Learning Tool Research Articles

Exploring the Impact of Social Media on English Language Learning: Opportunities and Challenges

This study seeks to elaborate on the influence that social media presents in the learning of the English language in Pakistan, together with the opportunities and challenges influenced by the phenomenon. The research object was to understand how media in Pakistan influence educational dynamics and language development within everyday life. The growing dominion of the English language as the lingua franca of the world would, therefore, make educators and policymakers understand the role that social media plays as host for language learning. The data collection method for this study was therefore done through a mixed-method approach across Pakistan with 30 participants from different linguistic and cultural backgrounds. Methods to use social media and keeping users engaged with English content were studied and presented as an effective tool for the most recent method of upgrading language skills. Finding out from our respondents further reiterated this, as a good number of them (97%) found social media helpful in improving their English language through substantial involvement with content in English and gains made in vocabulary and grammar. Quality of language, however, remains a persistent challenge amid informal styles of communication. In other words, the study suggests that while social media is an effective supplementary tool for language learning, yet at the same time, it should be used with a degree of caution within formal educational frameworks to optimise the benefits. It is precisely this research, then, that highlights strategies that could fully exploit such an educational potential while mitigating social media's weaknesses to improve English language education in Pakistan and other similar settings.

Explaining learning achievement in student experience of blended learning

Drawing on theories of student approaches to learning and sociomaterial perspectives on learning, we investigated how a combination of sociocognitive and sociomaterial variables explain variation in 365 students’ learning achievement in a first year human biology blended learning course in an Australian research intensive university. We used a close-ended questionnaire to measure students’ approaches to learning through inquiry, approaches to using online learning technologies, and their use of on-campus physical learning spaces. We obtained use of online learning tools in terms of frequency and duration through analytics provided by a proprietary learning management system. Students’ learning achievement was evaluated using six assessment tasks in the course. Correlation analyses were conducted to examine the relationship between approaches, use of online learning tools, use of oncampus physical learning spaces, and achievement. Based on the correlation results, we regressed learning achievement on approaches, use of online learning tools and physical learning spaces. The results showed that by introducing sociomaterial variables into the regression model, a significant proportion of learning achievement was explained over and above the explanations offered by sociocognitive factors alone, highlighting the important role of both sociocognitive and sociomaterial factors in blended learning.

Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.

Understanding digital transformation challenges for online learning and teaching in higher education institutions: a review and research framework

PurposeThe widespread uptake of digital technology tools for online teaching and learning reached its peak during the nationwide lockdown triggered by the COVID-19 pandemic. It transformed the higher education institutions (HEIs) marketplace both in developed and developing countries. However, in this process of digital transformation, several HEIs, specifically from developing countries, faced major challenges. That threatened to affect their sustainability and performance. In this vein, this study conducts a bibliometric review to map the challenges during the COVID-19 pandemic and suggest strategies for HEIs to cope with post-pandemic situations in the future.Design/methodology/approachThis comprehensive review encompasses 343 papers published between 2020 and 2023, employing a systematic approach that combines bibliometrics and content analysis to thoroughly evaluate the articles.FindingsThe investigation revealed a lack of published work addressing the specific challenges faced by the faculty members affecting their well-being. The study underscores the importance of e-learning technology adoption for higher education sustainability by compelling both students and teachers to rely heavily on social media platforms to maintain social presence and facilitate remote learning. The reduced interpersonal interaction during the pandemic has had negative consequences for academic engagement and professional advancement for both educators and students.Practical implicationsThis has implications for policymakers and the management of HEIs, as it may prove useful in reenvisioning and redesigning future curricula. The paper concludes by developing a sustainable learning framework using a blended approach. Additionally, we also provide directions for future research to scholars.Originality/valueThis study has implications for policymakers and HEI management to rethink the delivery of future courses with a focus on education and institute sustainability. Finally, the research also proposes a hybrid learning framework for sustainability and forms a robust foundation for scholars in future research.

The impact of a chatbot working as an assistant in a course for supporting student learning and engagement

AbstractNowadays, many digital learning tools can be integrated into a course to enhance the teaching–learning process. It is a fact that technology is changing how students learn and how professors teach. The growing utilization of AI chatbots in education to support students' learning underscores the importance of identifying critical aspects related to students' acceptance of this technology. Teachers require a deeper understanding of how students perceive and respond to chatbot assistance, as well as methods to measure levels of satisfaction, engagement, and knowledge acquired while using these technological tools. The aim of this study was, on the one hand, to illustrate the integration of a chatbot into a course to assist students in solving specific learning activities. On the other hand, it aimed to analyze the correlation between the factors influencing university students' intention to use chatbots and their perceived usefulness. It is noteworthy that the learning activity integrated two technologies: an Integrated Development Environment and a chatbot, offering additional educational value to students. The Technology Acceptance Model was used to measure the students' perceived usefulness and attitude toward this digital learning tool. The findings of this study provided insights to faculty on how to integrate and use chatbots to enhance students' engagement and motivation. The results revealed that the perceived usefulness of chatbots does, in fact, influence students' intention to use them. Most of the students expressed satisfaction with having a chatbot to assist them in solving exercises. Students highlighted that one of the most significant benefits was the 24/7 chatbot's support availability and its user‐friendly interface.

