scholarly journals Research on Adaptive Learning Prediction Based on XAPI

2020 ◽  
Vol 10 (9) ◽  
pp. 679-684
Author(s):  
Jun Xiao ◽  
◽  
Lamei Wang ◽  
Jisheng Zhao ◽  
Aizhen Fu
Keyword(s):  
Online Learning ◽  
Adaptive Learning ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Online Learners ◽  
Learning Platform ◽  
Teaching Support ◽  
The Relationship ◽  
Learning Data

In the field of online learning, there is a problem of high student turnover rate. How to accurately identify learners and provide targeted teaching support services is an urgent problem for education researchers. In this paper, 1306 online learners majoring in finance from Shanghai Open University were selected as the subjects, and two kinds of data sets are adopted, which are learning data of online learning platform and learning behavior data of students based on xAPI, to analyze the relationship between learners' various online learning behaviors and learning achievements, and to determine the characteristics related to learning state of learners, describe the personalized learning state portrait, and select a variety of machine learning algorithms to build prediction model based on two data sets, to explore which data is more effective for building prediction models to identify potential risk learners. It is found that data mining analysis based on xAPI data has higher prediction accuracy than traditional online learning data.

Working together online to enhance learner autonomy: Analysis of learners’ perceptions of their online learning experience

ReCALL ◽  
2012 ◽  
Vol 24 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Jérôme Eneau ◽  
Christine Develotte
Keyword(s):  
Online Learning ◽  
Distance Learning ◽  
Interpersonal Relationships ◽  
Learning Experience ◽  
The Body ◽  
Learning Collaborative ◽  
Online Learners ◽  
Social And Emotional ◽  
Learning Platform ◽  
Working Together

AbstractThis study concerns the development of autonomy in adult learners working on an online learning platform as part of a professional master's degree programme in “French as a Foreign Language”. Our goal was to identify the influence of reflective and collaborative dimensions on the construction of autonomy for online learners in this programme. The material used was 27 self-analysis papers in response to an assignment which asked students to review their distance learning experience (reflective dimension) and to highlight the role of others, if any, in their learning (collaborative dimension). In addition to these two major points, the analysis by category of the body of results shows principally that in qualitative terms, the factors of autonomisation for online learning are interconnected and include: the difficulties related to distance learning and the strategies that learners develop to face those difficulties, the importance of interpersonal relationships in social and emotional terms in overcoming those difficulties, the specific modes of sociability developed for distance learning and the related development of a new type of autonomy that is both individual and collective. The discussion examines the creation, over the course of time, of a new “distance learning culture” that is nonetheless never easy to create and share.

scholarly journals From Digital Humanities to a Renewed Approach to Digital Learning and Teaching

2019 ◽  
pp. 1682-1693 ◽  
Author(s):  
Samuel Nowakowski ◽  
Guillaume Bernard
Keyword(s):  
Artificial Intelligence ◽  
Online Learning ◽  
21St Century ◽  
Digital Humanities ◽  
Full Measure ◽  
Digital Learning ◽  
Learning And Teaching ◽  
Learning Platform ◽  
The World ◽  
The Relationship

In a world in which digital interfaces, dematerialization, automation, so-called tools of artificial intelligence aim to drive away the human or eliminate the relationship with humans! The way other beings see us is important. What would happen if we took the full measure of this idea? How would this affect our understanding of society, culture, and the world we inhabit? How would this affect our understanding of the human, since in this world beyond the human, we sometimes find things that we prefer to attribute only to ourselves? What impacts on education, learning, teaching? After having explored the field opened by these questions, we will bring an answer with a reinvention of the learning platform named KOALA (KnOwledge Aware Learning Assistant). KOALA is a new online learning platform that comes back to internet sources. Symmetrical and acentric, KOALA combines analyzes from the digital humanities and answers to the challenges of education in the 21st century

scholarly journals Exploring the Antecedents of Online Learning Satisfaction: Role of Flow and Comparison Between use Contexts

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Quan Xiao ◽  
Xia Li
Keyword(s):  
Online Learning ◽  
Social Interaction ◽  
Self Efficacy ◽  
Online Survey ◽  
Critical Role ◽  
Learning Satisfaction ◽  
Learning Platform ◽  
New Findings ◽  
Mobile Context ◽  
The Relationship

