Online Learning Applications Research Articles

Optimizing Learning Management System Using UTAUT Model: Environment, Compatibility, Material Content, and Interactivity for Enhanced Usability

he purpose of this study is to analyze the factors that influence Behavioral Intention to Re-use to users of online learning applications in learning. Combining the Unified theory of acceptance and use of technology (UTAUT) with the theoretical results of the previous literature related to the effectiveness of learning using an online learning system. The data collection method used a questionnaire distributed to 110 users of the Ruangguru or Zenius learning management system. The study used a quantitative method with the Structural Equation Modeling (SEM) method and was tested empirically using Partial Least Square (PLS). The results showed that the learning environment, content quality, and level of interactivity positively and significantly influenced the perceived ease of use, ultimately affecting behavioral intention. Device or technical factors and the perceived ease of use positively and significantly influenced the perceived usefulness.

Teachers' perspectives on applying online learning tools through learning management system: The need for an online Malay language teaching module

This study examines Malay language teachers' perceptions of using Google tools in their online teaching in primary schools. It uses a basic interpretative research methodology to investigate the perspectives of six primary school Malay language teachers from Terengganu, Malaysia. Interviews with semi-structured questions were used to collect data and analyzed thematically. All the themes and sub-themes were organized using NVivo 12 software. The data show that the predominant topic in teachers' thoughts on the importance of Google tools was the reasons for their absence of use. There were two essential sub-themes: a lack of skills and proficiency and a need for more awareness of how to use Google tools. Furthermore, this study highlighted personal support of module development as a secondary theme which included two sub-themes: agreement with the need for modules and the importance of providing them. This study also underlined the importance of module preparation which was reinforced by two sub-themes: facilitating teachers' implementation of online teaching and serving as a significant reference for teachers. This study shows that effective online education requires competency, skills and knowledge of Google Tools. It suggests creating a Google Tools module to guide and serve as a resource for Malay language teachers.

Thai Students Perspective of Online Learning Media during Covid-19 Pandemic

The present qualitative study explores the perceptions of seven Thai undergraduate students in a university on the online learning medium when the COVID-19 pandemic hit this country. Semi-structured interviews were created to clarify the students' viewpoints on online learning medium. Thematic content analysis was used to analyze the transcriptions of the data collected from the online interviews. The study elicits three significant data themes that are connected to the research questions: the Thai students’ perception towards the online learning medium, their preference on the online learning medium, and the problems faced by them along using the online learning medium and the solving. Findings showed that the using of online learning mediums helped Thai students in lecturing during the COVID-19 pandemic. The most preferred online learning medium they use is WhatsApp application, followed by Google Meet as the second choice. They prefer WhatsApp to other online learning mediums or applications since it is stable, cheap, and easy to use. They also re-studied the lecturer’s explanation everywhere and anytime. The research found some problems students faced while using Google Meet or Zoom; the most common problem they faced was an unstable connection. While excessive internet data usage was another obstacle faced by them. To solve those problems, they frequently moved to another place providing free Wi-Fi or asked their friends or family to share the hotspot.

The Effects of Teacher's Competencies in the Application of Online Teaching and Learning on Students' Academic Stress, Learning Motivation, and Academic Achievement in Statistics Subject

Online learning may lead to several problems, causing academic stress that lowers student motivation. Thus, great instructor skills are needed to overcome challenges and automatically build a successful and efficient online learning process. The study examines how teacher competencies in four dimensions – Teacher’s technological pedagogical content knowledge (TPACK), teacher’s belief (TB), teacher’s self-efficacy (TSE), and teacher’s enthusiasm (TE) – affect students’ academic stress in the cognitive (CD), behavioral (BD), affective (AD), and physiological domains (PD), as well as learning motivation (SLM) and academic achievement (AA). The study uses multivariate regression analysis, the ordinal logistic regression, and the Structural Equation Model (SEM). Multivariate regression analysis is utilized to examine the effects partially and simultaneously of dependent and independent variables. The ordinal logistic regression is used for the same purpose but on the data with an ordinal scale; the final grade shows the AA variable. Whereas, SEM is used to investigate the relationship between complex variables. The sample retrieval technique is conducted using non-probabilistic sampling by the method of accidental sampling with 219 college students. Results show that TPACK, which is one dimension of a teacher’s competencies, negatively affects students’ academic stress, which is BD, AD, and PD. However, TPACK does not significantly affect CD. Adverse effects suggest increasing teacher’s competencies in integrating technology and pedagogies in the development of educational content, especially in online learning, and reducing or minimizing students’ academic stress. Besides that, TPACK also has a positive effect on SLM, and SLM in turn positively affects AA. On the other hand, the variable component of teachers’ competencies, such as TSE and TE, has a positive effect on CD. Likewise, CD positively affects AA, so indirectly, TSE and TE positively affect AA. Of every effect and relationship between independent and dependent variables studied, the variable to contribute most effectively to online learning is the TPACK variable. Keywords: online teaching, teacher’s competencies, student’s academic stress, student’s learning motivation, academic achievement, TPACK, SEM, multivariate regression, logistic ordinal regression

