Search

Although educators in the field of kinesiology implement online education in various forms, online education rarely receives attention in literature. The purpose of this study is to explore online courses in the subject matter of kinesiology through the community of inquiry framework. This mixed-method study adopted a multiple case study approach collecting data using survey, learning management system analysis, and semi-structured interviews. The analysis of the quantitative and qualitative data revealed that instructors shared high intentions toward the implementation of teaching and cognitive presences. The online instructors expressed much lower levels of intentions to support the social presence. The authors suggest online instructors be mindful of students' engagement that is bound to the interplay of different types of learning activities. This study is expected to serve as a meaningful contribution to the understanding of the status of online teaching and learning in the field of kinesiology. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students' academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP). The 22 courses still resulted in a small sample size of 1,007. Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature. [ABSTRACT FROM AUTHOR]

We investigated the utility of ChatGPT 3.5 in the creation of a fully online asynchronous higher education course. Our collaborative effort with ChatGPT resulted in developing a Master's level course on Trends and Issues in Instructional Design using the Backward Design Model. Throughout this process, we recognized the critical role of precise prompts and the need for continuous refinement to achieve the best possible outcomes. We delineated a workflow for effective prompts, underscoring the importance of setting clear objectives, employing a systematic sequence of prompts, and integrating feedback loops. Our research also emphasizes best practices and highlights the value of human expertise in collaboration with AI and the significance of validating AI-generated output. Through this process, we were able to develop a 14-week, three-credit course in a significantly reduced amount of time including generating HTML and CSS code for the Learning Management System. Although ChatGPT proves to be a powerful tool with the potential to expedite online course design and development significantly, it is crucial to acknowledge that human expertise remains indispensable for achieving optimal results. [ABSTRACT FROM AUTHOR]

In the last decade, learning analytics (LA) has evolved in a positive way, considering that the term emerged in 2011 through the society for learning analytics research (SoLAR). This area of data analytics can be identified as a specialization of educational data mining (EDM). LA emphasizes student learning outcomes. In addition to, a better understanding of student learning behavior and processes. While EDM focuses on helping teachers and students with the analysis of the learning process using popular data mining methods. The purpose of this research is to explore the first decade of work with the application of learning analytics in higher education institutions (HEI) in the context of tutoring information systems (TIS), with the intention of supporting institutions, teachers and students to decrease dropout rates. This article presents a systematic literature review (SLR) with 17 primary studies, comprised between 2014 and 2024. The findings reflect the use of LA in improving or optimizing learning using student academic history obtained through learning management systems (LMS), noting the scarcity of works with a focus on tutoring or academic advising. Ultimately, a gap is opened to apply LA in HEI, with information from institutional tutoring program (PIT), integrated with information from an LMS, to contribute to student permanence. [ABSTRACT FROM AUTHOR]

Learning Management System (LMS) is a major tool used in most universities and institutions for online/distance education purposes. A variety of LM systems are being used in different universities and institutions, and these LM systems are also updating their versions and patches to stay in the competition. Hence, the selection of an appropriate LMS is an important task as it is going to influence the academic proceedings of an academic institution. There are a few online comparison sheets available for different LMS, which have been scored based on online feedback, but those online comparisons lack a comprehensive features list for the comparison, and are unable to provide a generalized scoring function to compute the suitability score for an LMS. This article proposes a framework to evaluate an LMS based on a detailed list of features and functions. This evaluation is scored through a generalized scoring function that computes the quantitative score of LM systems. Lastly, some widely used open access and proprietary LM systems have been evaluated using the proposed framework and scoring function. [ABSTRACT FROM AUTHOR]

In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergent strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 74 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study highlights a discrepancy: sophisticated AI techniques like intelligent tutoring systems, learnable robots, web-based educational systems, and advanced learning management systems are infrequently applied in Nigeria. The study suggests that Nigerian educational institutions should adopt AI practices from advanced nations to enhance student learning and bridge the gap in AI integration. [ABSTRACT FROM AUTHOR]