Learners’ satisfaction plays a critical role in the success of online learning platform. Many factors that affect online learning satisfaction have been addressed by previous studies. However, the mechanisms by which these factors are associated with online learning satisfaction are not sufficiently clear. Moreover, the difference in the antecedents of online learning satisfaction between two use contexts- Mobile context and PC context, was rarely examined. Based on the Stimulus-Organism-Response (S-O-R) framework, we investigate the key factors (self-efficacy, social interaction, platform quality, teacher’s expertise) affecting flow and highlights its role in online learning satisfaction, which is empirically tested through an online survey of 333 online learners. Results show that self-efficacy, teacher’s expertise, platform quality, and social interaction positively affect online learning satisfaction through the mediation of flow. Use contexts not only moderate the relationship between flow and online learning satisfaction, but also between social interaction, platform quality, teacher’s expertise, and flow. These new findings expand educators with ways to increase flow, add to knowledge about the relationship between flow and online learning satisfaction and provide references for online learning platforms to enhance learners’ online learning satisfaction under multiple-version affordances.

scholarly journals An ESL Online Classroom Experience in Oman during Covid-19

2020 ◽  
Author(s):  
Syerina Syahrin ◽  
Abdelrahman Abdalla Salih
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Undergraduate Students ◽  
Learning Experience ◽  
Online Classroom ◽  
Learning Platform ◽  
E Learning ◽  
Learning Styles Inventory ◽  
The Relationship

This paper aimed to investigate the online learning experience of a group of ESL students at a higher learning institution in Oman during the Covid-19. The paper studied the interaction between the students’ preferred online learning style and the technologies the students experienced on the e-learning platform (Moodle) for the particular ESL course. The rationale for investigating the relationship between the students’ learning styles and the technologies the students experienced is to evaluate if the learning style and the technologies complement each other. It is also aimed to provide an evaluation of an ESL e-learning course by considering the different technologies that can be incorporated into the e-learning classroom to meet the different learning styles. Data was gathered from 32 undergraduate students by utilizing Kolb’s Learning Styles Inventory. The study included analysis of Moodle utilizing Warburton’s Technologies in Use (2007) to develop an understanding of the technologies the students experienced online. The results of the study revealed that the majority of the students’ preferred learning style is reflected in the technologies they experienced in the online classroom. As the relationship of the technology in use and the students learning style preference in the classroom complements each other, the study revealed that the emphasis of the particular skill-based pedagogy ESL classroom is on receptive skills (listening and reading). The lack of the students’ productive skills (speaking and writing) is a cause for concern to the ESL course instructors, policymakers, and the wider community.

scholarly journals Microlearning and computer-supported collaborative learning: An agenda towards a comprehensive online learning system

2021 ◽  
Vol 1 (4) ◽  
pp. 225
Author(s):  
Soheila Garshasbi ◽  
Brian Yecies ◽  
Jun Shen
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Smart Devices ◽  
Computer Supported Collaborative Learning ◽  
Online Learning Environments ◽  
Learning Platform ◽  
Intelligent Tools

<p style='text-indent:20px;'>With the rise of the COVID-19 pandemic and its inevitable consequences in education, increased demand for robust online learning frameworks has occurred at all levels of the education system. Given the transformative power of Artificial Intelligence (AI) and machine learning algorithms, there have been determined attempts through the design and application of intelligent tools to overcome existing challenges in online learning platforms. Accordingly, educational providers and researchers are investigating and developing intelligent online learning environments which share greater commonalities with real-world classroom conditions in order to better meet learners' needs. However, short attention spans and the widespread use of smart devices and social media bring about new e-learning systems known as microlearning (ML). While there has been ample research investigating ML and developing micro-content, pedagogical challenges and a general lack of alternative frameworks, theories and practices still exist. The present models have little to say about the connections between social interaction, including learner–content, learner–instructor and learner–learner communication. This has prompted us to investigate the complementary aspects of Computer-supported Collaborative Learning (CSCL) as an interactive learning model, along with an embedded ML module in the design and development of a comprehensive learning platform. The purpose of this study is to explore the pedagogical frameworks and challenges with reference to interaction and retention in online learning environments, as well as the theoretical and pedagogical foundations of ML and its applications. In addition, we delve into the theories and principles behind CSCL, the main elements in CSCL, identifying the issues and challenges to be faced in improving the efficacy of collaboration processes and outcomes. In short, we aim to synthesize how microlearning and CSCL can be applied as effective modules within a comprehensive online learning platform, thereby offering STEM educators a relevant roadmap towards progress that has yet to be offered in previous studies.</p>

Predicting risk of stroke from lab tests using machine learning algorithms (Preprint)

2020 ◽  
Author(s):  
Eman Alanazi ◽  
Alaa Abdou ◽  
Jake Luo
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Health Records ◽  
Time Prediction ◽  
Health And Nutrition ◽  
Different Types ◽  
Potential Risk Factors

