Learning Technology Research Articles

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

AbstractDiabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

Examining TPACK Enactment in Elementary Mathematics with Various Learning Technologies

As schools continue to invest more resources into learning technologies and their infrastructure, there is a need to consider ways to support teachers’ professional learning related to the appropriate uses of learning technologies. The construct of TPACK continues to provide a framework for the field to help make sense of the knowledge and skills related to effectively integrating learning technologies into teaching. The article includes three vignettes that describe research-aligned enactments of TPACK using three different learning technologies: an internet-based activity focused on lower-level fluency, an internet-based simulation/game on budgeting, and an internet-based open-ended tool. The three vignettes suggest that TPACK can be enacted regardless of the learning technologies and that the culture and context of schools greatly influences whether and how teachers integrate technology into their mathematics teaching.

Asthma Care Chatbot

Asthma is a chronic respiratory condition affecting millions of people worldwide, characterized by inflammation and narrowing of the airways, leading to symptoms such as coughing, wheezing, and shortness of breath. Managing asthma effectively requires timely intervention and personalized care to alleviate symptoms and prevent exacerbations. In this context, the Asthma Care Chatbot serves as an innovative solution to assist individuals in managing their asthma symptoms more efficiently. Leveraging advancements in technology and machine learning, this chatbot provides personalized recommendations based on user-reported symptoms, age, and gender. By integrating a user-friendly graphical interface, the chatbot aims to enhance accessibility and usability for individuals seeking guidance on managing their asthma condition. This project utilizes a machine learning model trained on a dataset comprising asthma severity data to predict the severity of symptoms reported by users. Through an intuitive interface, users can input their symptoms, demographic information, and receive real-time predictions regarding the severity of their asthma symptoms. Based on the predicted severity level, the chatbot offers tailored recommendations, ranging from self-care measures to prompt medical intervention. Furthermore, the chatbot's performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score, ensuring its reliability and effectiveness in providing accurate predictions. By continuously refining its recommendations based on user feedback and data-driven insights, the Asthma Care Chatbot aims to empower individuals to take proactive steps in managing their asthma condition and improving their quality of life. Asthma Care Chatbot represents a pioneering approach towards personalized asthma management, bridging the gap between individuals and accessible healthcare solutions through the integration of technology and machine learning. By offering personalized guidance and support, the chatbot endeavours to empower individuals in effectively managing their asthma symptoms and promoting better.

Mobile Assisted Language Learning: Ecuadorian Undergraduate Polytechnic Students' Perceptions

Despite the increased use of mobile devices for language learning, little research has examined Ecuadorian university students' perceptions of Mobile-Assisted Language Learning (MALL). This study aims to explore undergraduate polytechnic students' perceptions of using MALL for English skills development. Also, it looks at the different factors associated with the acceptance of technology and identifies those that are more common. This quantitative research design uses a survey adapted from the Technology Adoption Model (TAM). It counts with the participation of 126 Ecuadorian undergraduate polytechnic students taking English as a foreign language classes. Participants were very positive about using MALL applications to learn English during their off-classroom time. The average total mean score was 4.373. It was also determined, through multiple regression analysis, that Perceived Usefulness (t = 6.025, p < 0.000), Social Influence (t = 2.876, p < 0.000), and Perceived Outcomes (t = 3.903, p <0.000) were the highest predictors of MALL acceptance. The two main contributions of this study to the literature are that students feel a connection between their language learning aims and attaining them by using MALL. Perceived Outcome is a high predictor of MALL usage. The findings suggest that Ecuadorian educational practices should increasingly integrate Mobile-Assisted Language Learning (MALL) technologies, as students perceive them as highly useful, especially in improving language skills, fostering social collaboration, and offering personalized, flexible learning experiences that align with their technological readiness and cultural context.