The application of on-line science-based inquiry learning in primary schools

The application of on-line science-based inquiry learning in primary schools

Resistance to online learning technology: Theoretical and empirical perspectives

The increasing popularity of online learning applications (OLAs) among institutions and individuals is an ongoing trend in information technology. While much of previous technology adoption research has been focused on adoption motivators or enablers, the lack of research on adoption inhibitors is the primary motivation for this study. The main focus is on dispositional resistance to change (RTC) and its role in individual OLA adoption decisions. A theoretical model integrating two fields of research, technology adoption and psychology, was developed to investigate the influence of RTC on key adoption predictors using a sample of 217 university-student OLA users. A combination of cognitive dissonance and self-verification theories was used to explain the mechanism of this influence, which was subsequently tested and verified. Findings indicate that RTC has a significant negative impact on effort expectancy and usage intention, but not on attitude and performance expectancy. This study contributes by theoretically underpinning and empirically validating the impact of RTC on OLA adoption decisions. Furthermore, it illustrates the importance of incorporating RTC into technology adoption studies in general and OLA studies in particular. This research provides the foundation for future work in this severely underexplored area.

Pattern Recognition of EFL University Students’ Online Behaviors through Data Science: Any Investment on English Language Components or Skills?

Artificial intelligence (AI) has played a crucial role in many fields of study, and its merits and demerits have been investigated by related scholars. However, it seems a somewhat gray area when implementing AI in teaching and learning. This could be mainly because AI developers know little about learning varied sciences and they also lack pedagogical knowledge for implementing AI in teaching (Luckin & Cukurova, 2019). Therefore, the current research aimed at gathering data regarding the online behaviors of 21 Iranian EFL university students through installing an application on their mobile phones for a period of eight days. Afterwards, the collected data were analyzed through Data Science methods and varied patterns of behavior were recognized. The results exhibited that messaging and social media applications are two great parts of the students’ life. Also, there existed some common patterns regarding the applications students owned and the amount of time they spent on them. As a small case in point, the results of the study cannot be generalized; however, the outcomes highlight the proof that in an EFL context, serious steps need to be adopted by course designers and stakeholders in order to cultivate a greater and purposeful use of online learning applications, websites etc. among EFL university students so as to enhance the teaching-learning process. This gains prominence because any AI-mediated learning approach is believed to enhance students’ second/foreign language motivation as well as self-regulation in learning. AI is actually promising a revolution in the realm of language education.

Offline Planning and Online Learning Under Recovering Rewards

Motivated by emerging applications, such as live-streaming e-commerce, promotions, and recommendations, we introduce and solve a general class of nonstationary multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from up to [Formula: see text] out of N different arms in each time period and (ii) the expected reward of an arm immediately drops after it is pulled and then nonparametrically recovers as the arm’s idle time increases. With the objective of maximizing the expected cumulative reward over T time periods, we design a class of purely periodic policies that jointly set a period to pull each arm. For the proposed policies, we prove performance guarantees for both the offline and the online problems. For the offline problem when all model parameters are known, the proposed periodic policy obtains a long-run approximation ratio that is at the order of [Formula: see text], which is asymptotically optimal when K grows to infinity. For the online problem when the model parameters are unknown and need to be dynamically learned, we integrate the offline periodic policy with the upper confidence bound procedure to construct on online policy. The proposed online policy is proved to approximately have [Formula: see text] regret against the offline benchmark. Our framework and policy design may shed light on broader offline planning and online learning applications with nonstationary and recovering rewards. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04202 .

Driving STEM learning effectiveness: dropout prediction and intervention in MOOCs based on one novel behavioral data analysis approach

AbstractWith the full application of MOOCs online learning, STEM multidisciplinary and knowledge structures have been achieved, but it has also resulted in a massive number of dropouts, seriously affected the learning sustainability of STEM education concepts, and made it difficult to achieve learning effectiveness. Based on the massive STEM learning behavior instances generated by MOOCs, as well as the entire learning periods, this study considers some key explicit and implicit features associated with learning behavior, and achieves the fusion of convolutional neural network and recurrent neural network through data-driven approaches, incorporates long short-term memory mechanism to develop dropout prediction methods and models. Based on the experimental results, we also discuss the relevant problems of dropouts related to STEM learning behavior, explore the key dropout temporal sequences of the learning process, identify related factors that have key impacts on learning behavior, and deduce intervention measures and early warning suggestions. The entire study can provide effective methods and decisions for researching the STEM learning behavior of MOOCs and has strong research feasibility and urgency.

