A Literature Review on Intelligent Services Applied to Distance Learning

Silva, Lidia Martins da; Dias, Lucas Pfeiffer Salomão; Rigo, Sandro; Barbosa, Jorge Luís Victória; Leithardt, Daiana R. F.; Leithardt, Valderi Reis Quietinho

doi:10.3390/educsci11110666

Cited by 15 publications

(10 citation statements)

References 89 publications

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“…is paper divides learning behaviors into listening, speaking, reading, and writing. After effectively identifying student behaviors, English teaching resources for corresponding behaviors are recommended to students through a personalized recommendation system [31].…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior Detection

Yuanfei

2022

Mobile Information Systems

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When confronted with a plethora of resources, many students struggle to quickly filter out the content that is relevant to them. Because there are many English teaching resources and it is difficult to accurately recommend suitable teaching resources for students. Therefore, in this paper we suggest a personalized recommendation system for English teaching resources, which is founded on learning behavior detection. To begin with, a spatiotemporal convolutional network is introduced to effectively identify students’ online classroom behavior, and a global attention module is added to increase the model’s ability to learn global feature information. Furthermore, the identified characteristics of student behavior are incorporated into the recommendation module. Similarly, the differential evolution (DE) algorithm is implemented to the smoothing factor and kernel function center of a generalized regression neural network (CRNN) for resource recommendation mode, while taking into account the strong dependence of the GRNN training effect on the smoothing factor and the kernel function center. The smoothing factor and offset factor are optimized and solved, and the optimized smoothing factor and offset factor are used to recommend GRNN resources. Experiments show that the approach described in this work first has a high precision (i.e., 90.98%) in behavior recognition, and second, the recommendation performance is superior to both of the comparison algorithms (i.e., 85.23% and 78.33%), resulting in better resource recommendation accuracy. The fundamental goal of this work is to deliver several important guidelines for the informatization and intelligence of the English educational resources and services.

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Section: The Proposed Methodsmentioning

confidence: 99%

A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior Detection

Yuanfei

2022

Mobile Information Systems

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Section: Related Workmentioning

confidence: 99%

“…A literature review has been provided in [38] which discussed different research for e-learning. As discussed in [38], most of the research focused on the courses' contents, while few researchers tackled the exams' aspects. In [39], machine learning algorithms have been applied to raise the accuracy percentage in predicting the students' results, however, the current research tackles the exams' aspects in building the exam as a pre-step to ensure the students' high performance.…”

Section: Related Workmentioning

confidence: 99%

A Smart Testing Model Based on Mining Semantic Relations

et al. 2023

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Engaging personalization in the education process is considered one of the success factors for raising the educational process quality by altering the educational institutions' vision for gaining more flexibility while attaining the institution's objectives. It is a fact that the situation of the COVID-19 pandemic is one of the main reasons that forwarded attention to online learning as an obligatory path rather than being optional until the arisen situation of the COVID-19 pandemic. This situation has altered the educational institutions' perspective permanently. This research proposes an intelligent model which considers the personalized student characteristics in exploring the student learning styles variation, then considering this variation in building the student exam. Following this model ensures the compatibility of the conducted exam with the student's capabilities as well as the course Intended Learning Outcomes (ILOs) coverage. The balance in building the exam with covering the course objectives as well as the appropriateness with the student's personalized characteristics is the main objective of this research. The proposed model has been applied and proved its applicability in enhancing the students' exam results to 92.36% and raising the exam quality level.

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“…This log data provides information on how and when students accomplish tasks such as visits, quizzes, assignments, forum conversations, course perspectives, etc. Moodle LMS offers a great degree of flexibility and scalability, allowing it to be adapted to meet the needs of small, medium, and large academic institutions, which vary from school to university [7].…”

Section: Moodlementioning

confidence: 99%

Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model

Krishnan

Nair

Saamuel³

et al. 2022

Sustainability

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In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

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A Literature Review on Intelligent Services Applied to Distance Learning

Cited by 15 publications

References 89 publications

A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior Detection

A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior Detection

A Smart Testing Model Based on Mining Semantic Relations

Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model

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