E-learning Research Articles

Exploring Cultural Diversity Awareness and Addressing Cultural Biases among Undergraduate Students in Online Learning Environments

The growing field of online education has developed within a cultural context rooted in many forms of inherent bias. Therefore, this mixed-methods study was conducted within a sociocultural theoretical framework. The population consisted of teachers and students from various backgrounds at some universities in Karachi. In qualitative research, twelve teachers from random universities were selected using a purposive sampling method. In quantitative research, the researchers used the simple random sampling method to select one hundred students. The researchers conducted interviews and surveys. The researchers analysed the quantitative data using the statistical analysis method in SPSS. The researchers manually analysed the qualitative data through thematic analysis. The study's findings show teachers encountered challenges and biases in synchronous and asynchronous online sessions. Cultural diversity is an important element and an anticipated norm in online learning environments. Teachers should develop cultural diversity awareness in students and use teaching strategies. This study also found that students aware of cultural diversity were satisfied with their online learning experience. The findings of this study add to the literature on online learning satisfaction and provide direction for solving problems related to online learning satisfaction.

Switching from In-person to Online Learning: Emotion and Temporal Focus in the Egyptian Tertiary Student Experience

Responding to the Covid-19 pandemic, the Egyptian government closed universities and ordering teachers online. Adjusting to online teaching was difficult for learners. This study of 26 students at a private Egyptian university used Voyant Tools and LIWC to explore their psychometric and cognitive responses to the first few weeks of online learning. Results indicate that stress and anxiety were common. Despite more time with family, social and cognitive process words were less frequent than the norm. Time words focused more on the present and the past than the future. Work words were more frequent than, but mentions of leisure less frequent than the norm. Since the pandemic, tertiary teaching and learning has made increasing use of online platforms. This is likely to continue in future. Negative emotions and the past temporal focus of these students highlights time as a problem for online learning, because virtual time has no boundaries, and may be experienced subjectively as ongoing in an unbounded or unlimited manner. In combination with social isolation, this will tend to enhance negative emotions. This highlights the need for online teaching to manage virtual boundaries, as clock time has in classroom teaching. Qualitative data analysis will remain a frontline tool in assessing learners’ experiences online, as universities go forward with this teaching delivery mode.

Online Kernel-Based Mode Learning

The presence of big data, characterized by exceptionally large sample size, often brings the challenge of outliers and data distributions that exhibit heavy tails. An online learning estimation that incorporates anti-outlier capabilities while not relying on historical data is therefore urgently required to achieve robust and efficient estimators. In this paper, we introduce an innovative online learning approach based on a mode kernel-based objective function, specifically designed to address outliers and heavy-tailed distributions in the context of big data. The developed approach leverages mode regression within an online learning framework that operates on data subsets, which enables the continuous updating of historical data using pertinent information extracted from a new data subset. By amalgamating the asymptotic distribution functions generated by local mode estimators from all data subsets, the newly suggested estimator is efficiently updated by minimizing a weighted least squares-type loss function. To facilitate this process, we suggest a modified mode expectation-maximization algorithm for numerical optimization, ensuring both storage friendliness and computational efficiency. We demonstrate that the resulting estimator is asymptotically equivalent to the mode estimator calculated using the entire dataset, provided that the covariates in each data subset are homogeneous and the errors are homoskedastic. Monte Carlo simulations and an empirical study are presented to illustrate the finite sample performance of the proposed estimator. Supplemental materials for this paper are available online.

Gender Digital Divide and Education: A Reflection from the University of Nairobi, Kenya

The use of digital technology safely and effectively is increasingly becoming important. Moreover, since the onset of the COVID-19 pandemic, digital technological skills have continued to transform the educational landscape, bringing a wide range of benefits to both students and teachers. This requires both men and women students and teachers to know how to use digital devices and connectivity tools for high-quality and future-focused teaching and learning processes. The objective of the study was to find out the extent to which gender digital divide exists in the Faculty of Education, University of Nairobi. Findings and information presented and discussed in this paper were obtained from a critical review of relevant literature based on gender and digital technology in education. In addition, using documentary analysis guide statistical data based on the number of women and men in selected Information, Communication and Technology courses was derived from the University of Nairobi’s E-Graduation booklets covering three consecutive academic years; 2020, 2021 and 2022. Descriptive data was computed using frequencies and percentages. Findings reveal that gender digital divide gap is clearly visible, for instance, there were only 6 and 8 men and no women graduates in Masters of Science in Computer Science and Masters in Education Technology respectively. Such gender disparity is likely to impact the number of women pursuing Information Communication and Technology-related careers. There is evidence to show that to some extent, the gender digital divide is a result of gendered-related social norms and stereotypes to poor advocacy of digital technological careers. Thus, there is a need to encourage both men and women students to study Information Communication and Technological courses with the aim of ensuring inclusivity and equitable education for everyone, while developing lifelong learning opportunities as stipulated in Sustainable Development Goals 2030