Purpose: Online learning has come to stay in a technologically advancing world with increasing populations. The search for ways to make online learning more efficient and effective in some developing countries continues as the accompanying issues in developing country contexts abound. This paper explores the issues that underlie online learning management in a developing country context, focusing on the Moodle, Sakai and Zoom platforms used in some selected public universities in Ghana. Design/methodology/approach: The study relied on the qualitative approach to data collection and a descriptive design for analysis. Using the social constructivism theory, the paper discussed the critical issues students, and to a lesser extent, instructors encounter in their engagement on these platforms for academic learning. Findings: The study found that the inefficient use of these platforms is due to several factors including the high cost of data, poor perception of the quality of online learning compared to traditional in-person contacts, poor attitude of students, low participation in online learning, lack of computers and poor internet connectivity among others. Research limitations/implications: It is important to mention that this study was limited to some selected public universities in Ghana. Data could have been collected from a wider sample including other Ghanaian tertiary institutions or some other developing countries. The similarity of study outcomes in other developing countries, however, suggests that similar results would have been obtained in an international sample. Practical implications: Despite the robustness of the Learning Management systems in place, the evidence suggests that their utilisation is far less than optimal. However, with relevant policies and the provision of needed technical support, training, provision of equipment like computers for use by both students and instructors and efficient internet connectivity, the LMS platforms could be more efficient for online learning. Originality/value: The authors conducted this research using original data from interviews in the selected public universities in Ghana. The data was meant to inform the discussion on some of the critical issues that underlie online learning in a developing country context. Although the study relied on data from selected public universities in a single country, its outcomes reflect fundamental issues of online learning in a developing country context which find relevance in available study outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT: E-learning has gradually become tolerated in the teaching and learning process in higher learning institutions as one of the main learning aids, which enable learners get access to so many resources with so much ease. As in the field of biochemistry, in which concepts are complex and procedures are integral to the information, e-learning has its advantages and drawbacks. This study was designed with the intention to examine the suitability of e-learning model in the context of biochemistry course, more specifically, it was to identify the extent to which digital learning environment supports students' understanding and real-life use of biochemical knowledge. Quantitative research comprises of the use of Survivors to obtain numerical data concerning the success of e-learning platforms concerning biochemistry. A survey is designed to have closed questions (such as the Likert scale) as well as demographic questions to capture. Closed questions measure participants' views on e-learning tools, its utility and instructional usefulness as well as the demographic questions, which give a précis of participant profile and their past experiences in e-learning. The learning objectives laid down for the course were specific, measurable, and generic and included topics, such as enzyme kinetics, metabolic pathways, and protein structures. These were grouped into topic-based weekly work schedule. These sessions revealed that students who were attending the biochemistry courses are different, in that 71% of them agreed that the learning objectives for each of the modules, which were at the start of the course, served as a useful guide for understanding biochemistry as the learning objectives were well outlined at the start of the course. In about and through all of these recommendations, educational institutions will be able to design a more responsive and fair-blended e-learning environment that fosters success among learners and advanced understanding of biochemistry. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has experienced exponential growth in recent years, profoundly transforming various aspects of life, particularly in education. As a team of educational technology innovators, we have developed a series of generative AI features within our social learning management system, CourseNetworking, to boost student engagement in online discussions and enhance instructor efficiency. This article introduces three key features: automated hashtagging, automated post reflection, and AI-driven quiz question generation. We examine the goals and technical implementation of each feature, present solutions to challenges encountered during development and deployment, and share the best practices we have accumulated. Additionally, we outline our plans for future advancements. This paper seeks to foster understanding and collaboration among developers, educators, institutional leaders, and researchers, with the aim of using AI technology to improve educational products and services, ultimately elevating learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

The article "Raising the Profile of CDEs Through Diabetes Research: Introducing the ADEA Research Advisory Group" discusses the importance of research in diabetes education and management. The ADEA Research Advisory Group was established to provide advice, support high-quality research, and connect CDEs to research projects. The group aims to support ADEA's strategic vision of influencing diabetes research and improving care delivery for people living with diabetes. Additionally, the group has initiated webinars, masterclasses, and community-building activities to empower CDEs interested in research. [Extracted from the article]

This research seeks to develop the UI/UX of the Mobile Learning Management System (LMS) application by utilizing the Design Thinking methodology to improve the effectiveness of student learning in higher education. Design Thinking is used because of its user-centric methodology, guaranteeing that LMS apps align with the preferences and behaviors of students who are increasingly relying on mobile devices in their daily routines. Application development goes through the stages of Empathize, Define, Ideate, Prototype, and Test, with validation carried out using the System Usability Scale (SUS) approach. The assessment involved five students from various academic fields who are proficient in desktop-based Learning Management Systems (LMS). The test results showed a SUS score of 78, categorized as "Good" and close to "Very Good". This score signifies the program's excellent usability, easy navigation, and functions that encourage learning flexibility, including content access, assignment submission, and discussion forums. This study shows that the mobile Learning Management System created using the Design Thinking methodology significantly increases student access to lecture materials and encourages the efficacy of learning in higher education. Suggestions for improvements include conducting assessments with a wider group of participants and implementing design iterations to refine features based on user feedback. [ABSTRACT FROM AUTHOR]

This review examined the impact of online learning environments (OLE) on acquiring transferable skills (TS) through an analysis of 31 papers from the Web of Science database. Significant attention is given to critical thinking and communication skills, with moderate consideration given to creativity, problem-solving, and cooperation skills. Lesser emphasis is placed on negotiation, decision-making, self-management, resilience, and participation, while respect for diversity and empathy skills remains unexplored. STEM dominated the research focus in many articles, primarily focusing on higher education institutions, with limited attention to primary and secondary schools. Learning management systems (LMS) and online platforms are widely used for teaching TS. Quantitative research methods predominated, emphasizing the cognitive dimension of TS, and limited exploration of the social dimension. Therefore, OLE significantly impacts TS development, contributing to a more knowledgeable society, improved education, lifelong learning, personal empowerment, enhanced employee skills, and entrepreneurial outcomes fostering economic development. OLE also plays a pivotal role in nurturing civic engagement, peace, and social harmony. However, some articles underscore negative consequences, including challenges in communication skill development, prolonged adjustment periods, and increased challenge for learners. According to the review's findings, the authors provide recommendations to teachers, educators, curriculum designers, and researcher. [ABSTRACT FROM AUTHOR]