Mapping the scientific landscape and evolution of the international journal of surgery: A scientometric analysis (2004-2024)

Aim: The purpose of this study was to analyze the publication characteristics and development of the International Journal of Surgery over its 20-year history. Methods: This study included articles published in IJS during the 20 years from 2004 to 2024. The data were analyzed using the Citespace, VOS viewer, and the ‘Bibliometrix’ package in R software. We studied the dynamics and trend patterns of IJS literature production through descriptive bibliometrics and identified the most prolific authors, publications, institutions, and countries. Bibliometric maps were utilized to visualize published articles’ content and identify the most prolific research terms and topics in IJS, as well as their evolution over time. Results: A total of 5964 publications in the IJS from 2004 to 2023 and 356 publications in 2024 were included in the analysis. The study revealed a positive trend in literature production, although the number of articles published in IJS has slightly decreased recently. The most productive country was China (n=1,211), the most productive institution was Sichuan University (n=151), and the most prolific author was Wang Yang (n=87). The top 15 most cited articles focus primarily on research reporting standard guidelines for surgical studies. Research published in IJS mainly targeted middle-aged and older adults, emphasizing postoperative complications and treatment outcomes. The future research focus in IJS might center on integrating artificial intelligence and deep learning technologies to revolutionize surgical research and practice. Conclusions: The International Journal of Surgery significantly contributes to advances in surgical research. IJS prioritized enhancing patient outcomes and advancing surgical techniques, focusing on middle-aged and older adults, postoperative complications, and treatment outcomes. The journal emphasized robust evidence through retrospective, controlled, and cohort surgical studies. Integrating artificial intelligence and deep learning represented a significant frontier poised to revolutionize surgical care, shaping the future landscape of research and practice.

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Calculation of Aortic Arch Calcification Degree in Hemodialysis Patients Using Deep Learning

Abdominal Aortic Calcification (AAC) is a common form of vascular calcification closely associated with atherosclerosis and serves as an important marker for measuring increased risk of cardiovascular, cerebrovascular, and peripheral vascular diseases. Particularly in patients with Chronic Kidney Disease (CKD) and those undergoing dialysis, the risk of AAC significantly increases due to a combination of traditional and non-traditional risk factors. Therefore, developing a rapid and accurate method to assess the extent of AAC is crucial for preventing the progression of vascular calcification and the associated risk of cardiovascular diseases. Dialysis patients are required to undergo an abdominal X-ray annually, and the degree of calcification of the abdominal aorta is assessed manually through these X-ray images. However, these methods have limitations in identifying subtle calcifications in the abdominal aorta and the assessment process is time-consuming and depends on the experience and subjective judgment of physicians. To overcome these limitations, we propose a new method that incorporates deep learning technology to improve the accuracy of assessing the extent of AAC. Our method utilizes CNN models and attention modules to enhance the model's ability to recognize features of abdominal aortic calcification.

Problematika Metode Pengajaran dalam Pendidikan Islam Modern

Teaching methods play an important role in achieving educational goals, including in modern Islamic education. However, in practice, there are a number of problems faced by educators. With a literature study approach, this paper aims to identify and analyze various challenges that arise in the application of teaching methods in modern Islamic education, such as the gap between the curriculum and social reality, the dominance of conventional teaching methods that are less interactive, and the limited use of technology. The influence of globalization and technological developments also affect student learning patterns and pose new challenges in maintaining Islamic identity and values. To overcome these problems, innovation is needed in teaching methods, integration of technology in learning, alignment of the curriculum with the needs of the times, and improvement of the quality of educators. Thus, modern Islamic education can be more relevant and effective in forming a generation of Muslims who are noble, intelligent, and able to compete in the global era.