UNSTRUCTURED Stroke, a cerebrovascular disease, is one of the major causes of death. It is also causing a health burden for both the patients and the healthcare systems. One of the important risk factors of stroke is health behavior which is an increasing focus of prevention. In addition, chronic diseases such as hypertension, diabetes, cardiac diseases, and asthma are potential risk factors for stroke. There are a lot of machine learning that built using predictors such as lifestyle or radiology imaging. However, there are no models built using lab tests. The aim of the study is to fill this gap by building prediction models to predict stroke from lab tests. We utilized the National Health and Nutrition Examination Survey (NHNES) data sets to develop models that would predict stroke from patient lab tests. We found that accurate and sensitive machine learning models can be created to predict stroke from lab tests. The results showed that prediction with the best tested algorithm random forest could reach the highest accuracy (ACC = 0.96) when all the attributes were used. The model proposed can be integrated with electronic health records to provide a real-time prediction of stroke from lab tests. Due to the data, we could not predict the type of stroke wither hemorrigic or ischemic. In future studies, we aim to use data that provide different types of stroke and explore the data to build a prediction model of each type.

scholarly journals Machine Learning and Deep Learning for Sentiment Analysis Over Students' Reviews: An Overview Study

2021 ◽  
Author(s):  
Ru Yang
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Online Teaching ◽  
Large Scale ◽  
Figurative Language ◽  
Machine Learning Algorithms ◽  
Learning Platform ◽  
Learning Platforms

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

scholarly journals Mathematics self-concept and challenges of learners in an online learning environment during COVID-19 pandemic

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Rex Bringula ◽  
Jon Jester Reguyal ◽  
Don Dominic Tan ◽  
Saida Ulfa
Keyword(s):  
Online Learning ◽  
Mixed Methods Research ◽  
Qualitative Data ◽  
Self Concept ◽  
Online Classes ◽  
Online Learners ◽  
Face To Face ◽  
Learning Mathematics ◽  
Face To Face Learning ◽  
The Relationship

AbstractIn this mixed-methods research, the relationship between four factors of individual online learners and their mathematics self-concept was explored. In addition, the challenges the students faced in learning mathematics online during the Coronavirus disease (COVID-19) pandemic were determined. The participant students were from two mathematics classes offered online during the summer of 2020. Pure online classes were first offered during this period because face-to-face learning sessions were suspended due to the COVID-19 pandemic. It was found that students owned the devices they were using for online classes. Internet connection and power interruption were the most problematic aspects of online learning. Students had positive as well as negative mathematics online learning self-concepts. Individual factors were partly related to mathematics self-concept. Qualitative data shows that students faced technological, personal, domestic, assessment, pedagogical, consultation, and test anxiety challenges. Implications and recommendations for teaching mathematics in an online environment are offered.

Limited Stochastic Meta-Descent for Kernel-Based Online Learning

2009 ◽  
Vol 21 (9) ◽  
pp. 2667-2686 ◽  
Author(s):  
Wenwu He
Keyword(s):  
Online Learning ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Support Vector ◽  
Data Sets ◽  
Step Size ◽  
Adaptive Learning Rate ◽  
Practical Performance ◽  
Convergence Proofs ◽  
Theoretical Results

To improve the single-run performance of online learning and reinforce its stability, we consider online learning with limited adaptive learning rate in this letter. The letter extends convergence proofs for NORMA to a range of step sizes, then employs support vector learning with stochastic meta-descent (SVMD) limited to that range for step size adaptation, so as to obtain an online kernel algorithm that combines theoretical convergence guarantees with good practical performance. Experiments on different data sets corroborate theoretical results well and show that our method is another promising way for online learning.

Support for Study: Online Educational Environments and the Rights to Education

2008 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Lin ◽  
Karen Swan
Keyword(s):  
Online Learning ◽  
Conceptual Framework ◽  
Collaborative Inquiry ◽  
Expert Knowledge ◽  
The Self ◽  
Open Educational Resource ◽  
Educational Resource ◽  
Online Learners ◽  
Educational Environments ◽  
Independent Inquiry

This paper uses an online learning conceptual framework to examine the “rights to education” that the current online educational environments could provide. The conceptual framework is composed of three inquiries or three spaces for inquiries, namely, independent inquiry, collaborative inquiry, and formative inquiry towards expert knowledge [42] that online learners pursue and undertake in the process of their learning. Our examinations reveal that most online open educational resource environments (OERs) can incorporate more Web2.0 or Web3.0 technologies so as to provide the self-directed learners, who are the main audience of OERs, with more opportunities to participate, collaborate, and co-create knowledge, and accordingly, to achieve their full rights to education.

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