Engineering education emphasizes experiential learning and laboratory experience, an approach which has faced significant challenges during the COVID-19 pandemic. The inability to conduct hands-on laboratory experiments in engineering courses can significantly impede the student's learning experience, as well as their acquisition and retention of knowledge. Hence, it is imperative that practical hands-on laboratory experiments be incorporated as a critical component of distance learning tools. While researchers have proposed multiple methods, the provision of practical hands-on laboratory experience without limitations of time or place remains a challenging issue. This study aimed to address this issue by developing an Intelligent Learning Management System (ILMS), the ILMS-d, to construct a hands-on distance laboratory for engineering courses. We investigated the effect of implementing the ILMS-d in IoT courses on students' learning achievement and engineering problem-solving skills, and we assessed their acceptance of the proposed system. We found a significant positive effect on the learning achievement and engineering problem-solving skills of high, medium, and low achievers. In addition, students at all achievement levels responded positively to the usefulness and ease of use of the ILMS-d. Future research should incorporate different AI functions into the ILMS-d to construct more adaptive hands-on distance laboratories for students. [ABSTRACT FROM AUTHOR]

Background: There has been a rapid development and adoption of online learning in medical education. However, it is difficult to adopt the currently available online learning management systems (LMS). This study aimed to examine learners' perspectives on the evaluation of online LMS. Methods: An online LMS was developed based on the evidence-based guidelines. Two cross-sectional studies were conducted. A short survey was conducted with 716 learners registered on the LMS to obtain their perspectives on the online participation. A satisfaction survey was conducted with 255 learners enrolled in the courses taught solely online. Data from the LMS monitoring system was used to report the uptake of online courses. Data were analyzed using descriptive statistics. Results: Participants reported that the major factor influencing LMS uptake was the ability to be accessed anytime and anywhere (n = 556, 77.7%). The participants had good experience in using the LMS and were satisfied with it (n = 255, mean = 4.53, SD = 0.62). For online degree courses, the course had a high completion rate of 90% provided that a mark was assigned for course attendance. For non-degree courses, irrespective of whether they were free, paid, exam-based, or participation only, the completion rate was considered low (range 4.3–36.7%). Conclusion: Under a limited budget, a medical school in a low- to middle-income country could develop an effective online LMS to meet learners' needs. Our newly developed online LMS is relevant, accepted and to the satisfaction of the learners. Medical schools in the same context are encouraged to develop their own online LMS that serve and support learning in both degree and non-degree courses. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Theory & Practice in Education (JTPE) / Eğitimde Kuram ve Uygulama is the property of Journal of Theory & Practice in Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today's technology reality is reminiscent of science fiction in many ways. In order to mitigate the spread of COVID-19, school buildings closed. When they reopened, many schools utilized videoconferencing, learning management systems, and other technology to provide virtual instruction; however, all students did not experience the same technology availability and accessibility. To investigate the extent to which students with disabilities were provided with instruction in technology or instruction integrated with technology during and postpandemic, 108 special education teachers in one northeastern state participated in a cross-sectional survey study. Teachers indicated that most students they worked with need assistance with accessing technology. Special educators collectively reported themselves as the professional responsible for teaching students with disabilities to use technology. The challenges teachers experienced in providing remote instruction and the most commonly used technology activities are summarized. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences Research / Sosyal Bilimler Arastirmalari Dergisi is the property of ODU Journal of Social Sciences Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The purpose of the research was to identify the effective factors in decreasing the rate of reading among the students of Farhangian University of Kurdistan province as well as expressing the consequences and providing solutions. Method: The paradigm of interpretivism with a qualitative approach and content analysis strategy of descriptive type of written text and descriptive-analytical text format were used in this study. The participants were student-teachers of Farhangian University of Kurdistan province in the academic year of 2021-2022 with using conventional sampling method. The data collection tool was a semi-structured interview made by the researchers that was sent to 60 people in a virtual environment and in the space of the learning management system (LMS). Data were analyzed by open and axial coding methods. Results: The findings were analyzed in three sections: causes, consequences and solutions. The causes in 9 categories and 111 concepts included cultural and social, economic, individual and personal, virtual space and media, books, librarians and libraries, government sector, friends and family, school and university, and authors, publishers and translators. In the consequences section, 3 categories including mental and cognitive poverty, moral consequence and cultural and social poverty were examined in 132 concepts, and in the solutions section, 7 categories including cultural and social solutions, economic solutions, individual perspective solutions, librarian solutions, libraries, book publishers and translators, solutions of the government sector, solutions of family and friends, and solutions of the university were identified in 127 concepts. Conclusions: The current research has been able to identify the causes and consequences of the decrease in reading rates among students and then identify solutions to reduce them. Public libraries can play an effective role in the application of these solutions while interacting with students on the one hand and the government on the other hand. [ABSTRACT FROM AUTHOR]

