Online Learning Environment Research Articles

Towards a comprehensive framework of social presence for online, hybrid, and blended learning

Social presence, which refers to the psychological phenomenon of perceiving other persons in technology mediated communication as “real” and with whom one can connect, has gained an increasing interest by teachers and researchers involved in designing online, hybrid, and blended learning environments, particularly group learning settings known as computer-supported collaborative learning (CSCL). While some scholars attribute social presence primarily to the physical attributes of communication media, others emphasize the importance of social contextual and individual factors. Despite considering these factors, they still cannot fully explain the varying degrees of social presence experienced across different communication and collaboration modes and modalities. Consequently, there is a need for a more comprehensive theoretical account on the antecedents of social presence. In this article we propose such an account that integrates the social information processing (SIP) theory, construal level theory (CLT), and telepresence theory into one social presence framework. In line with CLT, we propose that social presence is also influenced by the impressions (construals) we construct from other persons not only through the accumulation of messages over time but also through the psychological distance we feel to those persons, which may be imposed by features of the communication media or realities of the learning context. Further, in line with telepresence theory, we propose that social presence is influenced by the sense of being “present” in the remote physical or virtual place, as this is where other salient persons “are.” This comprehensive theoretical framework allows us to understand varying degrees of social presence while in (pseudo) real-time and asynchronous communication and collaboration using a variety of different communication media ranging from text-based (e.g., e-mail, instant text messaging) to immersive (e.g., 3D computer generated; a physical remote place).

Towards prescriptive analytics of self‐regulated learning strategies: A reinforcement learning approach

AbstractSelf‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments. In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use. Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior. What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task. Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding. The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task. The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories.

Measurement of Student Engagement in a Generic and Online Learning Management System-Based Environment

Use of technology on campuses of higher education has changed how students are engaged in the process of learning. It also has brought lot of asynchrony to the definition of class room teaching, active learning, student-staff interactions, and dealing with various academic challenges. The paper presents a study conducted to measure student engagement in a generic and online learning management system-based teaching learning environment. The paper presents threefold results. The first one is on identification of student engagement styles. The styles identified can further be used to design, develop, and implement most student engaging policies on campus which are beneficial to all the stakeholders. Second, the central point of the study is the student and her/his engagement with the learning process. The paper presents a student engagement report card to individual students for their analysis. Informing and involving students to know about their engagement report card would be beneficial. The third is feedback on a trail left by students' logs on the learning management system that can help the teachers to plan the teaching methodology. The methodology used was based on the data collected by the students of the institute/university. A student engagement questionnaire was used to measure student engagement in both generic and online learning environments. A cluster analysis was conducted on the data collected to identify the student engagement styles. A subcategory analysis was reported as a student engagement report card. The student-logged data on the institute learning management system was used to present the third analysis.

First-year Preservice Teachers’ Understanding of Digital Technologies and Their Digital Literacy, Efficacy, Attitude, and Online Learning Engagement: Implication for Course Design

Ensuring quality education for all students requires teachers to possess knowledge and skills in utilising digital technologies effectively for teaching and learning. This study explored how pre-service teachers (PSTs) perceive their digital attitude, efficacy, literacy, engagement, and understanding of digital technologies in an online learning environment. The study employed a mixed-methods research approach. A survey comprising Likert Scale questions and demographic information was administered to 110 PSTs from a regional university. Descriptive statistics were employed to investigate the relationship between PSTs’ understanding of digital technologies and their attitude, literacy, self-efficacy, and learning engagement in online learning. To illustrate the frequently used words in participants’ definitions of digital technologies, a ‘word cloud’ representation was utilised, accompanied by quantitative weightings of key terms. The study revealed significant connections between PST self-efficacy and their attitudes toward digital technologies, digital literacies, and learner engagement. Notably, most of the PSTs exhibited narrower definitions of digital technologies than anticipated. The implications of these findings for course design are discussed, emphasising the need to address PSTs’ perceptions of digital technologies, foster their self-efficacy, and enhance their digital literacy and engagement in online learning environments. Understanding these factors can lead to more effective integration of digital technologies in teacher education program courses, ultimately preparing future teachers for digital-age classrooms.