Use of ICT in English Language Teaching Classroom

Information Communication Technology (ICT) in teaching and learning is indispensable for improving teachers' professional skills in English language teaching (ELT). This paper examines secondary English language teachers' experiences in using ICT in teaching English in Nepal. The extensive semi-structured interview as a key information-gathering strategy under the phenomenological research method was employed to explore the participants’ experiences in applying ICT skills in teaching English. Four English language teachers who had been using ICT in teaching for at least two years were selected purposively from two public secondary schools in Rupandehi, Nepal. The findings revealed that the use of ICT engages students' cognitive processes and their autonomy in class. It also creates an engaging and dynamic atmosphere for learning and facilitates better access to learning materials. The study can contribute to improving teaching and learning efficacy using ICT skills for effective teaching in public schools in Nepal.

Game-based Learning for Science Education in Institutional Care Settings in Northwestern Mexico

Play has been key to knowledge acquisition, creativity, and innovation. In education, game-based learning (GBL) is a physical and digital environment that enhances learning outcomes in students. This paper presents the results of observational research on game-based learning in science education as a motivation for young students from two institutional care settings in Northwestern Mexico, in the State of Sonora. The observational research touched on 1) game-based learning in science, technology, engineering, and mathematics (STEM) education, and 2) learning motivation in vulnerable student groups. This paper focuses on the authors' experiences, most of them early researchers focused on science education, their purpose of promoting game-based learning in STEM education, and interviews with representatives from both institutions. We found that game-based learning was seen as a novelty, innovative, and motivational way of engaging young students from institutional care settings to learn and become curious about the STEM field. We also designed and created games focused on STEM subjects in both institutions. Students living in institutional care settings face even greater obstacles regarding education. In some instances, the lack of mentorship or follow-up tutoring activities can determine their academic outcome or lack thereof. With the ever-changing world of technology, industries demand highly qualified people in STEM fields. Nonetheless, international organizations such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organisation for Economic Co-operation and Development (OECD) have reported that school systems around the world are not equipped with the proper teaching methodologies for students to acquire basic skills such as mathematics nor critical thinking. In Mexico, public school systems and institutional care settings often lack teacher training or mentorship approaches to promote STEM education. This paper concludes with a reflection on the benefits of GBL in STEM education in such settings and suggestions for student-led collaborations with children and youth in such settings.

ISETECH-Statistics and Machine Learning Based Decision-Making for Diet Beverage Choice and Recommendation through Software Application

Positioned within the realm of ubiquitous beverage chains acclaimed for their diverse coffee and tea offerings, this investigation deeply explores the intricate nutritional dynamics and caloric compositions of these widely consumed beverages. At its core, the research aims to streamline decision-making for individuals actively seeking health-conscious beverage alternatives. This comprehensive endeavour unfolds through an integrated methodology that combines hypothesis testing and other statistical techniques with the establishment of a robust decision support system, complemented by powerful machine learning techniques. Distinctively, the paper integrates an intuitive React application (ReactApp) into the robust Fast API framework. Users engage seamlessly with the decision support system, as Fast API efficiently manages data processing, interfaces with machine learning algorithms, and delivers personalized recommendations to the ReactApp front. In essence, this interdisciplinary initiative epitomizes the fusion of nutritional science, statistical analysis, machine learning, and modern web technologies, providing a holistic and pioneering solution for selecting health-conscious beverage alternatives. One of the most noteworthy outcomes of our research lies in the compelling results we've achieved. Our predictive model has demonstrated exceptional performance: We achieved an impressive Accuracy Rate of 83.67%, signifying the high precision of our recommendations in identifying health-conscious beverage alternatives. Simultaneously, the F1-Score, which harmonizes precision and recall, stands at a commendable 82.21%, indicating a well-balanced and effective decision support system. Our model's ability to recall health-conscious choices is also strong, with a Recall rate of 83.67%. Furthermore, we assessed the model's agreement with observed data using Cohen's Kappa, which yielded a substantial score of 78.01%. This indicates not only the model's predictive power but also its ability to capture agreement beyond what might occur by chance alone. These results, seamlessly integrated into our research narrative, underscore the effectiveness and reliability of our approach. They constitute a rare and valuable contribution to the field, promising a novel pathway for informed and health-driven consumer choices, firmly supported by our rigorous methodology and advanced machine learning outcomes.