This study focuses on developing a web-based digital learning space (DLS) designed to support a STEM learning model that fosters students’ mathematical creativity. The DLS builds upon traditional learning management systems by introducing customizable elements tailored to individual teaching and learning environments (PTEs and PLEs). These features allow for the formation of diverse learning networks within the DLS. This enhanced version integrates frameworks for STEM learning, engaging activities, instructional videos, quizzes, evaluations, feedback tools, and a chat feature for direct communication with instructors. The research and development process employed a 4D model–define, design, develop, and disseminate–and involved validation from content and media experts, confirming the DLS’s reliability for broader testing. Results indicate that the STEM-based DLS is highly suitable for mathematics education, as shown by validation from experts, practicality tests from users, and effectiveness tests that measured students’ mathematical creativity. This study also found moderate improvement in students’ creative mathematical thinking (Ng = 0.554) within classrooms using the STEM-based DLS, highlighting its effectiveness for enhancing these skills. Thus, this STEM-focused DLS offers a mobile-friendly digital environment that effectively promotes students’ mathematical creativity. [ABSTRACT FROM AUTHOR]

This study presents a task-based needs analysis of female English major seniors at a Saudi university to identify the academic tasks needed for their academic progress. The study utilized triangulated sources and methods, including the department's written materials; semi-structured interviews with instructors and students; and questionnaires. Academic tasks were found to be essential for academic progress, providing insights that could be overlooked without such analysis. The interviews revealed that students' proficiency levels ranged from low to average and that course content was not adapted to support below-average students. According to the questionnaire, the most essential skills for students' academic progression were effectively using the university's Learning Management System, time management, and delivering presentations. Listening to teachers as well as writing coherent and cohesive paragraphs were also important, whereas critical thinking and writing research papers were the most difficult tasks. The findings of this study emphasize the importance of adapting course content and pedagogical strategies to accommodate diverse student proficiency levels, thereby enhancing academic success. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to identify the instructional challenges in the elearning management system during the COVID-19 pandemic from the perspective of professors in University of Tabriz. Methods: The method of this research is descriptive-survey. The statistical population of the research included of all professors of University of Tabriz in the second semester of the academic year 2020- 2021, of which 211 professors were selected as a sample size based on Krejcie and Morgan Table and using the cluster random sampling method from four groups of technical-engineering, humanities, basic sciences, and agriculture. Data collection tool is researcher-made questionnaire that assess the views of professors on the challenges of instruction in the e-learning management system. The face validity of questionnaire was confirmed by professors and experts in the field of educational sciences, and its construct validity was also determined through confirmatory factor analysis. Cronbach's alpha was used to determine the reliability of the research tool, and its coefficient was calculated as 0.907. The data were analyzed using one-sample T-test and Friedman's test. Results: The results showed that dimensions of infrastructure, support, attitude, and access are the most important instructional challenges in the e-learning management system of University of Tabriz from the perspective of professors. Conclusion: It is recommended that University of Tabriz maximize the professors’ desirable use of the electronic learning management system by providing the necessary infrastructure. Furthermore, the University of Tabriz to overcome barriers and challenges in learning management systems, with actions such as establishing technical, instructional, and human support groups to provide necessary assistance to professors in urgent situations, preparing programs to change professors’ attitudes towards electronic learning, and facilitating professors’ access to the electronic learning management system. [ABSTRACT FROM AUTHOR]

We consider the reference-based approach for Automatic Short Answer Grading (ASAG) that involves scoring a textual constructed student answer comparing to a teacher-provided reference answer. The reference answer does not cover the variety of student answers as it contains only specific examples of correct answers. Considering other language variants of the reference answer can handle variability in student responses and improve scoring accuracy. Alternative reference answers may be possible, but manually creating them is expensive and time-consuming. In this paper, we consider two issues: First, we need to automatically generate various reference answers that can handle the diversity of student answers. Second, we should provide an accurate grading model that improves sentence similarity computation using multiple reference answers. Therefore, our proposed approach to solve both problems highlights two components. First, we provide a sequence-to-sequence deep learning model that targets generating plausible paraphrased reference answers conditioned on the provided reference answer. Secondly, we propose a supervised grading model based on sentence embedding features. The grading model enriches features to improve accuracy considering multiple reference answers. Experiments are conducted both in Arabic and English. They show that the paraphrase generator produces accurate paraphrases. Using multiple reference answers, the proposed grading model achieves a Root Mean Square Error of 0,6955, a Pearson correlation of 88,92% for the Arabic dataset, an RMSE of 0,7790, and a Pearson correlation of 73,50% for the English dataset. While fine-tuning pre-trained transformers on the English dataset provided state-of-the-art performance (RMSE: 0.7620), our approach yields comparable results. Simple to construct, load, and embed into the Learning Management System question engine with low computational complexity, the proposed approach can be easily integrated into the Learning Management System to support the assessment of short answers. [ABSTRACT FROM AUTHOR]