Scrunitizing Social Presence of In-Service EFL Teachers’ Instruction in The Enactment Of Blended Learning

Technology utilization learning is gaining popularity especially when online learning is conducted during and after the worldwide pandemic. Many studies have examined blended learning as an effort to integrate online learning and face-to-face learning which has an impact on increasing student engagement, improving learning outcomes, and providing a more flexible and personalized educational experience. However, the results of previous research also reported that the application of online learning has minimal interaction, thus leading to less meaningful learning. To respond to these problems, this study aims to investigate perceptions about the application of social presence in English teaching in the implementation of blended learning. The results of this study show that quantitatively English teachers have applied 3 important aspects of social presence, of which 59.82% agreed with teaching activities based on affective categories. Followed by the second position, which strongly agrees with 39.50% and disagrees with the application as much as 0.69%. In the Open Communication category, as many as 62.37% of English teachers also agreed with the implementation of this activity, 34.72% strongly agreed, and as many as 3% expressed disapproval. Meanwhile, on the concept of group cohesion, as many as 64.63% agreed, and 35.37% strongly agreed with this concept.

ВЕБ-ОРІЄНТОВАНЕ НАВЧАННЯ ТА УСПІШНІСТЬ СТУДЕНТІВ У НАЦІОНАЛЬНОМУ ВІДКРИТОМУ УНІВЕРСИТЕТІ НІГЕРІЇ

The study examined the impact of Web based learning structures (Moodle, Blackboard, Online Library, Online Course Module/Study Guide, Internet Learning for coach student and Student - Student Connection and Times of Access of Internet Learning Applications) on students' scholarly accomplishment at the National Open University of Nigeria. The descriptive survey research design was used with a population of 100 to 400 students and a sample size of 150 students from the National Open University of Nigeria's schools of law, arts and social science, business and human resources, education, science and technology, and center for continuing education. A questionnaire with 53 questions titled: Experts validated the Online Learning and Learners' Academic Achievement in National Open University of Nigeria Scale (OLLAAS), and Cronbach's alpha yielded a reliability coefficient of 0.76. To test the hypotheses at the 0.05 level of significance, data were analyzed using Multiple Regressions and the Pearson Product Moment Correlation Coefficient. The discoveries of the review uncovered that the Internet learning structures (Moodle, Blackboard, Online Library, Online Course Module/Study Guide, Web based Learning for mentor student and Student - Student Connection and Times of Access of Web based Learning Applications) essentially impacted students' scholarly accomplishment (F (6, 143) = 24.907; R2 = 0.511), and mutually represented 49.1% of its difference. Moodle (r =.206, P0.001), Blackboard (r =.417, P0.001), the Online Library (r =.594, P0.001), the Online Course Module/Study Guide (r =.395, P0.001), online learning for tutor-learner and learner-learner interaction (r =.442, P0.001), and periods of access to online learning applications (r =.625, P0.001) all had positive correlations with students. In light of the discoveries, it was suggested that administration ought to put more in training of this season of computerized age through satisfactory financing to empower schools obtain and introduce ICT foundation. The government and all relevant stakeholders ought to address the problem of incessant power outages. Mechanical gadgets ought to likewise be made accessible to students at sponsored rates to empower everybody partake in remote learning. Web based learning meetings ought to be planned with significant and profoundly captivating and intelligent learning exercises to lessen fatigue among detached students during learning.

Teaching physical education during the time of pandemic

This research study aimed to identify the modalities used in teaching Physical Education in the district of San Rafael, Bulacan during the time of pandemic. Further, the study investigated the challenges experienced by the Physical Education (PE) teachers and the solutions they made to address these challenges. The end goal of the study was a school action plan for physical education to address the issues in teaching PE during the pandemic. Qualitative research design through thematic analysis was employed and a semi structured virtual interview was conducted to further substantiate the findings of the study. Results revealed that modular learning is the most common modality used by Physical Education teachers in this time of pandemic. Other modalities such as synchronous and asynchronous online learning and physical activity mobile applications were used by the teachers. It was also revealed that their main reason for using the modalities was for the welfare of their students. Further, the lack of resources of students in terms of internet connectivity and gadgets was the common struggle experienced by the Physical Education teachers and the most common solution done by the Physical Education teachers was doing follow-up with the students through social media. This study recommended the implementation of the proposed action plan to improve the modalities of teachers in teaching Physical Education and to help them avoid problems they may encounter.