The effect of transcranial direct current stimulation and inhibitory control training on depression and anxiety among post-stroke individuals

BackgroundRecent research has highlighted the role of fronto-parietal brain networks and cognitive control in mood disorders. Transcranial direct current stimulation (tDCS) and computer-based cognitive training are used in post-stroke rehabilitation. This study examined the combined effects ofof computer-based inhibitory control training (ICCT) and anodal tDCS on post-stroke depression and anxiety.MethodsThirty-five participants were randomly assigned to one of three groups: active tDCS treatment (A), sham tDCS treatment with ICCT (T), or active tDCS with ICCT (AT), for a duration of ten days. Primary outcome measures included the Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HAM-D), and Spielberger’s State-Trait Anxiety Inventory (STAI-S/T). Statistical analysis was performed using a Mixed-model Analysis of Variance, with supplementary Bayesian analysis.ResultsThe AT group showed a significant improvement in BDI scores (p < .001), whereas no significant effects were observed on the HAM-D, STAI-T, or STAI-S scales.ConclusionsThe combination of tDCS and ICCT reduced depressive symptoms as measured by the BDI; while no significant effects were found with either treatment alone. Further research is needed to explore the mechanisms behind the synergistic effects in the treatment of post-stroke mood disorders.

Integrating CALL and AIALL for an interactive pedagogical model of language learning

Abstract Given the great potential of integrating Computer-Assisted Language Learning (CALL) and Artificial Intelligence-Assisted Language Learning (AIALL) to enhance language learning outcomes, there is a growing interest in exploring their combined effects. In this vein, the present study aimed to develop and test an interactive pedagogical model of language learning (IPMLL) by integrating CALL and AIALL elements in a combined module. To further investigate the effects of this model, a comprehensive evaluation was conducted, considering various aspects such as learner motivation, personalized learning experiences, and feedback effectiveness. The results indicate that (1) the integration of CALL and AIALL in the IPMLL positively influenced learner motivation, leading to greater involvement and active participation in language learning activities; (2) the personal learning interactions facilitated by the IPMLL, including adaptive instruction and intelligent feedback, contributed to improved language proficiency and learner satisfaction. Theoretically, this integration aligns with established pedagogical theories and frameworks, such as cognitive theories of multimedia learning, emphasizing the significance of interactive and technology-enhanced learning environments. Pedagogically, the IPMLL offers practical implications for teachers, highlighting the benefits of incorporating CALL and AIALL elements in language teaching methodologies. This study contributes to the growing body of research on technology-enhanced language learning and provides insights for future developments in this field.

Reflections on Using Interactive Engagement Applications in Higher Education Accounting Courses

This reflective article explores the use of audience response software (ARS) in higher education accounting courses – namely Wooclap. Based on personal teaching experiences, the article explores the use of ARS to enhance student engagement and active learning. These experiences include teaching in an online environment during the COVID-19 pandemic at Dalarna University and in physical classrooms at Malmö University. Together, these contexts demonstrate the versatility and effectiveness of ARS in different educational settings. The author reflects on the pedagogical benefits of integrating ARS as an interactive application, noting that it facilitates real-time feedback, encourages student participation, and supports a constructivist approach to learning. The article also addresses the limitations of Wooclap, including its unclear impact on student attendance and performance. Although Wooclap and similar ARS have proven effective in enhancing engagement, their long-term benefits may diminish over time, indicating a need for further research and alternative strategies to maintain student involvement. The findings contribute to the broader discourse on technology-enhanced learning (TEL) in higher education, emphasizing the critical role of interactive applications in creating a more engaging and responsive learning environment.

