scholarly journals The role of user preferences in engagement with online learning

2021 ◽  
pp. 204275302110355
Author(s):  
Vladislav Ilin
Keyword(s):  
Online Learning ◽  
Learning Preferences ◽  
User Preferences ◽  
User Preference ◽  
Digital Learning ◽  
Learning Tools ◽  
Learning Materials ◽  
History Curriculum ◽  
Media Preferences ◽  
Access To Knowledge

Digital technology in education has become a staple of many contemporary classrooms. Educational technology offers many benefits, including access to knowledge, mobility, multiple means of engagement, accessibility, distance learning, as well as the ability to connect in unprecedented ways. There is a growing body of research that examines digital learning tools, online classroom environments, learning management systems and other technologies that are integrated into the learning process. Such research extends to analysing the positive impacts of online and blended learning; however, few studies explore the user preferences of the learners. Without assessment of the user preference, the benefits of learning through technology are often lessened. This research explores and analyses how user media preferences influence engagement and motivation in online learning. One hundred twenty-two KS3 (13-year-olds) secondary school were provided voluntary access to a purpose-built online learning resource that augmented their in-class Holocaust history curriculum with learning materials in audio, video and e-text. Data were collected through web analytics and user feedback forms. The analytical tools provided detailed quantitative data on user activity on the site, while the feedback forms yielded qualitative data on usefulness and enjoyment. Results provided insights on the usage trends and user preferences that influence engagement. These behavioural patterns reflect user motivation and learning preferences and can be utilised to personalise digital content delivery to increase engagement with online learning materials.

Do Open Educational Resources and Cloud Classroom Really Improve Students’ Learning?

2014 ◽  
Vol 10 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Chia-Wen Tsai ◽  
Pei-Di Shen
Keyword(s):  
Online Learning ◽  
Open Educational Resources ◽  
Traditional Classroom ◽  
Educational Resources ◽  
Educational Institutions ◽  
Digital Learning ◽  
Learning Materials ◽  
Certification Examinations ◽  
Traditional Classroom Instruction ◽  
The Difference

More and more educational institutions are using educational technologies and online learning materials to help students achieve satisfactory learning effects. However, not all teachers are able to prepare and design digital learning materials for students. This research attempted to empirically demonstrate the effects of applying open educational resources (OERs) and a cloud classroom developed by Ming Chuan University, which comprises access to related software and online learning materials, to enhance students’ computer skills and also improve their scores on certification examinations. The researchers conducted an experiment that included 114 undergraduates from two class sections – the first section received OERs in a cloud classroom in addition to their traditional classroom instruction (OER group, n=61), and the other learned in the traditional classroom without OERs (non-OER group, n=53). The results show that students who received OERs had significantly higher grades than those without in the PowerPoint module; however, the difference is not statistically significant in the Excel module. The authors further discuss the implications and unexpected results in this paper.

Do Open Educational Resources and Cloud Classroom Really Improve Students' Learning?

2015 ◽  
pp. 2126-2134 ◽  
Author(s):  
Chia-Wen Tsai ◽  
Pei-Di Shen
Keyword(s):  
Online Learning ◽  
Open Educational Resources ◽  
Traditional Classroom ◽  
Educational Resources ◽  
Educational Institutions ◽  
Digital Learning ◽  
Learning Materials ◽  
Certification Examinations ◽  
Traditional Classroom Instruction ◽  
The Difference

More and more educational institutions are using educational technologies and online learning materials to help students achieve satisfactory learning effects. However, not all teachers are able to prepare and design digital learning materials for students. This research attempted to empirically demonstrate the effects of applying open educational resources (OERs) and a cloud classroom developed by Ming Chuan University, which comprises access to related software and online learning materials, to enhance students' computer skills and also improve their scores on certification examinations. The researchers conducted an experiment that included 114 undergraduates from two class sections – the first section received OERs in a cloud classroom in addition to their traditional classroom instruction (OER group, n=61), and the other learned in the traditional classroom without OERs (non-OER group, n=53). The results show that students who received OERs had significantly higher grades than those without in the PowerPoint module; however, the difference is not statistically significant in the Excel module. The authors further discuss the implications and unexpected results in this paper.

scholarly journals THE CREATION OF BRAIN-STIMULATING ONLINE LEARNING CONTENT FOR A YOUNG MIGRANT AND REFUGEE PROJECT

2021 ◽  
pp. 1-11
Author(s):  
NICOLE FLINDT ◽  
MIGANOUSH MAGARIAN ◽  
GRETTA HOHL
Keyword(s):  
European Union ◽  
Online Learning ◽  
Digital Learning ◽  
Learning Tools ◽  
The European Union ◽  
Digital Platforms ◽  
Learning Content ◽  
The World ◽  
Seamless Transition ◽  
Good Presentation

Due to the COVID-19 crisis schools and universities all around the world were forced to use digital learning methods. But even assuming a seamless transition to digital platforms, good Online Learning tools do not imply good content and a good presentation of the content. This article describes the approach of a project funded by the European Union to create brain-stimulating Online Learning content which combines elements from classic didactics with elements from neurodidactics and aims to add value to Online Learning. It also suggests that further research should be conducted in other contexts, such as schools and universities, to describe how brain-stimulating Online Learning content could bring more value to Online Learning content.

scholarly journals Environmental Education Infographics by Instagram: Digital Learning Material Increasing Motivation During the Covid-19 Pandemic

2021 ◽  
Vol 4 (1) ◽  
pp. 50-60
Author(s):  
Habibah Rahmawati ◽  
Rizhal H. Ristanto ◽  
Mieke Miarsyah
Keyword(s):  
Online Learning ◽  
Environmental Education ◽  
Student Learning ◽  
Learning Motivation ◽  
Average Score ◽  
Learning Material ◽  
Digital Learning ◽  
Development Research ◽  
Assessment Data ◽  
Learning Materials