Brain-inspired learning in artificial neural networks: A review

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs’ operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to improve these networks’ capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. In this review, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.

Analysis and Prevention of AI-Based Phishing Email Attacks

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.

BRAVEST: A Visual Teaching Aid for Stroke Clinicians

Stroke clinicians incorporate essential stroke care measures into their professional practice within certified stroke centers on a regular basis. Supported by evidence-based guidelines, these time-sensitive care requirements target the unique needs of stroke patients making them essential for optimal translation to bedside clinical practice. Varied interprofessional staff learning styles call for educational instruction to be engaging and innovative to yield intended results. This article introduces a new learning tool called BRAVEST (B: Blood pressure; R: Rehabilitation; A: Antithrombotics; V: Venous thromboembolism prophylaxis; E: Education; S: Statin therapy; T: Thrombolysis/Thrombectomy), a custom-made visual aid that presents evidence-based, certification-required acute stroke care as a creative mnemonic infographic to enhance interprofessional stroke clinicians’ learning and retention.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Integration of infographics in higher education: challenges faced by novice creators

In higher education, the popularity of using infographics as teaching and learning tools is increasing. However, their design is a demanding task and the majority of students are inexperienced in creating this form of visual. To help educators integrate tasks of creating infographics more successfully and thus enhance the development of visual literacy in higher education, this study seeks to identify challenges faced by university students while creating infographics for the first time. The study participants were students of Informatics attending the course of Academic and Technical Communication in English. Data sources were their initial drafts of infographics created in small groups during a multi-step project. The results of the content analysis revealed that the most commonly observed challenges were difficulties while applying titles and subtitles, too substantial amount of textual information, inability to put information in a story-like format, inappropriate layout of information, inaccurate spelling, lack of references or absence of their correct attribution and use of unjustified text. Importantly, the study includes lists of attributes and recommendations necessary to design infographics, the appliance of which together with the use of professionally designed templates could result in effective infographics.

Perspectives of Students, Teachers and Parents on the WhatsApp Platform Remedy for Covid-19 Learning Restrictions in Tanzania

The study explored the WhatsApp platform's effectiveness as an alternative to traditional classroom learning during the COVID-19 pandemic in Tanzania by examining parents, students, and teachers’ perspectives regarding its use in education. The study employed the descriptive survey design. The design was particularly useful as the researchers sought to explore patterns or trends of its use in education. The study sample comprised parents, students and teachers who voluntarily participated in the study, reflecting a range of viewpoints from Tanzanian school communities. The study involved 125 participants from four schools. Data were acquired through a combination of telephone interviews, online group discussions and an online questionnaire, providing a comprehensive understanding of the participants' experiences and opinions on the use of the platform during the pandemic. Descriptive statistics, including frequencies and percentages, were applied to the quantitative data, whereas qualitative data analysis employed the thematic approach. The results indicate that WhatsApp is an effective classroom-learning platform, facilitating connectivity among students, parents and teachers during the pandemic. The study highlights benefits like improved parental involvement, enhanced student inquiry skills and subject mastery. The study recommends integrating WhatsApp into the curriculum and, during teaching, combining it with other digital tools for comprehensive student-directed learning.

Preservice Teachers’ Perceptions of AI in Education

This study explores the integration of generative AI tools into multiple-subject and single-subject teaching credential courses at California State University Sacramento during the Fall 2023 and Spring 2024 semesters. The study involved 210 teacher candidates per semester, most of whom had no prior experience with AI in education. Through a series of AI-focused assignments and a survey, the study investigated preservice teachers' perceptions, experiences, and concerns regarding the use of AI tools in educational settings. The assignments aimed to introduce AI tools, encourage experimentation, and facilitate discussions on the potential benefits and challenges of AI in education. The survey results revealed that a significant majority of participants (88.9%) used generative AI tools for personal learning or teaching preparation, with 92% using text generators like ChatGPT. However, opinions were mixed regarding the adoption of AI in future classrooms, with 49% in favor, 41.2% uncertain, and 9.8% opposed. Key concerns included overreliance on technology and ethical issues. The study concludes that while preservice teachers show enthusiasm for experimenting with AI tools and acknowledge their potential to enhance learning experiences, there is a need for ongoing training and support to ensure ethical and effective implementation. The findings suggest that incorporating AI assignments into the curriculum can provide valuable experiences and prepare preservice teachers for the growing role of AI in education.

