The widespread adoption of Learning Management Systems (LMSs) in educational contexts, underscored by their critical role in facilitating cloud-based training across diverse settings, serves as the foundation of this investigation. In the era of increasing integration of technology within higher education, a notable reduction in the costs associated with the creation of online content has been observed. The shift towards remote learning, precipitated by the COVID-19 pandemic, has highlighted the indispensable nature of LMSs in the delivery of specialized content, the application of varied pedagogical strategies, and the promotion of student engagement. Adaptability, defined as the ability to adjust behavior, cognition, and emotional responses in the face of new circumstances, has been recognized as a key factor in the success of online learning. This study employs sophisticated Machine Learning Techniques (MLTs) to explore the determinants of student adaptability, introducing the novel framework of Online Learner Adaptability Assessment using MLTs (OLAMLTs). Through the analysis of comprehensive datasets, which include indicators of student behavior, performance, and engagement within online platforms, MLTs facilitate the identification of patterns and correlations pertinent to adaptability. The OLAMLTs framework applies a retrospective analysis to variables such as technological proficiency, motivation, and self-regulatory capabilities, enabling the provision of customized recommendations for educators. By facilitating targeted educational interventions, the study seeks to address the disparity between the need for adaptable learners and the availability of tools designed to foster this critical attribute. The ultimate aim is to augment the resilience and efficacy of online learning platforms in anticipation of future disruptions, including pandemics or other unforeseen challenges. This research contributes to the ongoing efforts to develop a more adaptive and resilient online learning landscape, marking a significant advancement in the fields of educational technology and pedagogy.
Course evaluation, a critical component for the implementation of outcome-based education (OBE), provides substantial data support. The reliability, validity, and discriminative power of evaluation results are significantly influenced by the choice of course evaluation methods. An effective course evaluation method identifies weak links in the teaching process, offering a foundation and reference for continuous course improvement. This study introduces a course evaluation method based on achievement pathways, establishing the supportive relationship among course-related graduation requirement indicators, course objectives, and achievement pathways. Grounded on formative assessment, a system to quantify the achievement of teaching objectives in courses is constructed. The method has been applied to courses, such as Data Visualization and Software Engineering, at the Beijing Institute of Petrochemical Technology. Practice demonstrates that this method is capable of identifying weaknesses in the course implementation process, providing theoretical foundation and reliable assurance for ongoing course improvement.
In today’s digital age, new technological tools are increasingly becoming an indispensable part of the educational process, particularly in educating children. The development of technology, including tablet computers, and mobile phones, offers numerous opportunities for enhancing teaching methods and fostering children’s development. These technologies offer access to a wide array of digital resources, interactive content, and personalized educational experiences, thereby opening up new methods in education. However, despite the benefits, many studies underscore the potential negative impacts of utilizing new technologies in children’s education. For the above reasons, a study was conducted to examine how new technologies influence children’s learning of geometric shapes. Forty children, evenly distributed by gender and place of residence, participated in the study, undergoing testing using both mobile phones and Tablet PCs. The results revealed statistically significant differences based on the respondents’ gender, place of residence, and the devices through which the tasks were presented, particularly for certain geometric shapes.
Purpose: To provide reference promoting the construction of nursing courses through the analysis of research hot spots on ideological and political education in nursing courses in China; Methodology: CiteSpace and VOSviewer software were used to visualize the pertinent literature that was downloaded from CNKI, Wanfang, VIP database before December 31, 2023; Results: A total of 918 literatures were included, and the publications, authors, institutions, journals, course type, keywords of the literature were analyzed. The number of published papers had increased year by year. Publishing institutions were primarily schools, authors were mostly independent researchers, published journals were relatively concentrated, with most of them being general or provincial journals, and courses are mostly theoretical. Hotspots for current research include the integration of nursing courses in higher vocational colleges and the mining of Ideological and political elements; Conclusions: Curriculum ideology and politics have received extensive attention from nursing educators. In the future, it is necessary to strengthen the exchanges between different research institutions such as schools and hospitals, pay attention to the depth of research, develop educators' political and ideological ability, actively use a variety of teaching methods, and integrate political and ideological elements into the teaching of a diversified curriculum, so as to provide talent guarantee for the realization of "Healthy China".