Preparing Pre-Service Teachers for Teaching in the Digital Age

At the onset of the COVID-19 pandemic, many traditional contact higher education institutions, such as the institution where the study was based, transitioned abruptly to remote online learning. Students in the School of Education at the institution who are predominantly from the lower end of the socioeconomic digital divide in South Africa had no choice but to navigate digital technologies to further their education. The extent to which their digital learning experience fostered the requisite TPACK (Technological, Pedagogical, and Content Knowledge) and other cross-functional skills needed for the 4IR classrooms remained uncertain. Framed by the TPACK Model, this paper employed a mixed-method approach to understand students' online learning experiences and how their digital learning experiences prepared them for their future teaching practices. Data generated from questionnaires randomly distributed to undergraduate students, followed by a focus group interview with twelve purposively sampled students across the five undergraduate clusters of the school, was used to address the research objective. The findings revealed that it is unlikely that their virtual learning experience could have prepared them for teaching in the digital age. Insights from this paper will benefit academics in their online pedagogical engagements and curriculum design while informing policy directions at the institutional level. Keywords: digital learning, pre-service teachers, 4IR classroom, TPACK, students' experiences

Identifying the Most Impactful Research Fronts in the Digital Education Ecosystem: Formulation, Metrics, and Insights

Research fronts are dynamic, knowledge-driven clusters of scholarly activity that emerge in response to pressing problems and/or groundbreaking discoveries. Clarivate Analytics provided a valuable tool based on Citation Productivity and Trajectory (CPT) indicator, which successfully identified particularly hot research fronts on a global scale. To enhance the accuracy and comprehensiveness of identifying both active and emerging research trends, this study develops an extended Clarivate formulation incorporating a novel Impact Factor (IF) metric. The refined approach incorporates growth rates, publication productivity, and the average publication gap between published and citing publications. This method is applied to exploring key research fronts in the digital education ecosystem using bibliometric data from the Scopus database in the period of 2019-2023. The results reveal that artificial intelligence and online learning are the most prominent and influential fields, with virtual reality, blockchain, hybrid learning, and digital literacy representing fast-growing areas. By analyzing both quantitative and qualitative aspects, this work informs key stakeholders about the evolving priorities and trends in the digital educational landscape.

The Challenge and Response of Network Security in The Era of Artificial Intelligence

With the emergence of artificial intelligence, new situations, new problems, and new challenges in the field of network security are emerging one after another, affecting the global economic pattern, development pattern, and security pattern, also putting forward higher requirements for network security assurance. Nowadays, cases of using fake audio and video such as AI face-changing and AI voice-changing to commit new cybercrimes occur occasionally. From the individual level, artificial intelligence lowers the threshold for personal cybercrime. At the social level, artificial intelligence forges false information that misleads the public and poses a certain threat to people’s property. This article aims to analyse the function of artificial intelligence and discuss the impact of emerging technologies on social development. This article by comparing domestic and foreign researches on artificial intelligence, using a qualitative research methodology with case studies and literature, and expresses views and ideas regarding the laws about artificial intelligence. The article finds that maintaining network security is the common responsibility of the whole society and requires the joint participation of governments, social organisations, and the majority of netizens. Therefore, it is suggested that other security risks that may be caused by artificial intelligence in the future, should be prevented based on risk prevention, and active use laws and regulations to regulate and guide. Secondly, by referring to the law, ensuring the protection of personal information, and standardise the cyber security environment is so significant. Finally, carry out online publicity and education to improve the network security awareness.

Mass Casualty Incident Training in Immersive Virtual Reality: Quasi-Experimental Evaluation of Multimethod Performance Indicators.

Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking. This study aimed to investigate whether different performance indicators based on visual attention, triage performance, and information transmission can be effectively extended to MCI training in iVR by testing if they can discriminate between different levels of expertise. Furthermore, the study examined the extent to which such objective indicators correlate with subjective performance assessments. A total of 76 participants (mean age 25.54, SD 6.01 y; 45/76, 59% male) with different medical expertise (MFRs: paramedics and emergency physicians; non-MFRs: medical students, in-hospital nurses, and other physicians) participated in 5 virtual MCI scenarios of varying complexity in a randomized order. Tasks involved assessing the situation, triaging virtual patients, and transmitting relevant information to a control center. Performance indicators included eye-tracking-based visual attention, triage accuracy, triage speed, information transmission efficiency, and self-assessment of performance. Expertise was determined based on the occupational group (39/76, 51% MFRs vs 37/76, 49% non-MFRs) and a knowledge test with patient vignettes. Triage accuracy (d=0.48), triage speed (d=0.42), and information transmission efficiency (d=1.13) differentiated significantly between MFRs and non-MFRs. In addition, higher triage accuracy was significantly associated with higher triage knowledge test scores (Spearman ρ=0.40). Visual attention was not significantly associated with expertise. Furthermore, subjective performance was not correlated with any other performance indicator. iVR-based MCI scenarios proved to be a valuable tool for assessing the performance of MFRs. The results suggest that iVR could be integrated into current MCI training curricula to provide frequent, objective, and potentially (partly) automated performance assessments in a controlled environment. In particular, performance indicators, such as triage accuracy, triage speed, and information transmission efficiency, capture multiple aspects of performance and are recommended for integration. While the examined visual attention indicators did not function as valid performance indicators in this study, future research could further explore visual attention in MCI training and examine other indicators, such as holistic gaze patterns. Overall, the results underscore the importance of integrating objective indicators to enhance trainers' feedback and provide trainees with guidance on evaluating and reflecting on their own performance.