This study aims to reveal the main trends and findings of the studies examining the use of learning analytics in distance education. For this purpose, journal articles indexed in the SSCI index in the Web of Science database were reviewed, and a total of 400 journal articles were analysed within the scope of this study. The systematic review method was used as the research methodology. The first study on the subject was published in 2011, and the publications continued to increase over the years. It was found that the publications on the subject were primarily found in "Computers and Education" and "Education and Information Technologies" journals. It was observed that China, USA, and Spain were the leading countries where the related studies were conducted. The studies primarily used the quantitative method, and university students were included as the sample. In addition, within the scope of learning analytics, it was observed that the data were mainly analysed with "regression analysis", "correlation analysis", "special algorithms-models", "ANOVA", and "cluster analysis" methods. It was determined that the most preferred platforms in the studies were learning management systems and MOOCs, learning behaviours were mostly examined, and log data were mainly used in this process. It was observed that the variables tested in the studies mainly consisted of students' behaviours on the platform, learning performances, communication processes, dropout behaviours and course designs. Furthermore, in the studies examined, the advantages of learning analytics in the context of distance education are mostly related to the possibilities of improving the teaching process, and as disadvantages, it is stated that learning analytics is not suitable for use in some situations, negatively affect students' performances, have limited interaction with students and are an expensive investment. [ABSTRACT FROM AUTHOR]

Information and communication technology considerably impacts students' engagement with self-regulated learning (SRL) methodologies. However, there has been a lack of any comprehensive visualization of the SRL process, making it difficult to interpret student behaviors. To address this issue, the REXX platform is used in this study to visualize SRL outputs. While REXX has previously been used to present educational metrics more comprehensively and personally in the learning management system (LMS) framework, research on understanding student behavior through the learning analytics platform (LAP) remains unexplored. This study focused on transforming REXX from an LMS to an LAP to capture detailed features of individual student profiles, thereby reflecting specific SRL characteristics. We collected profile data from 215 high school students via an e-learning web application and used K-means clustering to categorize their behaviors. The method yielded a Davies–-Bouldin score of 0.9718, a silhouette score of 0.54, and a Calinski–Harabasz score of 124.1805. This study addresses both teaching and learning strategies for educators and students. It represents a considerable step toward understanding student behavior in the e-learning environment. However, we recommend integrating machine learning models to enhance automated learning strategies alongside this baseline framework. [ABSTRACT FROM AUTHOR]

This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system's effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics. [ABSTRACT FROM AUTHOR]

Business intelligence (BI) and analytic solutions originated from learning analytics (LA), which has emerged in the education sector due to the success of data mining models in businesses. However, the application of BI models in analytics for at-risk students (those failing academically or facing challenges that may hinder their completion of studies) is currently unclear in the global higher education context. LA is being tested and implemented in some higher education institutions (HEI) worldwide to enhance learning and teaching through monitoring students’ interactions and success in fully online and blended courses. Furthermore, most studies on LA are data-driven and lack theoretical foundations. This study, from which the findings in this article are derived, is based on Tinto’s longitudinal model of dropout. This model is used to select dropout conditions and extract data from institutional information systems and student learning data, with the goal of improving the identification of at-risk students and providing real-time interventions. Through an inductive analysis of literature, this article explores how theoretical frameworks can be applied in analytics for at-risk students, with a focus on predictive modeling. As a result, a modified theoretical model based on Tinto’s longitudinal model of dropout is presented. This modified model aims to demonstrate the potential of information systems and student learning data in indigenous HEI, providing a learning analytic approach that universities can use to identify students at risk of dropping out. [ABSTRACT FROM AUTHOR]

Digital transformation has become a critical aspect of modern education, necessitating the development of digital skills among all students, including those with disabilities. Among these, blind students face unique challenges in acquiring the digital competencies needed for academic success and professional integration. This study aimed to enhance the digital transformation skills of blind postgraduate students by evaluating the effectiveness of a cloud-based learning management system, Moodle Cloud. Using a mixed methods approach, we combined descriptive and quasi-experimental designs to assess the impact of the intervention. The sample included 20 blind graduate students from Beni Suef University, equally divided into experimental and control groups. Pre- and post-assessments measured participants' digital transformation skills through achievement tests and performance evaluations. The findings indicated significant improvements in the experimental group, with higher scores in both the achievement tests and performance assessments compared to the control group. The results suggest that the cloud-based learning management system played a vital role in enhancing digital skills, and no significant differences were found between remote and in-person applications of the intervention. The study emphasizes the importance of incorporating modern digital technologies into the education of blind students, aligning with Egypt's Vision 2030 plan and ongoing educational reforms. [ABSTRACT FROM AUTHOR]

This study examined students' experiences of assessment and feedback engagement in digital contexts in upper secondary school through an explanatory sequential mixed-methods case study. The data material consisted of 435 survey responses and 16 individual interviews. The results indicated that the use of digital feedback was crucial for students' experiences of digital feedback engagement. The final model of the path analyses suggested that students' experiences with digital feedback engagement were dependent on several predictive and mediating variables. Multiple regression analyses suggested gender differences regarding variables predicting digital feedback engagement. Whereas a deep approach and learning from examinations were important for male students, clear goals and standards were more important for female students' digital feedback engagement. Thematic analyses of the interview data identified three themes: Grades and digital feedback; dialogic feedback interactions; and a performance-oriented assessment culture. Grades tended to reduce the relevance of digital feedback when provided at the same time and in separate learning management systems (LMSs). Opportunity for dialogic feedback interactions was considered essential to students' feedback engagement and criteria orientation, but rarely offered in digital contexts. A performance-oriented assessment culture risked outweighing focus on learning in digital contexts for some students. [ABSTRACT FROM AUTHOR]