This investigation examines the influence of ChatGPT on mathematics achievement, with a specific focus on the moderating role of students’ interest in mathematics. A sample of 250 students, encompassing undergraduates pursuing a Bachelor of Science and postgraduates engaged in Masters of Philosophy and Doctor of Philosophy programs in Mathematics Education at Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development (AAMUSTED), Kumasi-Ghana, was selected through random sampling. Employing a quantitative methodology, data were collected via structured questionnaires and analyzed using Amos software, version 23, to test the hypothesized relationships. The findings revealed that student interest in mathematics significantly and positively correlates with the use of ChatGPT, as evidenced by a p-value of less than 1%. Conversely, ChatGPT’s direct influence on mathematics achievement was found to be negative, though not statistically significant, with a p-value of less than 1%. Furthermore, a direct, positive, and statistically significant relationship between students’ interest in mathematics and their achievement in the subject was observed, with a p-value of less than 1%. Notably, the study identified a statistically significant positive moderation effect of students’ interest on the association between ChatGPT usage and mathematics achievement, underlined by a p-value of less than 1%. The findings advocate for a cautious integration of ChatGPT in mathematics education, emphasizing that reliance on artificial intelligence should complement, not replace, traditional learning modalities. Additionally, it is suggested that future research might benefit from employing surveys or self-evaluation tools beyond questionnaires to gather data. This study contributes to the existing body of knowledge by highlighting the nuanced role of student interest in leveraging technology-enhanced learning tools for academic success in mathematics.
Teachers face challenges when conducting their jobs (stress, work overload, lack of resources, lack of time, etc.), which may lead them to psychological problems. With the current information overload and sparsity of resources, it can be challenging to find relevant educational resources promptly. Recommender systems are designed to address the issue of information overload by filtering relevant information from a large volume of data based on user preferences, interests, or observed behavior. A recommender system can help mitigate teachers’ struggles by recommending personalized resources based on teachers’ needs. This paper presents previous works related to recommender systems in education. It highlights their techniques and limitations. Some papers relied on machine learning and/or ontology for building recommender systems, while others relied on a hybrid system comprising several techniques. The most employed recommendation techniques include collaborative filtering (CF), content-based (CB), and knowledge-based (KB) approaches. Each approach has its advantages and limitations. To overcome these limitations, several advanced recommendation methods have been proposed, such as social network-based recommender systems, fuzzy recommender systems, context awareness-based recommender systems, and group recommender systems. Our analysis reveals that existing recommender systems are learner-centered, often lacking an understanding of the teacher’s context. The continuous advancement of recommendation approaches and techniques has led to the implementation of numerous recommender systems and the development of numerous real-world applications. A context-aware personalized recommender system for teachers should consider personal and professional development goals and psychosocial state when presenting a recommendation. Years of experience, access to equipment, and commute time are some of the aspects that should be considered when designing such a system. Moreover, the studies surveyed provided detailed information about their evaluation methodologies. However, the evaluation of these systems is typically conducted using simulated or nonreal students, along with various assessment approaches such as algorithmic performance tests, statistical analysis, questionnaires, and qualitative observations.
Starting from social identity theory, this article explores the importance of interpersonal relationships to the work performance of teachers in private colleges and universities born in the 1990s, and examines the relationship among interpersonal relationship, organizational commitment and work performance. After conducting a questionnaire survey and analysis of 951 teachers from 19 private Colleges and Universities, the results show that aspects of interpersonal relationships, specifically caring for others and self-image, exert a significant positive impact on organizational commitment and work performance; and these factors can enhance the work performance of post-1990s teachers in these institutions through the partial mediating role of organizational commitment. Research shows that the interpersonal relationships established and maintained from public goals or private goals can promote the organizational commitment and work performance of post-1990s teachers in private colleges and universities. Higher level of interpersonal relationship can improve teachers’ identification and sense of belonging to the organization, and then improve their work performance.