Investigating Effective Strategies for Providing Personalized Feedback to Students and Engaging Them in Online Learning Environments

Investigating Effective Strategies for Providing Personalized Feedback to Students and Engaging Them in Online Learning Environments

An Effective Online Learning for Listening Skill Subject: Students' Voice

This study aims to investigate the expectations of English Language Education students regarding effective online learning, with a focus on the listening subject. Qualitative research was conducted with 68 participants in the 2019 academic year, using online questionnaires to collect data. Results indicate that students anticipate clear communication, lecturer presence, an appropriate tone, and support from the online learning environment. They expect online courses to be challenging and offer opportunities for active learning, self-reflection, small group discussions, and practical application of theory. Positive social interactions are highly valued by learners, both online and offline. While time management and the home environment are concerns, participants’ express confidence in their ability to complete online exercises and learn independently with effective time management, the flexibility offered by online courses, and support from family and friends. Findings suggest that students have high expectations for their online learning experience and believe that they can achieve their goals with the appropriate resources and support.

Enhancement of Students Motivation in Online English Classes: Teacher's Response Strategies and Challenges

Students' motivation in online classes is a crucial concern when online learning is becoming more and more common. To improve student motivation in online English courses, this study investigates and analyzes the coping mechanisms and difficulties faced by teachers. This paper examines the many tactics used by educators to encourage and engage students in an online learning environment. It does this by using a mixed research methodology that includes surveys, comparisons, and interviews. The present study's findings highlight the difficulties faced by teachers and how these difficulties affect students' motivation. This paper has found that there are some similarities in the strategies made by teachers in different countries to enhance students' motivation to learn, but there are also many differences. Anyway, they all use some theories. The strategies of teachers with different ages and teaching experiences are also very different, young teachers know how to catch the students' interest more while older teachers are more oppressive and tend to make the classroom atmosphere tense. Secondly, students' motivation is also influenced by the students themselves, such as their personality, age, interest in the subject, and the compatibility of their major with the course content.

A Study of The Effects of Feedback on College Students’ Self-Regulated Learning in an Online Learning Environment

Background: Effective feedback positively affects the learning process of students, and its role is manifested in the fact that it can stimulate learning motivation and promote cognition. In self-regulated learning as a learning mode, feedback is also an essential key factor in the process of its activities. Based on the requirements of an online learning environment for college students' self-regulated learning as well as the current situation that the overall level of Chinese college students' self-regulated learning ability is only in the middle, this study hopes to provide feedback based on the process of self-regulated learning, implement feedback for college students using online learning, and explore the impact of feedback on college self-regulated learning to test the utility of feedback. Methods: This study combines literature analysis, experimental research, and a questionnaire to conduct an in-depth study of the impact of feedback on undergraduate self-regulated learning in an online learning environment. Sample: 37 students’ final grade point average in the previous semester is taken as the pre-test score. Based on this data, the study subjects are divided into two groups that are not statistically different. A teaching experiment lasting one semester is conducted on two groups of students: one receiving feedback and the other not, serving as the experimental and control groups. Results: (1) Feedback has a positive impact on college students' self-regulated learning level; (2) Feedback does not have a significant impact on college students' academic performance in the short term. Conclusion: This study investigates three aspects of model construction, feedback design, and empirical evidence to provide feedback based on learners' self-regulated learning processes in online learning environments to improve college students' self-regulated learning.

Attention-Based AdaptSepCX Network for Effective Student Action Recognition in Online Learning

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students’ behaviors. This study's primary objective is to detect and recognize students’ actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, Con-vXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students’ academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images.

Developing Gamified Learning Management Systems to Increase Student Engagement in Online Learning Environments

Online learning has become a trend in today’s digital age. Accessibility, flexibility, a wide variety of learning resources, collaboration and communication, and the use of technology and innovation are reasons for the increasing popularity of online learning. However, student engagement is often low and requires innovative solutions to increase it. This study aims to develop and analyze the effectiveness of gamified Learning Management Systems (LMS) in increasing student engagement in online learning. The research method used is the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The subjects of this study were students of one of the private universities in Indonesia who attended online lectures during the pandemic. Data collected using questionnaires, observation sheets, and interview guidelines were then analyzed using descriptive statistics. The results showed that gamified Learning Management Systems (LMS) was able to increase student engagement in online learning. However, technical limitations and inadequate institutional support were barriers to implementing gamified LMSs. In conclusion, developing gamified LMS can be an effective alternative strategy to increase student engagement in online learning and positively influence their academic performance. Overall, this research contributes to the development of gamified LMS in Indonesia and provides insight into the effectiveness of gamification strategies in increasing student engagement in the online learning environment.