Can open cholecystectomy be taught by cadaveric simulation?

Laparoscopic cholecystectomy (LC) is the current standard of treatment for surgical gallbladder removal as it has an overall improved post-operative recovery compared to Open Cholecystectomy (OC). This has resulted in the loss of exposure to surgical trainees and the associated technical skills and decision-making required to convert to OC. The aim of this study is to provide construct validity to the proposition that cadaveric simulation can be used successfully to teach and learn open cholecystectomy. Participants (n = 25) were surveyed on a 9-point questionnaire using a 5-point Likert scale to determine their opinion on cadaveric simulation as a tool for teaching OC. Overall respondents deemed the tool as highly translatable. There was no significant correlation in the responses between candidates versus tutors (P = 0.05, r = 0.51). The outcome of the survey revealed that participants agreed that cadaveric simulation is a positive learning tool to aid in OC.

Νέοι εκπαιδευτικοί, νέες συνθήκες και νέα εργαλεία για την εκπαίδευση παιδιών με μεταναστευτικό και προσφυγικό υπόβαθρο

The main objective of the Language Curriculum is to make the educational system responsive to the new conditions that have emerged in schools. Considering the requirements of the Curricula, new learning tools were used in this research and were applied in the context of language teaching at primary schools at the Department of Primary Education, University of the Aegean. These tools included a combination of theories, i.e. Cummins' (2005) theory for the development of Academic Language Proficiency, approaches about New Learning (Kalantzis & Cope, 2005) and Transformative Pedagogy (Cummins, 2005; Kalantzis & Cope, 2013). The aim of the research was for future teachers to learn how to support the multilingual students in schools in critically processing the diverse textual types in the language lessons, thereby achieving both the development of linguistic and communicative competence and the interaction of the school community with the wider society.

Education runs quickly violence runs slowly: An analysis of closed captioning speed and reading level in children’s television franchises

Closed captioning is a great example of how efforts in technological equity can benefit all, as closed captioning has become a common tool in childhood literacy and new language learning for adults. In comparison to traditional print reading forms closed captioning is in its infancy. Popular methodologies for assessing reading level of text were invented before closed captioning existed. This study is an investigates an aspect of closed captioning that has historically received less attention, the content and reading level of the closed captions. While considerable research attention has been given to the role of speed in relation to reading comprehension of closed captioning, less attention has been given to specific analysis of closed captioning content. This study analyzed closed captioning text extracted from 337 episodes of popular children’s television shows through the lens of content and reading level analysis using both traditional literacy tools and tools for analysis of closed captions. The goal was to provide insight for language teachers, parents of deaf children, and other relevant practitioners to make more informed and equitable decisions about children’s programming. The 21 shows sampled displayed clear patterns based on genres and age. The data also provided insight into methodological assessment differences between closed captioning and traditional print forms.

Developing interpretable machine learning model for evaluating young modulus of cemented paste backfill

Cemented paste backfill (CPB), a mixture of wet tailings, binding agent, and water, proves cost-effective and environmentally beneficial. Determining the Young modulus during CPB mix design is crucial. Utilizing machine learning (ML) tools for Young modulus evaluation and prediction streamlines the CPB mix design process. This study employed six ML models, including three shallow models Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particle Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (GB-PSO), Random Forest-Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid model exhibited superior performance (coefficient of determination R2 = 0.906, root mean square error RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dependence Plots (PDP) provided insights into component influences. Cement/Tailings ratio emerged as the most crucial factor for enhancing Young modulus in CPB. Global interpretation using SHAP values identified six essential input variables: Cement/Tailings, Curing age, Cc, solid content, Fe2O3 content, and SiO2 content.

Malware Detection using Machine Learning

As the level of malware and viruses is on the rise, the prominence of effective detection systems is crucial. Malwares are the modern-day threats that have troubled major companies worldwide. This article explores in depth two powerful machine learning tools, Random Forest, Support Vector Machines in particular, for the detection of malware. Our study revealed the Random Forest's capacity to reach the upper detection accuracy limit of 98% by applying an analysis of a dataset of variousmalware samples. The feature selection process as well as the model improvement that we've adopted have substantially improved use of our approach for malware detection, and this is thereby highly crucial for organizations to fight against evolving cyber threats. The results of the present research support the ongoing actionsof strengthening cybersecurity security, therefore, providing invaluable information for proactive defense approach mechanisms against malicious software attacks.