Acceptance and use of Video Games for Education by Secondary School Teachers

Video games (VGs) are frequently described as a transformational force within education and the use of video game technology for learning is becoming more common across all education sectors. Despite this, most literature regarding VGs in education focuses on measuring specific learning or engagement outcomes, and there is less of an emphasis on the attitudes of teachers on the uses of VGs in the classroom. A comprehensive understanding of teacher attitudes towards VGs within education is essential for their successful implementation within the classroom. This paper will present the development and progress of a PhD project that aims to explore the factors that influence secondary Australian teachers’ attitudes towards, and intentions to use, VGs within their own teaching. An online survey was used to collect data on teacher beliefs about technology and VGs and the factors that influence VG acceptance. A subsample of participants were also interviewed about their experiences and opinions in relation to VGs. This paper focuses on the proposed thematic analysis of semi-structured interviews. Respondents were divided into groups based on their opinions of, and level of experience with, VGs in teaching. The qualitative results provide a detailed and nuanced investigation of teacher attitudes and beliefs in relation to their competencies, obstacles, and facilitators of VGs use for teaching. The findings of this research will assist in building a deep understanding of VG use in secondary classrooms assisting game developers, administrators, teachers, and policy makers in decisions relating to effective and meaningful video game use within education.

Target detection of helicopter electric power inspection based on the feature embedding convolution model.

This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. First, simulation experiments and model analysis determine the keyframe and flight trajectory. Second, an improved FEC model is proposed, extracting features from aerial images in large ranges in real time and accurately identifying and classifying electric power inspection targets. In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. In conclusion, the proposed model improves the accuracy and scope of inspection, provides a more scientific strategy for electric power inspection, and ensures inspection efficiency.

Malware Detection using Deep Learning (DL)

The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods. In view of the low accuracy and high false positive rate of traditional malware detection methods, this research proposes a fine-tuned deep learning model with a novel dataset that is compared with ANN, Support Vector Machine (SVM), random forest (RF), K-Nearest Neighbourhood (KNN) classifiers. Converting a malware code into an image could allow users to effectively identify the presence of malware, even if the original code is modified by the creator. This is due to the fact that the attributes of images remain unchanged, allowing for reliable identification. So, researchers used deep learning technology to detect malware, like detecting malaria from red blood cells. The deep learning model found that a more detailed analysis of malware data sets, focusing on RGB and greyscale images, is needed. These data sets currently rely on publicly available data, but the accuracy of the traditional model could be a lot higher. It also produces many false positive results. The main goal is to create a new data set and model using malware images to identify and categorize malware using deep learning without relying on existing image detection and transfer learning models. The researcher adjusted different hyper parameters, like the number of neurons, filters, stride, hidden units, layers, learning rate, batch size, activation function, optimizer, and epochs. Identifying and correcting issues in models, improving their clarity, stability, and fairness, as well as debugging and monitoring them, is a challenging task. To eliminate this obstacle, assess the model's behaviour and performance by employing various methods such as logging, profiling, testing, visualization, and model clarity. To overcome the challenges posed by the electricity shutdown, we utilized Google's cloud-based GPU and Python 3.7 language to conduct the experiment and train the model. Kali Linux is an operating system that can automatically encrypt file systems and has a lower chance of crashing the system due to malware in a virtual sandbox. The researcher used techniques like early stopping and cross-validation to prevent over fitting and assess generalization in addition to monitoring and evaluating the model's behaviour and performance. The researcher used different methods like L1 and L2 regularization, dropout, and batch normalization to improve the model's performance and avoid over fitting. The binary portable executable (PE) malware dataset is collected from Kaggle, Malimg, Virusshare, Malvis, MS Big2015, and VX-underground and finally converted to greyscale and RGB images to create a novel dataset to fill the lack of image dataset. The raw dataset was then rescaled to a 128x128 greyscale and RGB (red, green, blue) image and flattened to 1024-byte vector images input into convolution neural network (CNN) interpolation to extract features for malware detection. The newly acquired dataset is utilized as an input for the innovative DL algorithms in order to create a tailor-made model capable of accurately predicting malware. The findings in this customized CNN model by fine-tuning hyper-parameters with the three-channel RGB dataset outperformed a greyscale dataset with an accuracy of 98.7% and error rate of 1.3% in current malware compared with other artificial neural network (ANN), ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and also Random Forest (RF) models. The proposed approach is compatible with all operating systems (Windows, Linux, and Mac) and can identify different types of malwares, such as packed, polymorphic, obfuscated, metamorphic, or variations of a malware family. There are some limitations to the shortage of malware image datasets and computational costs for fine-tuning hyper parameters in the whole model. Furthermore, the proposed approach detects malware and groups it into families without using common techniques like disassembly, decompilation, de-obfuscation, or running malicious code in a virtual environment.