This study investigates the effect of studying with topic maps provided by a self-developed language learning support system on (a) learning perception, (b) learning achievement and (c) variation in learning attitude and motivation, from the perspective of prior learning attitude, motivation and learning style. An analysis was conducted on the learning data of 90 participants for examining pre-test and post-test scores, learning perception, learning style indicators, attitude and motivation rankings before and after the study of target Japanese grammar content with the support of the system. The findings suggest that learners with high attitude/motivation perceived greater development of the habit of "learning from the comparison of related knowledge" and felt more satisfied with the learning situation in the system environment. Furthermore, the learners' attitude towards Japanese grammar learning and their motivation toward Japanese language learning were significantly stronger after the learning activity. It is also observed that (a) learner attitude towards Japanese grammar after studying with the system only had a positive correlation to prior attitudes; and (b) learner motivation towards Japanese language after studying with the system correlated highly with prior motivation and perception of development of the habit of "learning from the comparison of related knowledge." Finally, from a learning style perspective, the analysis result suggests that "Global learners" expressed a stronger feeling about the usefulness of the comparison function of the system and their increment in motivation after using the system was higher than for "Sequential learners." Moreover, between learning style indicators in the Sequential/Global dimension and level of learner expertise in the reported mental load scale, a significant interaction effect was found. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress. Optional assessments may encourage voluntary engagement, potentially leading to a more genuine reflection of student understanding. Also, frequent assessments provide continuous opportunities for feedback and adjustment, which can keep students actively engaged in the learning process.This study aims to investigate two crucial facets of formative assessments: frequency and the level of stakes involved (mandatory vs. optional). We examine how modifying the frequency of formative assessments affects students' course performance. Additionally, we evaluate the impact of mandatory versus optional formative assessments on students' course performance in higher education.The sample of this study consisted of undergraduate students (n = 336) enrolled in three sections of a large asynchronous course at a Canadian university. We extracted features associated with online formative assessments (e.g., the number of attempts and average scores) from the learning management system. Next, we used these features to predict students' performance in summative assessments (two midterms and a final exam).Our findings indicated that increasing the frequency of online formative assessments did not consistently improve student performance. Also, participation frequency in online formative assessments seemed to vary depending on assessment stakes (i.e., optional vs. mandatory). We recommend that instructors examine what conditions can maximize the contribution of formative assessments to students' academic achievement before building predictive models. [ABSTRACT FROM AUTHOR]

Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching. ICT-based learning generates a huge amount of data that can be utilized to better understand student behavior and improve students learning. Predicting students' academic performance is essential to take proactive measures to improve student learning and reduce the risk of student dropout and failure. This study aims to use video learning analysis and data mining approaches to predict student academic achievement and identify the factors affecting their performance. For this purpose, the dataset containing records of 326 students from a higher education institution (HEI) is used which contains records from SIS, Moodle, and eDify. This study advocates the use of a balanced dataset and optimized feature set to obtain better performance for students' academic performance prediction. Several machine learning and deep learning models are applied to analyze their performance against the original dataset, balanced dataset, and balanced dataset with the optimized feature set. Experimental results demonstrate decision tree classifier outperforms with an accuracy of 99.06% with a balanced dataset and optimized feature set. Further analysis indicates that the video interaction feature has a strong impact on the performance of students. [ABSTRACT FROM AUTHOR]

This study explores techniques for managing class imbalance in predictive modeling to forecast water pipe failures using XGBoost and logistic regression. Given the significant challenges posed by water pipeline failures—such as service disruptions, costly repairs, and environmental hazards—there is a pressing need for effective predictive models. Using a dataset from 2015 to 2022 that includes features like pipe age, material, diameter, and maintenance history, the study applies methods such as random oversampling and undersampling to improve model performance. Results show that XGBoost outperforms logistic regression in recall (0.795 vs. 0.683), a critical metric for managing water infrastructure. Although logistic regression has slightly better precision (0.695), XGBoost demonstrates superior overall performance with higher Matthews correlation coefficient (MCC) and F1 score, effectively balancing precision and recall. This research is essential as it addresses the need for robust predictive models to anticipate and mitigate water pipeline failures. By offering a comprehensive framework for managing large-scale datasets and showcasing how accurate predictions can reduce maintenance costs and water wastage, this study contributes to more efficient and sustainable water infrastructure management. [ABSTRACT FROM AUTHOR]