Learning activities during the Covid -19 pandemic were dominated by online learning, so digital learning materials are needed to help increase student learning motivation. The purpose of this development research is to develop digital learning material based infographic by instagram on environmental education that can increase student learning motivation in online learning during the Covid-19 pandemic. This development research uses a method that integrates the Borg and Gall models. The data obtained were through documentation, interviews, expert validation and provision of ARCS questionnaires. The results of the validity test based on the analysis of the expert's assessment data stated that this learning material fell into the "very valid" category with an average score of 3.66. During the learning process using infographic learning materials assisted by Instagram, there was an increase in student motivation with an initial percentage value of 54.25% to 83.75% after using the learning materials developed, the magnitude of the increase was obtained from the n gain test with a score of 0.67 in the moderate category. Keywords: Digital Learning Material, Environmental Education, Infographic b Instagram, Covid-19

PENGEMBANGAN PERANGKAT PEMBELAJARAN MENGGUNAKAN PENDEKATAN CONTEXTUAL TEACHING AND LEARNING (CTL) DENGAN INTEGRASI NILAI-NILAI KARAKTER DI SMAN I TUGUMULYO

2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Sri Wahyu Widyaningsih ◽  
Irfan Yusuf
Keyword(s):  
Teaching And Learning ◽  
Lesson Plan ◽  
Development Stage ◽  
Design Stage ◽  
Learning Tools ◽  
Test Results ◽  
Learning Materials ◽  
Is Research ◽  
Efficacy Trials

<p>The research is motivated not yet using CTL approach. In addition, the study provided yet foster the character value of students. This study aimed to the development of learning materials by using CTL approach with the integration of character value are valid, practical, and effective. The type of this research is research and development by using 4-D models. The stages of this research are define, design, and development. The define stage consists of analyzing of curriculum, students, and concept. Then, the learning materials as lesson plan, handout, student’s worksheet, and evaluation, were designed at design stage. The development stage was doing validity, practicality, and effectiveness test. The data of this research was collected by using validation instruments, questionnaire of students and teacher, observation and test instruments. The result of research with validity of the test results showed that the syllabus, lesson plans, teaching materials, worksheets and assessment sheets (cognitive, affective and psychomotor) developed very valid. The test results showed that the learning practicalities developed very practical. Based on the results of efficacy trials, it was stated that the developed learning very effectively used as learning tools are developed to improve the activity and competence of students in the cognitive, affective and psychomotor and behavioral character. And Those, learning materials by using CTL approach with the integration of character values are classification of very valid, very practical, and effective.</p>

Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario

2017 ◽  
Vol SED2017 (01) ◽  
pp. 5-7
Author(s):  
Ruchi Jain ◽  
Neelesh Kumar Jain
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Online Education ◽  
Data Protection ◽  
Teaching And Learning ◽  
Relevant Information ◽  
Indian Society ◽  
Digital Learning ◽  
Modern Education ◽  
Indian Scenario

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

scholarly journals COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong

2021 ◽  
Vol 10 (1) ◽  
pp. 221258682110070
Author(s):  
Ka Ho Mok ◽  
Weiyan Xiong ◽  
Hamzah Nor Bin Aedy Rahman
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Hong Kong ◽  
Social Presence ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Student Evaluation ◽  
Learning Experiences ◽  
University Teaching ◽  
Digital Learning

The COVID-19 pandemic outbreak has forced online teaching and learning to be the primary instruction format in higher education globally. One of the worrying concerns about online learning is whether this method is effective, specifically when compared to face-to-face classes. This descriptive quantitative study investigates how students in higher education institutions in Hong Kong evaluated their online learning experiences during the pandemic, including the factors influencing their digital learning experiences. By analysing the survey responses from 1,227 university students in Hong Kong, this study found that most of the respondents felt dissatisfied with their online learning experiences and effectiveness. Meanwhile, this study confirms that respondents’ household income level and information technology literacy affected their online learning effectiveness. Moreover, this study highlights the significant contributions of the community of inquiry, which places social presence on the promotion of a whole person development that could not be achieved when relying mainly on online learning. Findings encourage university leaders and instructors to search for multiple course delivery modes to nurture students to become caring leaders with the 21st century skills and knowledge set.

scholarly journals Exploring the Continuous Usage Intention of Online Learning Platforms from the Perspective of Social Capital

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 141
Author(s):  
Chun-Hsiung Huang
Keyword(s):  
Social Capital ◽  
Online Learning ◽  
Shared Vision ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Survey Method ◽  
Learning Tools ◽  
Perceived Ease Of Use ◽  
Learning Platforms ◽  
Continuous Usage Intention

This research explores the factors that influence students’ continuous usage intention regarding online learning platforms from the perspectives of social capital, perceived usefulness, and perceived ease of use. The questionnaire survey method was used in the research to analyze the relationship between the research variables and verify the hypothesis based on data from 248 collected valid questionnaire responses. The following results were obtained: (1) “Social interaction ties” positively affect students’ continuous usage intention. (2) “Shared language” negatively affects students’ continuous usage intention. (3) “Shared vision” positively affects students’ continuous usage intention. (4) “Perceived usefulness” positively affects students’ continuous usage intention. (5) “Perceived ease of use” positively affects students’ continuous usage intention. According to the results, students believe in useful teaching that promotes knowledge and skills. The ease of use of learning tools is key to whether they can learn successfully. Paying attention to the interaction and communication between students, so that students have a shared goal and participate in teamwork, is something that teachers must pay attention to in the course of operation. The professional vocabulary of the teaching content and the way of announcing information should avoid using difficult terminology, which is also a point to which teachers need to pay attention.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

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