The Correlation Between Self-Disclosure, Academic Hardiness, and Academic Stress on Vocational High School Students

Implementing an independent curriculum aims to create a flexible teaching and learning process but often creates academic pressure that impacts student academic stress. In this case, students need to recognize the conditions and situations that exist so that self-disclosure becomes an important step that needs to be owned, as well as a resilient personality that makes students able to deal with stress and accept academic situations that arise as a step of self-development. The research was conducted on 238 students of vocational high school “X” students in Surabaya using multiple linear regression analysis techniques. The results showed that self-disclosure and academic hardiness correlate with vocational students' academic stress. Students with self-disclosure can express self-information, supported by the personality of academic hardiness, which makes a person stronger, resistant, and optimistic so that academic stress decreases. The following result shows that self-disclosure negatively correlates with vocational students' academic stress. Self-disclosure with the closest person can reduce academic stress because problems and conflicts in the surrounding environment can be expressed to their friends. The third hypothesis result shows that academic hardiness has a negative correlation with the academic stress of vocational students. Students with resilient personalities can deal with stressors positively because they can identify activities around them.

Anxiety in Online and Face-to-Face English Classes Amongst Japanese University Students During and After the COVID-19 Pandemic

The COVID-19 outbreak in 2020 prompted a sudden transition from in-person to online education in Japan. This research examined English language learning anxiety in virtual versus traditional classroom settings among 105 Japanese university students at a national university in Ibaraki Prefecture between 2020 and 2023. Students completed surveys about their anxiety levels in both learning environments. Statistical analysis using t-tests showed no significant differences in anxiety between online and in-person classes. Analyses by gender and academic year also revealed no notable distinctions. While mean anxiety scores were slightly higher for traditional classes, female students, and third-year undergraduates, these differences were not statistically significant. The results indicate that foreign language anxiety continues to be a concern in both online and face-to-face English classes for these students during the 2020-2023 period. Additional research into factors affecting anxiety across different learning contexts is needed. This study provides initial evidence that the shift to online learning due to COVID-19 in 2020 did not significantly change anxiety levels for Japanese university students studying English through 2023.

How technology influences English learning attainment among Chinese students.

The purpose of this research includes analyzing the interaction between online English learning motivations, digital readiness, academic engagement, self-regulated English learning attainment, and technology self-efficacy. These interactions were examined based on data gathered from learners in online English courses through the method of structural equation modeling. Analysis found that online English learning motivation has a significant impact on the learners' level of digital readiness and levels of academic engagement, thereby underlining its importance in getting learners ready for meaningful navigation of the digital environments with emphasis on specific academic tasks. Moreover, digital readiness and academic engagement mediate the link between online English learning motivation and online self-regulated learning, thus signifying the role of these variables in achieving translation of motivation into efficient self-regulation. Moreover, online English learning motivation exerts an indirect impact on digital readiness and academic engagement through the moderation of technology self-efficacy; in particular, the high level of self-efficacy strengthens the positive effects of motivation. This basically emphasizes the confidence in technology usage as a fundamental dimension to the effectiveness of online learning. Hence, based on these findings it can be concluded that increasing online English learning motivation, readiness for digital learning environments and academic engagement as well as the technology self-efficacy is important in order to achieve the greatest learning success in the field of online English learning. Although the study has its drawbacks like the use of self-reported data and cross-sectional research design, the study present implications that would be useful to educators and policymakers in the improvement of the online education strategies. The next steps in the research should involve following up the participants over a long period and using both qualitative and quantitative methods to augment these results.

Opportunities and Challenges in Harnessing Digital Technology for Effective Teaching and Learning

Most of today’s educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines.