Learning management systems (LMS) have become central to modern education, enabling accessible, personalized, and engaging learning experiences. This study aims to investigate Saudi university students' perception of LMS in order to explore the critical factors that shape their engagement, satisfaction, and acceptance of these platforms. Drawing from the existing literature that points out the usability challenges of LMS, this study hopes to derive actionable insights to optimize e-learning outcomes. Using Kelly's repertory grid analysis technique, this study systematically captured and analyzed the personal constructs students associate with LMS, focusing on ease of use, interactivity, and content alignment with educational needs. A sample of 20 university students provided insights on their experiences with LMS features related to usability, functionality, and interactivity, which are critical to engagement. Findings indicate that ease of use is a major determinant of acceptance, along with interactivity and relevant content delivery that supports diverse learning preferences. The study identifies key elements to improve LMS platforms, fostering a more engaging digital learning environment and supporting students' learning needs. The findings highlight the key aspects: usability of LMS and students' satisfaction through user-friendly interfaces and interactive features. Institutions that incorporate student feedback into LMS development will likely see improved e-learning outcomes. This research contributes to a deeper understanding of LMS user perceptions and implies refinements that can align platforms with pedagogical demands in higher education. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused constraints on education that takes place face-to-face; thus, the learning process has been conducted online. With this emergence, an application-based learning tool called a Learning Management System (LMS) was created to cater to the requirements of distant students. Therefore, this study explores how SPACE-LMS is used in the Teacher Professional Education Program (TPEP) and how it interacts with the Technology Acceptance Model (TAM) and self-efficacy. This study collected data from TPEP participants in the province of West Sumatra-Indonesia from the 2022 cohort, and 245 individuals actively participated. As the quantitative method, Partial Least Square-Structural Equation Modeling (PLS-SEM) analysis with SmartPLS software was used to test and predict the conceptual model. The empirical findings demonstrate that the readiness of adopting SPACE influences perceived ease of use (PEU) and perceived usefulness (PU). Self-efficacy (SE) influences PEU and intention to use (ITU); PEU influences PU; PEU influences attitudes toward users (ATT); and PU influences ATT. Interestingly, PU had no impact on the ITU. The desire of TPEP students to use the SPACE-LMS is also influenced by their sense of self-efficacy. Thus, this study has both theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Copyright of Elektronik Industrie is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Sustainable Development Goals (SDGs) are universally recognised targets designed to combat poverty, inequality, and climate change. However, there exists limited awareness and understanding of these goals among nursing and midwifery students. To address this knowledge gap, a co-designed audio podcast was introduced as an educational tool to enhance students' comprehension of SDGs and their relevance to the healthcare profession. Methods: A prospective study was conducted at Queen's University Belfast, involving 566 first-year nursing and midwifery students. A 60-minute SDG podcast, co-designed with students and stakeholders, was made accessible within the university's learning management system. Pre- and post-test questionnaires were administered to assess changes in students' knowledge levels and attitudes toward SDGs. Additionally, 37 participants engaged in focus group interviews six months after listening to the podcast to explore their experiences and reflections. Quantitative data was analysed using paired t-tests and descriptive statistics, while qualitative data was analysed thematically. Results: The podcast significantly increased students' awareness of SDGs and their understanding of the goals' relevance to their profession and personal lives. Post-test scores showed substantial improvements across all three sub-scales: knowledge, professional relevance, and personal relevance. Moreover, participants rated the podcast as a valuable learning resource with high acceptability, although some expressed uncertainty about replay intentions. Focus group interviews revealed three themes, including 1) More than you know', which described how participants developed new knowledge and understanding about SDGs, 2) 'Nurse-Midwife Nudges', which illuminated how participants made minor changes to their behaviour and 3) 'Fitting Format', which highlighted how participants favoured the use of an audio podcast to learn about SDGs. Discussion: This study demonstrates the potential of audio podcasts as an effective and engaging tool for increasing awareness and understanding of SDGs among nursing and midwifery students. The results suggest that such interventions can positively impact students' knowledge, attitudes, and behavioural intentions regarding the SDGs. The findings also emphasise the importance of co-design in developing educational resources tailored to the specific needs and preferences of students. [ABSTRACT FROM AUTHOR]

Prolonged stress and the need for rapid uptake of information can have detrimental effects on memory and cognition, whereas meaningfulness of study material and motivation to learn can have positive effects. How do these opposing conditions impact workplace learning in essential frontline workers during a global pandemic? We analyzed learning data collected longitudinally since before the pandemic in over 85,000 essential frontline grocery workers and nonessential telecommunications workers via a learning management system that incorporates a spaced retrieval schedule, where items are retrieved following retention intervals of varying length. Findings indicate more rapid knowledge uptake in grocery workers (a) during than before the pandemic, (b) for COVID‐19‐related content than non‐COVID content, and (c) in the United States than in Canada. Longer‐term maintenance of training material was similar across groups. Evidence of enhanced workplace learning and retention supports efforts to integrate empirically based strategies from the behavioral sciences into learning‐based technologies. [ABSTRACT FROM AUTHOR]