An interpretable English reading proficiency detection model in an online learning environment: A study based on eye movement

Aiming at the low English reading proficiency of ESL (English as second language) students in online learning environments, this study proposed an eye-movement-based machine learning monitoring model to detect English reading proficiency in real time. Eye-movement data from 43 students while completing online English reading tasks were recorded and 31 eye-movement features were extracted from the taxonomy of fixation, saccade, movement direction and gaze velocity. During the model training phase, LightGBM achieved an accuracy of 96.51 % in detection. An interpretable model, SHAP (SHapley Additive exPlanation), was used to explain the main effects of eye-movement features in detection, where high gaze velocity, absolute saccade direction, and average saccade duration were found to be strong indicators of English reading proficiency. Furthermore, SHAP analysis allows the identification of individual factors contributing to differences in English reading proficiency. This study demonstrated the effectiveness of the combination of eye-movement data with machine learning methods to identify students with low English reading proficiency in online reading and provided insights into the intrinsic correlation between eye movements and reading.

MALAYSIAN ELEMENTARY LEARNERS’ SELF-REGULATION, MOTIVATIONAL BELIEFS AND LEARNER CONTROL MOTIVATION WHEN EXPERIENCING ONLINE TUTORIALS

Online learning is emphasised in the 21st century curricula as one of the efficient instructional practices that improves the learning ownership of contemporary learners. A key factor in ensuring success in learners’ online learning environments is the implementation of learner control. Learner control motivation is influenced by multiple interconnected beliefs and learner-directed learning practices. This study focuses on self-regulation strategies and motivational beliefs in the context of post-pandemic online tutorials. The aim of this study was to discover the influence of self-regulation strategies on learners’ motivational beliefs, and the impact of learners’ motivational beliefs on their learner control motivation when experiencing online tutorials. This study comprises 14 learners (age 9) from a Malaysian elementary school. Qualitative case study methodology was employed, using interviews and observations. The results of this study revealed a significant relationship between self-regulation and motivational beliefs in order to comprehend learners’ motivation for learner control when experiencing online tutorials. The results drew attention on self-regulation strategies to guide online instruction that underpins learners’ motivational beliefs and enhances learner control motivation.

Global Education without Walls: A Multidisciplinary Investigation of University Learning in Online Environments across Disciplines

Global Education without Walls: A Multidisciplinary Investigation of University Learning in Online Environments across Disciplines

Understanding the Relationship between Support Provided to Students and Their Engagement in an Online Learning Environment: A Moderated Mediation Model

Understanding the Relationship between Support Provided to Students and Their Engagement in an Online Learning Environment: A Moderated Mediation Model

The importance of effective learning technology utilization, teacher leadership, student engagement, and curriculum in the online learning environment

The importance of effective learning technology utilization, teacher leadership, student engagement, and curriculum in the online learning environment

Evaluating Student Satisfaction and Preferences in Higher Education Online Learning: A Post-Pandemic Exploration

This study, grounded in transformational learning theories, investigated the satisfaction levels of higher education students with online learning during the COVID-19 pandemic. The study found that students expressed a neutral sentiment towards the online learning environment, with key aspects valued by students, including instructor support, guidance, and personalization. However, students reported the lowest satisfaction with the knowledge and skills acquired, indicating a need to align teaching methods with desired learning outcomes. Facility performance also received a neutral mean satisfaction score. Students appreciated webinars, seminars, and extracurricular activities, recognizing their importance in enriching online education. However, they wanted more than school-provided facilities, suggesting physical resources and service improvements. Regarding future learning modes, students preferred blended classrooms, face-to-face instruction, and specific online teaching platforms over exclusive online classes, emphasizing the need for diversified learning approaches and effective digital tools. A notable finding was the positive correlation between students' willingness to recommend online learning in the future and their overall satisfaction, underscoring the importance of instructor engagement, diverse learning modalities, and aligning teaching methods with desired learning outcomes to meet the evolving needs of students in a post-pandemic educational landscape. These findings have significant implications for the future of higher education, particularly in the design of online learning platforms and the role of educators in enhancing the student experience. Keywords: Blended Learning; Higher Education; Online Learning; Post-pandemic education; Student satisfaction.