Publications on Online Learning Technology for Mathematics and Science: Bibliometric Computational Mapping Analysis using VOSviewer

This research aimed to explore the trends of research on online learning technology for mathematics and science. The data were obtained from Scopus database using the Publish or Perish application as a reference management tool. Subsequently, the computational mapping analysis was conducted using VOSviewer with a bibliometric approach. The keywords used as a guide for the search process were “technology” AND “online learning” AND “math” OR “science”. Over a span of ten years (2014 to 2023), a total of 458 relevant articles indexed by Scopus were identified. The results showed that research on online learning technology for mathematics and science were categorized into four terms, namely technology, online learning, mathematics, and science. The term "technology" fell within cluster 1, consisting of 149 links, a total link strength of 4936, and 324 occurrences. Online learning was categorized within cluster 2, comprising 149 links, a total link strength of 2921, and 181 occurrences. Mathematics was classified within cluster 4, with 138 links, a total link strength of 939, and 54 occurrences. Lastly, science was also categorized in cluster 4, with 129 links, a total link strength of 2420, and 167 occurrences. The data analysis revealed a fluctuation in research output on online learning technology for mathematics and science. The number of articles increased from 22 in 2014 to 25 in 2015, followed by a decrease to 17 in 2016, increased to 20, 21, 32, 40, 99, and 125 in 2017-2022, and finally decreased to 57 in 2023. These results were illustrated by VOS viewer analysis. The discussion presented can serve as a foundational point for future research on related topics.

Between world models and model worlds: on generality, agency, and worlding in machine learning

AbstractThe article offers a discursive account of what generality in machine learning research means and how it is constructed in the development of general artificial intelligence from the perspectives of cultural and media studies. I discuss several technical papers that outline novel architectures in machine learning and how they conceive of the “world”. The agency to learn and the learning curriculum are modulated through worlding (in the sense of setting up and unfolding of the world for artificial agents) in machine learning engineering. In recent computer science articles, large models trained on Internet-scale datasets are framed as general world simulators—despite their partiality, historicity, finite nature, and cultural specificity. I introduce the notion of “model worlds” to refer to composable interactive environments designed for the purpose of machine learning that partake in legitimising that claim. I discuss how large models are grounded through interaction in model worlds, arguing that model worlds mediate between the sheer scale of language models and their hypothetical capacity to generalise to new tasks and domains, rehashing the empiricist logic of “big data”. Further, I show that the emerging capacity of artificial agents to generalise redraws the epistemic boundary between artificial agents and their learning environments. Consequently, superficial statistics of language models and abstract action are made meaningful in distilled model worlds, giving rise to synthetic agency.