Optimizing Decentralized Online Learning for Supervised Regression and Classification Problems

Decentralized learning networks aim to synthesize a single network inference from a set of raw inferences provided by multiple participants. To determine the combined inference, these networks must adopt a mapping from historical participant performance to weights, and to appropriately incentivize contributions they must adopt a mapping from performance to fair rewards. Despite the increased prevalence of decentralized learning networks, there exists no systematic study that performs a calibration of the associated free parameters. Here we present an optimization framework for key parameters governing decentralized online learning in supervised regression and classification problems. These parameters include the slope of the mapping between historical performance and participant weight, the timeframe for performance evaluation, and the slope of the mapping between performance and rewards. These parameters are optimized using a suite of numerical experiments that mimic the design of the Allora Network, but have been extended to handle classification tasks in addition to regression tasks. This setup enables a comparative analysis of parameter tuning and network performance optimization (loss minimization) across both problem types. We demonstrate how the optimal performance-weight mapping, performance timeframe, and performance-reward mapping vary with network composition and problem type. Our findings provide valuable insights for the optimization of decentralized learning protocols, and we discuss how these results can be generalized to optimize any inference synthesis-based, decentralized AI network.

A Feminist Scholars Collective Supporting the Growth and Dissemination of a Digital Guide: A Collaborative Autoethnography

This paper explores our experiences as scholars in higher education who collaborate as part of an informal collective supporting the Feminist Pedagogy for Teaching Online digital guide[1]. We, the authors, have diverse professional and educational backgrounds; our areas of research interest also vary significantly. However, we have a passion for humanizing online learning experiences and practically applying feminist pedagogical tenets to these interactions. The purpose of this paper is to explore, through a process of self-reflection, our experiences as scholars in higher education as part of an informal collective supporting the Feminist Pedagogy for Teaching Online digital guide. To share our experiences as editors of this digital guide, we included our individual stories using a collaborative autoethnography approach. In our stories, we specifically discussed our interest in joining this collective of feminist scholars, the evolving nature of our efforts in support of the digital guide, the success experienced, the challenges that we encountered, and the internal and external support we received throughout this journey. Ideally, through this critical reflection, we can aid other collectives who already engage, or are considering engaging, in similar scholarly communication endeavors. [1] This digital scholarship project works towards communicating liberatory pedagogical principles for educators in digital modalities.

ISOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression and multi-label classification

The task of feature ranking has received considerable attention across various predictive modelling tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have so far predominantly focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees, and additionally extend it to task of online feature ranking for multi-label classification. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the ranking convergence of iSOUP-SymRF in terms of the methods’ parameters, the size of the ensemble and the number of selected features, as well as their stability under different random seeds. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression and multi-label classification methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings obtained from iSOUP-SymRF.

Pre-University Students’ Perceptions towards Mathematics in the Post-pandemic Era

The Covid-19 pandemic that had begun in December 2019 had made all students in Malaysia being thrown into the online learning environment. After the prolonged home schooling, the post pandemic era that was declared in the year 2022 forced the students to attend physical classes again. This may have caused confusion among the students as they had to readapt with their old learning style once more after being comfortable with the online environment. In this research, students’ perceptions towards mathematics concerning their self-concept and achievement were explored. Participants of this study were a group of pre-university students who experienced the shift from online to physical classes in the post pandemic era. A set of questionnaires regarding the students’ perceptions was distributed to the students of the pre-university program at Universiti Kebangsaan Malaysia. The results show that 61.3% students discovered mathematics as scary despite they perceived positively on their achievement in mathematics. In conclusion, the students had moderate level of self-concept and achievement. Furthermore, there was a positive correlation between self-concept and achievement in mathematics, and there was no significant gender difference on the students’ self-concept.

Taking a Pulse on Quality and Safety Education

Background: The COVID-19 pandemic radically reshaped nurses’ education with a prominent shift to online learning. While there are emerging studies on quality and safety education post-pandemic, large-scale studies examining the pandemic’s impact on nurses’ quality and safety education are not available. Purpose: We examined quality and safety education in a national sample of nurses who were first licensed to practice in 2020, 2021, and 2022, adjusting for demographic, education, and work characteristics. Methods: We used correlational research design to analyze data from 821 nurses, which corresponded to a weighted population estimate of 170 824. Weighted logistic regression was used to model the nursing degree type on quality and safety education variables. Results: Approximately 90% of nurses reported receiving sufficient training in patient-centered care, teamwork and collaboration, and evidence-based practice. Approximately 75% received sufficient training in quality improvement. Conclusions: In a national survey conducted post COVID-19 pandemic, nurses reported a high level of preparedness in quality and safety education. One area that may require additional attention is quality improvement education.