THE STUDENTS’ PERCEPTION OF THE APPLICATIONS USED IN ONLINE LEARNING DURING COVID-19 PANDEMIC AT BORNEO TARAKAN UNIVERSITY

This research aimed to describe students’ perception of efficient applications used in online learning at the English Education Department of Borneo Tarakan University. This research was quantitative research with 32 English Education respondents in the Borneo Tarakan University. The sampling method used was total sampling. The instrument used was a questionnaire. The results of the research indicated that the applications used in online learning amid the COVID-19 pandemic were very efficient because these applications provided a good way or alternative for supporting the learning activities from home. Students used various online learning applications. The applications used are considered efficient enough to help the learning process and can improve English skills, especially students’ speaking skills. Applications used during online learning also facilitate learning because of easy access in the low network coverage setting, not using too much internet bundlest, and economical and suitable enough features to support the learning process. Even though it supports the learning process, some students still find some difficulties with the applications used in online learning.

Real-life data-driven model predictive control for building energy systems comparing different machine learning models

By considering forecasts and exploiting storage effects, model predictive control can achieve significant energy and cost savings in the building sector. However, due to the high individual modeling effort, model predictive control lacks practical applicability. For that reason, data-driven process models, approximating the system behavior based on measurements, have become increasingly popular in recent years. Still, scientific literature lacks consent about the most promising model types and efficient workflows to integrate different machine learning models into a model predictive controller. With this work, we present a workflow to provide efficient model predictive controllers based on measurement data automatically. The main idea is to translate different machine learning models into optimization syntax to enable efficient optimization with full access to gradients. We currently consider artificial neural networks, gaussian process regression, and simple linear regression process models. We use a generic model ontology to automatize the controller generation further and test the methodology on two real-life use cases. The first use case is the application of five office rooms with smart thermostat valves. The second use case is a test hall with an air handling unit and a concrete core activation. Using only two days of initial training data, we deploy controllers based on the different model types for six weeks in the offices and apply online learning to improve the models continuously. We observe only minor differences in controller performance despite the artificial neural networks showing the highest prediction accuracy. The second use case shows that the simple linear models require less controller tuning effort. Thus, for practical applications, we recommend linear regression models.

Implementasi Matode Weighted Product Dalam Penentuan Aplikasi Tatap Muka Pembelajaran Daring Dimasa Pandemi Covid-19

The covid-19 pandemic as we have experienced lately has overwhelmed the community so that activities in various fields are hampered, one of which is in the field of education where the government shifts face-to-face learning to learning which can be done with various visual media such as Classroom, Zoom, Google Meet, E-learning and so on. Due to the large number of media that can be used for learning, a determination is needed to choose a face-to-face online learning application. In the determination, a system is needed, namely a decision support system where this system can provide efficient and objective decisions based on predetermined methods and data. , in this study determine the WP (Weighted Product) method as a decision-making tool which is part of the DSS method. And by setting 5 alternatives or applications that are calculated and determined the 5 criteria that are used as requirements in order to produce the right decision. So from the problems above, this study was made which discusses the implementation of the WP method in determining the application used for face-to-face during online learning during the covid-19 pandemic. Based on the implementation of this method, a face-to-face online learning application is produced, namely the A3 alternative, namely Elearning with a preference value of 0.341.

Developing a web application for Chinese calligraphy learners using convolutional neural network and scale invariant feature transform

Chinese Calligraphy is an ancient art form with practical benefits such as improving elders' cognition and helping diagnose Parkinson's disease. Learning Chinese calligraphy requires a lot of time and effort. This is due to the different styles and the vast number of Chinese characters used daily. Chinese character and style recognition are multi-class classification problems. In multi-class classification problems, the accuracy decreases as the number of classes increases. Previous research on Chinese calligraphy was limited to either style recognition or character recognition. More research that combines style and character recognition is needed. Furthermore, the models in previous research were limited to recognizing small sets of 300 characters. These small sets cover only 12% of the Chinese characters used daily. The small size is a severe limitation for creating applications for Chinese calligraphy learning. Thus, new research is needed on AI models that combine style and character recognition to recognize a much larger dataset, while maintaining reasonable accuracy rates. Besides, there are no applications that use such AI models to help learners of Chinese calligraphy compare their works with those created by skillful calligraphers. This research proposed enhancing Chinese calligraphy recognitions by combining the VGG (Visual Geometry Group)-net architecture, data augmentation techniques, and dropout in the ANN (artificial neural network) hidden layers. The research produced two AI models that can recognize 962 Chinese characters in five calligraphic styles. We proved that using dropout increased the accuracy convergence during training epochs. In the proposed models, style recognition achieved up to 88.3% accuracy, while character recognition achieved 95.6% accuracy. Finally, we built an online learning application by combining the proposed models with Scale Invariant Feature Transform (SIFT). The application could compare the learner's work with skillful calligraphers' and provide a similarity score to help Chinese calligraphy learners track their progress. Five university students tested the application. They provided positive feedback and some suggestions for improvement.