Multilingualism, Technology, and Religious Moderation in Indonesian Islamic Boarding Schools

Communities that can speak more than one language as a daily habit are not only happening in the present era but have long existed in the pesantren environment. However, multilingualism in pesantren is often only seen as a capacity enhancement of the santri's knowledge. On the other hand, the multilingual ability of the Santri is an asset in forming an attitude of religious moderation. This article then presents the results of qualitative research using the concept of additive multilingualism which focuses on pesantren students in Indonesia. Through observations, interviews, and literature review, this research can describe the reality of multilingualism in pesantren and its impact on the religious attitudes of its santri. It turns out that the religious moderation attitude of the students is closely related to the multilingual learning process in pesantren that takes place with technological restrictions. The application of additive multilingualism is aligned with the pillars of religious moderation in four ways. First, respect for locality is reflected in the Santri's habit of using local languages. Second, multilingualism is an expression of anti-violence, this is reflected in Santri's acceptance of linguistic and ethnic diversity as a brotherhood base. This is what manifests in the Third pillar by making santri a tolerant community as a result of learning from the reality of ethnic and linguistic diversity in their environment. Fourth, the introduction of santri to foreign languages is directed to reaffirm their national commitment through the use of foreign languages in flag ceremonies. The limitation of technology in multilingual learning is also intended to keep students from the bad influence of technology and to maintain the discipline of students in maintaining a pesantren culture that is friendly to locality and sensitive to the times

Drowsy Driver Warn System Using OpenCV

The "Drowsy Driver Alarm" project leverages cutting-edge machine learning and face recognition technologies to address the critical issue of driver drowsiness on the road. Drowsy driving is a major cause of accidents and fatalities worldwide, highlighting the urgent need for effective countermeasures. Our research focuses on developing an intelligent system capable of detecting signs of drowsiness in real-time, providing timely warnings, and ultimately preventing accidents.

Meningkatkan Motivasi Belajar Siswa pada Materi Kesetaraan Laki-Laki dan Perempuan dengan Model Problem Based Learning di Kelas X Fase E SMA Negeri 2 Tarutung

Learning motivation is an important factor that influences student success in learning learning process. However, students often experience a decrease in motivation to learn which can hinder the achievement of learning objectives. Therefore, there is a need for learning methods that can increase students' learning motivation. One method Effective learning in increasing students' learning motivation is the use of interesting and interactive learning media. Attractive learning media can arouse students' interest and enthusiasm in the learning process. With the existence of learning technology, students can learn independently and interactively, so that their learning motivation can increase. Apart from learning media and learning technology, management of Catholic Religious Education and Character education is also has an important role in increasing student learning motivation. Good management of Catholic Religious education will create a learning environment that is conducive, disciplined and oriented towards achieving learning goals. With good management of Catholic Religious education, students will feel comfortable and motivated to learn. In this research, it is hoped that learning methods can be found that are effective in increasing student learning motivation. It is hoped that this effective learning method can be implemented in the context of Islamic education to improve student learning achievement. By increasing student learning motivation, it is hoped that learning objectives can be achieved better.

E-tutors’ experiences with integrating technologies in online course modules: A case of open distance e-learning

This study aims to explore how e-tutors' experiences influence their ability to integrate technologies, with a focus on the realities of technology knowledge during the facilitation of modules in an Open Distance e-Learning (ODeL) context. A survey was conducted to investigate the experiences of e-tutors in integrating technology across various modules within a department of an ODeL institution. Six e-tutors participated in the survey, and their involvement was voluntary, with no impact on their employment contracts, ensuring informed participation. The research utilized the constructivist learning theory to examine how e-tutors prioritize student engagement and promote active involvement in technology-enhanced constructivist learning environments. Data collected were organized and displayed in tables. The findings revealed that e-tutors had positive experiences integrating technologies into online module teaching. Additionally, they recognized the role of technology in supporting and enhancing the teaching of online student activities. These results provide further insight into the use of technology as a valuable educational tool in ODeL institutions, helping students deepen their understanding of subject matter. Moreover, students exposed to technology-integrated modules were more likely to adopt technology for independent learning.