Intelligent Bus Tracking System with Attendance based on Facial Recognition

The Intelligent Bus Tracking System is a cutting-edge solution that integrates machine learning tools and face detection technology to revolutionize transportation services within academic institutions. Leveraging the power of artificial intelligence, this system enhances the accuracy, efficiency, and security of bus tracking while introducing innovative features such as facial recognition for identification to Student Attendance.(Saad et al., 2018) The bus tracking system is a comprehensive solution designed to enhance the efficiency and safety of transportation services within academic institutions. This system leverages modern technologies, including GPS and mobile communication to provide real time tracking and monitoring of buses operating within the academic campus. The primary goal is to optimize the transportation experience for students, faculties and staff while also improving overall operational management.(Sharif et al., 2018) Keywords: GPS, Tracking system, Attendance system Face Recognition, Machine Learning,

Developing Podcast-Based Learning Media for English Education Among Deaf Students

This study addresses the educational challenges faced by deaf students in Indonesia, focusing on developing and implementing a specialised Android-based application, "PODclusive," to facilitate English language learning. The research aimed to create an interactive learning tool that incorporates sign language and textual translations in both English and Indonesian, tailored to the needs of deaf learners. The methodology was a mixed-method approach, combining qualitative and quantitative analyses. The research and development (R&D) framework guided the application's design, development, and evaluation. Participants included a purposive sample of 30 deaf students from SLB N Pringsewu, ensuring a representation across different learning outcomes. The findings revealed that the PODclusive application significantly enhanced the learning experience for deaf students. Students demonstrated improved comprehension of English language concepts, facilitated by the application’s use of sign language video content and offline accessibility. The application was evaluated through expert reviews and user testing, receiving high validity scores for content, design, and educational impact. The implications of this study underscore the effectiveness of tailored educational technologies in addressing specific learning needs. The PODclusive application exemplifies how digital tools can enhance accessibility and engagement for deaf students, suggesting that further exploring similar technologies could be beneficial. This research contributes to the broader discourse on inclusive education, advocating for more innovative approaches to support diverse learning environments.

Effectiveness of digital and analog learning methods for learning anatomical structures in physiotherapy education

BackgroundAccording to the German Physiotherapy Education and Qualification Regulations, teaching of anatomical structures is one of the fundamental subjects of physiotherapy education. Besides exhibits and models, anatomy atlases are usually used as teaching and learning tools. These are available in both analog form such as printed books or in digital form as a mobile application. Furthermore, the use of digital teaching and learning tools is steadily increasing within the education of health professionals.AimTo assess the efficacy of a digital educational tool in contrast to an analog anatomical atlas in acquiring knowledge about anatomical structures.Material and methodThe data collection took place in the context of an anatomy tutorial for students of the bachelor’s degree program in physiotherapy. In a cross-over design, the students completed two learning assignments, each, with different learning materials provided, either with an anatomy app on a tablet or with an anatomy atlas as a book. The tests to assess the newly acquired knowledge immediately after the task, consisted of questions about the anatomical structures of the knee as well as the shoulder. In addition, the students’ satisfaction with the learning materials provided was surveyed using a questionnaire. The survey assessed their satisfaction, their assessment of learning success, and their affinity to digital learning materials. This was done using a 5-point Likert scale and a free-text field. The data was analyzed descriptively, and group differences were calculated using a t-tests.ResultsThirty students participated. The group comparison showed a significantly better outcome for the group that prepared with the analog anatomy atlas for the questions on the knee than the comparison group that used the anatomy app (t(28) = 2.6; p = 0.007). For the questions concerning the shoulder, there was no significant difference between the digital and analog groups (t(28) = 1.14; p = 0.26). The questionnaire revealed that satisfaction with the analog anatomy atlas was significantly higher than with the anatomy app. A total of 93.34% rated their experience with the analog learning tool at least “somewhat satisfied”. In contrast, 72.67% of students partially or fully agreed that they “enjoyed learning with digital learning tools”.DiscussionLearning anatomical structures with the Human Anatomy Atlas 2023 + app did not show a clear advantage when compared to an anatomy book in these two cohorts of physiotherapy students. The results of the questionnaire also showed greater satisfaction with the analog anatomy atlas than with the anatomy app, whereas most students stated that they frequently use digital learning tools, including some for anatomical structures. Satisfaction with the learning tool seems to play a central role in their effectiveness. In addition, sufficient time must be provided for users to familiarize themselves with the user interface of digital applications to use them effectively.RegistrationDiese klinische Studie wurde nicht in einem Studienregister registriert.