.40-3.70 out of 4.00. Pembelajaran pada masa pandemi Covid-19 menyebabkan pembelajaran menjadi online sehingga menyebabkan terjadinya kejenuhan siswa dalam proses pembelajaran Geografi. Oleh karena itu, guru harus mampu mengintegrasikan teknologi dalam pembelajaran, melalui mobile learning (M-Learning) berbasis mobile context aware systems (MCAS). Penelitian ini bertujuan mengetahui opini siswa dalam penerapan M-Learning berbasis MCAS pada masa pandemi. Jenis penelitian ini termasuk kualitatif deskriptif dengan pendekatan mix method serta teknik pengumpulan data berupa penelitian lapangan serta studi kepustakaan. Hasil penelitian menunjukkan bahwa siswa memiliki opini positif terkait implementasi M-Learning berbasis MCAS pada masa pandemi, dengan perolehan skor rata-rata skor 3,40-3,70 dari 4,00.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Deep Level ◽  
Image Resolution ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Natural Image ◽  
Historical Period ◽  
Proposed Model

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

scholarly journals Tower Crane Location Optimization for Heavy Unit Lifting in High-Rise Modular Construction

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 121
Author(s):  
Hosang Hyun ◽  
Moonseo Park ◽  
Dowan Lee ◽  
Jeonghoon Lee
Keyword(s):  
Low Cost ◽  
Modular Construction ◽  
Algorithm Optimization ◽  
Location Selection ◽  
Project Completion ◽  
High Rise ◽  
Tower Crane ◽  
Proposed Model ◽  
Study Results ◽  
Crane Selection

Modular construction, which involves unit production in factories and on-site work, has benefits such as low cost, high quality, and short duration, resulting from the controlled factory environment utilized. An efficient tower crane lifting plan ensures successful high-rise modular project completion. For improved efficiency, the lifting plan should minimize the reaching distance of the tower crane, because this distance directly affects the tower crane capacity, which is directly related to crane operation cost. In situations where units are lifted from trailers, the trailer-to-tower crane distance can have a significant impact on the tower crane operation efficiency. However, optimization of this distance to improve efficiency has not been sufficiently considered. This research proposes a genetic algorithm optimization model that suggests optimized tower crane and trailer locations. The case study results show that through the proposed model, the project manager can reflect the optimal location selection and optimal tower crane selection options with minimal cost.

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