Help-seeking

Help-seeking occurs when learners realize a gap in their learning, and they seek assistance to bridge the existing gap. Traditionally, they engage in conversations with instructors or peers for constructive advice in both face-to-face settings and online learning environments. The advent of technologies has greatly diversified learners’ help-seeking options. Studies on help-seeking focus predominantly on the correlational relationship between learners’ demographics like gender, major, characteristics, motivational beliefs, mastery or performance goals, and their help-seeking behavior without enough attention to learners’ online help-seeking pattern (Cheng et al., 2013, Tanaka, 2002). Research on help-seeking strategies can support students in a variety of formal and informal learning environments and their psychological decision-making.

A Fuzzy C-means Algorithm to Detect Learning Styles in Online Learning Environment

The ability to detect learners’ learning styles based on their learning behaviors is of utmost importance for online educational systems, as it greatly enhances student engagement, motivation, and overall learning outcomes. Knowing the learning preferences of learners may significantly aid in creating personalized learning recommendations and empower learners to identify their own learning styles. However, learners exhibit diverse behaviors in an online setting, which poses significant difficulties in detecting their learning style. This paper proposes a novel approach for detecting learning styles using graph representation learning techniques and machine learning algorithms. While our approach is not reliant on a particular learning style model, our approach may be divided into two distinct parts. Initially, we represented the behavior of learners as a bipartite graph and then transformed this graph into a lower-dimensional representation using the graph embedding approach. This lower-dimensional representation was then utilized for machine learning tasks. Furthermore, we categorize the encoded learner’s sequence using a clustering technique based on the selected learning style model. Our methodology uses the Felder-Silverman model as the learning style model and the Fuzzy C-means algorithm as a clustering technique. Our approach was evaluated using the 2015 KDD Cup dataset through a series of comprehensive experiments to showcase its effectiveness. The findings demonstrate that our approach surpasses the previous approach, achieving an average precision of 0.8737 and an accuracy of 0.9182.

Evaluation of factors affecting student acceptance of Zedemy using the Unified Theory of Acceptance and Use of Technology (UTAUT)

Due to the COVID-19 epidemic in March 2020, the government is mandated that schools in Indonesia implement School from Home (SFH) and use online instruction. A community-based learning platform called Zedemy was created during the COVID-19 pandemic to connect social media with online learning environments. The use of Zedemy has yet to be prevalent among teachers and students in Indonesia, as shown by the number of participants, 329 for all courses at Zedemy in 2021. This study used the UTAUT approach to assess user interest and acceptability based on users' voluntariness in utilizing Zedemy as an online learning tool during the COVID-19 epidemic. Surveys were sent to Zedemy users who have used at least one other online learning platform. The data will next be examined using the SmartPLS 3 tool and the SEM method. The findings showed that variables such as performance expectancy, effort expectation, social influence were affecting the behavioral intention of the user, meanwhile facilitating conditions was affecting the use behavior of the user. This research also found that moderating variables may have an impact on the model.

Research on Optimization of University English Practice Teaching Mode Based on Graph Structure in Online Learning Environment

Abstract This study investigates effective integration of node and edge features through N-way K-shot problem definition and iterative updating of graph structure information. The flexibility and effectiveness of the model are enhanced by using the gate function of the adaptive layer to control the degree of neighborhood aggregation and optimize the edge features through the double stochastic normalization technique. The introduction of the LGACN model strengthens the clustering performance through the Attention Network, and improves the adaptability and accuracy of the teaching model. The empirical Analysis shows that compared with the traditional method, the model has outstanding performance in enhancing students’ knowledge understanding, skill application and vocational quality, especially the student satisfaction in practical teaching effect and student-student mutual evaluation is significantly improved. Among the 256 students in the experimental class, the comprehensive satisfaction score increased from 68.15-80.21 to 80.21-89.89, significantly improving teaching effectiveness. By deeply optimizing the practical teaching mode of college English, this study provides new perspectives and effective strategies for language teaching in online learning environments, which helps to improve teaching effectiveness and student satisfaction.