SENTIMENT ANALYSIS OF REVIEW DATA OF THE RUANGGURU ONLINE LEARNING APPLICATION USING THE C5.0 ALGORITHM

Sentiment analysis is process to determine the sentiment of a person that is manifested in the form of text. Internet users write their opinions and everything that concerns them in the google play store review column. Moreover, when the world of education could not carry out face-to-face learning due to the covid-19 pandemic, learning turned to e-learning applications. Through this innovation, many pros and cons flow from the community with the existence of Ruangguru online learning application in the world of education. This research was conducted with the aim of determining word cloud visualization and the accuracy of the results of sentiment analysis of review data on the Ruangguru application using the C5.0 algorithm. The word cloud visualization results are dominated by word such as “paham”, “bagus”, “mudah”, “suka”, “langganan”, “seru”, “nyaman”, “senang”, “menarik”, “keren”, “lancar”, “sukses”. This shows that Ruangguru Application is a good application because it is dominated by positive sentiment words which means that users find it helpful and easy to understand the learning material in Ruangguru. The results of the Confusion Matrix show that the model successfully classifies 0.8557 or 85.57% of the data correctly from all test data

Implementation of Assignment-Assisted Video-Based Online Learning to Increase the Intelligent Character Values of Physics Teacher Candidates

This research was motivated by the ineffectiveness of learning physics during the pandemic. Subject lecturers apply a solution to learning, namely implementing assignment-assisted video-based learning. This study aimed to increase the value of the intelligent character of physics teacher candidate students. This research is classroom action research that consists of two cycles. The research subjects were ten physics teacher candidates teaching optical physics courses. The research instruments used were student character observation sheets and intelligence tests. Data analysis techniques using descriptive statistical analysis. The results showed increased acquisition of intelligent character scores for physics teacher candidates in each cycle. The highest increase is in grade II in the range of 75-95. The conclusion that can be drawn from this research is that implementing assignment-assisted video-based online learning can increase the intelligence character scores of prospective physics teacher students and is at grade II in the range 75-95 (above average intelligence). This research provides an overview of learning innovations for teachers for classroom application in physics learning. By implementing learning videos, students were actively involved in constructing knowledge, attitudes, and skills both personally and in groups, so the implication of implementing video-based learning assignments was to create a young generation with character in the development of science, technology, and society, where students have been able to implement character values in applying online learning technology. In this case, the student has high integrity and character based on Islamic and Muhammadiyah values.

Metaverse-Based Learning in the Digital Era

Digital disruption in the world of education refers to the impact of technology on traditional education systems and models, where the current education system is felt to need to be adjusted to the progress of the times and the needs of students. The business model of education in this digital era requires the innovation and creativity of teachers. After the Covid 19 pandemic, in addition to providing challenges in the world of education, it also provides an opportunity where students and educators are accustomed to online learning applications, teleconference applications, or collaboration applications that were previously not or rarely used by the world of Education. Many apps have been created to make online learning easier. Metaverse has been around since 2010, but due to the pandemic, the use of metaverse in learning is again being used with the development of Virtual reality, extended reality, and augmented reality technology. These three technologies can display a virtual environment just like the original environment. The method used to design metaverse-based learning is to use the ADDIE research and development model while learning development follows the stages of the Dick and Carey model. The results of field trials using survey methods and experiments on 20 junior high school students for one semester using the metaverse, resulted in a score of 4.82 with the conclusion of excellent learning material, students felt very happy, motivated to complete tasks, and always followed the learning stages designed in the form of games. Implementing the metaverse in learning can increase student engagement, creativity, technology skills, collaboration, and interaction as well as improve learning experiences that are more fun and engaging.

Investigation of student experiences with ChatGPT-supported online learning applications in higher education

The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot’s functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use. Implications for practice or policy Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities. ChatGPT can promote and enhance students' personalised learning experiences. ChatGPT can be used in all higher education courses. Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT. Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.