The emergence of the COVID-19 pandemic brought unparalleled disruptions in people's way of living. Most transactions are done online to practice social distancing. Face-to-face classes were suspended, forcing educational institutions to migrate into flexible learning modalities. Hence, higher education institutions universally utilize learning management systems (LMS) to manage online classes. During this period, outcomes based education's (OBE) performance was challenging to analyze. This prompted the researchers to explore the integration of OBE learning analytics and RIASEC model into existing LMS to enhance educational outcomes. OBE focuses on achieving student learning outcomes, while the RIASEC model categorizes career interests into six personality types: realistic, investigative, artistic, social, enterprising, and conventional. By combining these frameworks, educators can provide a more personalized learning experience. The integration allows for the continuous monitoring of learners' performance, improves instructional strategies to meet individual needs, and career aspirations. This paper reviews current literature on the benefits and challenges of implementing OBE-based learning analytics and RIASEC in LMS, demonstrating how this approach can enhance student engagement, improve learning outcomes, and align educational activities with career goals. The findings suggest that this integrated approach holds significant promise for advancing personalized education and preparing learners for successful careers. [ABSTRACT FROM AUTHOR]

This study focuses on developing specific vocabulary learning strategies to assist the receptive knowledge of English for Specific Purposes (ESP). It aims to provide a practical framework for teaching technical vocabulary. The study introduced the Multimedia Strategic Vocabulary Learning (MSVL) model and investigated its effects on teaching technical vocabulary to pre-engineering students in online courses during the early wave of COVID-19. A total of 88 Saudi undergraduate students randomly assigned to control and experimental groups were treated with an intact pre-engineering ESP course for ten weeks. Vocabulary learning strategies from Schmitt's (1997) vocabulary taxonomy were adopted and incorporated into two stages of the vocabulary acquisition process. The experimental group was treated with the MSVL model through multimedia and explicit vocabulary learning instructions in online classes using Blackboard Learning Management System (LMS) to assist them with specialized vocabulary acquisition. The dependent measure in this study was the participants' receptive knowledge of technical vocabulary, which was assessed using the pre-test, post-test, and delayed post-test. The findings indicated that using the MSVL model significantly improved the experimental group's receptive knowledge of technical vocabulary. This study's guidelines help educators create multimedia resources for ESP learners, preparing them for their professions. [ABSTRACT FROM AUTHOR]

The cognitive aspect is crucial in developing interactive and adaptive systems, including learning management systems (LMS). By understanding human cognitive processes, developers can create adaptive systems that are sustainably used by targeted users. Cognitive traits need to be explored to become part of the user model, especially especially to develop an LMS with an adaptive interface based on cognitive load. However, there is limited research exploring the correlation between cognitive load and adaptive interfaces, as well as the visualization of information, within online learning settings, including those specifically within LMS platforms. Additionally, a lack of understanding of cognitive processes renders the use of LMS static and unable to adapt to learners' abilities. Therefore, information regarding the specific characteristics or model of cognitive load that accurately represent LMS users as triggers for adaptive interfaces needs to be further examined. A total of four experts in the cognitive field were interviewed. The interview process was conducted in a semi-structured manner to obtain information about problems in the use of e-learning, the nature of cognitive, and the process of capturing cognitive load during learning using LMS. By using a soft system methodology, the results from the interviews are mapped into a cognitive load-based LMS user model design. The initial model design includes information on working memory capacity, task performance (time, true/false rate), learning behavior (learner log), physiological (in a lab scale), and self-reporting (Likert scale). This model design is a promising first step towards a more technical process in developing adaptive and interactive systems. [ABSTRACT FROM AUTHOR]

The significance of accident prevention in the construction industry has been consistently emphasized. Despite the research into various preventive methodologies, their application in actual construction sites still needs to be improved. Therefore, it is necessary to develop accident prevention methodologies and make improvements so that practitioners can utilize them in actual construction sites. This study sets out to propose methods for accident prevention using construction accident data and to develop a system that construction practitioners can easily utilize. Firstly, a methodology was developed to assess the risk of construction sites by considering site-specific characteristics through actual construction accident data. This led to the identification of five principal risk factors (i.e., construction type, facility type, ordering organization, construction cost, and safety management plan), and the results of risk assessment for different site classifications were derived by combining these factors. Then, machine-learning models were developed to predict accident-causing objects, accident types, and injury-death using four algorithms: random forest, light gradient boosting model, eXtreme gradient boosting (XGBoost), and categorical boosting. As a result, XGBoost demonstrated the most outstanding predictive performance, with averaged F1 scores of 0.839, 0.749, and 0.977, respectively. A web-based prototype was also developed to deploy the proposed methods, confirming the practical utility of the accident prevention strategies outlined in this research. These findings have the potential to improve the efficacy of site management by enabling proactive identification of high-risk areas, thereby promoting more effective accident prevention initiatives. [ABSTRACT FROM AUTHOR]

This study investigates the impediments and facilitators of artificial intelligence (AI) use in the Blackboard Learning Management System. A survey of 286 faculty members at Northern Border University examined demographics, AI tool usage frequency, adoption barriers, and factors influencing AI integration in teaching. Results revealed significant perceived barriers to AI tool use in Blackboard, with insufficient support and training identified as the primary obstacle. The survey found that gender and educational level influenced the adoption of the AI tool. The study suggests that addressing faculty training needs, ensuring privacy and security, and improving AI tool reliability could help overcome perceived impediments to AI use in Blackboard. These findings contribute to understanding the challenges and potential solutions for integrating AI in higher education learning management systems. [ABSTRACT FROM AUTHOR]