The efficacy of political education is pivotal in developing critical thinkers and informed citizens. Traditional methods, however, face challenges such as low engagement, accessibility issues, slow adaptation to changes, and underutilization of technological advancements. This research investigates the transformative impact of integrating Artificial Intelligence (AI) and cutting-edge design strategies into political education courses at Pakistani universities. The study adopts a methodological approach that synergizes AI-based network media with traditional educational practices, subsequently evaluating the implementation’s outcomes through empirical data. The integration of AI into the educational framework has shown remarkable results: a 57% increase in the rate of education post-implementation, a 71% satisfaction rate among students regarding their learning experience, and a political accomplishment (PA) score of 81±4. These metrics indicate a substantial enhancement in the quality of political education. The research underscores the potency of AI-supported communication coaching in elevating political education standards, thereby nurturing political and ideological competencies among students. This modernization, characterized by dynamic, interactive, and globally accessible learning experiences, promises to redefine political education. It effectively dismantles historical barriers, equipping individuals to navigate the complexities of the contemporary geopolitical landscape.
The field of educational research increasingly emphasizes predictive modeling of academic performance, focusing on identifying determinants of student success and crafting models to forecast future achievements. This investigation evaluates the efficacy of different feature selection techniques in predicting mathematics performance among Ecuadorian students, based on data from the 2021-2022 cycle of the Ser Estudiante test. Employing supervised logistic regression for classification, the study compares three feature selection methods: selection based on the highest k-scores, recursive feature elimination with cross-validation (RFECV), and recursive feature elimination (RFE). The assessment reveals that both the highest k-scores and RFECV methods are highly effective in isolating the most pertinent features for predicting mathematical prowess. These methods achieved prediction accuracies exceeding 90%, with k-scores attaining 96% and RFECV 92%. Furthermore, they demonstrated remarkable recall (94% and 97%, respectively) and F1-Score (96% and 91%, respectively). Additionally, both methods presented Receiver Operating Characteristic (ROC) curves with an area under the curve (AUC) of 99%, signifying superior discriminatory capability. The findings illuminate the critical role of judicious feature selection in enhancing the precision of predictive models for academic performance, particularly in mathematics. The results advocate for the application of these techniques in pinpointing key factors contributing to student success. This study not only contributes to the methodological discourse in educational data analysis but also provides practical insights for the Ecuadorian education system in leveraging data-driven approaches to enhance student outcomes.
In educational settings of Pakistan, where English is utilized as the primary medium of instruction but not as an official language, the assessment of instructional text readability is crucial. This research investigates the impact of text readability on student comprehension and achievement by integrating deep learning methods with mathematical and statistical approaches. It has been observed that when suitably trained, deep learning models exhibit a significant correlation with human assessments of text readability. The investigation further illuminates the linguistic and structural elements influencing readability. Such insights are instrumental for educators and content developers in establishing standards to craft more accessible educational materials. Emphasis is placed on the exploration of Advanced Natural Language Processing (NLP) techniques, the incorporation of multilingual models, and the refinement of curricular structures to enhance readability assessments. Additionally, the study underscores the necessity of engaging with educational policymakers in Pakistan to implement accessibility guidelines. These efforts aim to reduce linguistic barriers, amplify student potential, and foster an inclusive educational ecosystem. The findings and methodologies presented in this study offer a comprehensive understanding of the challenges and solutions in optimizing English language instructional materials for non-native speakers, with potential applications in diverse multilingual educational contexts.
In the epoch where globalization and knowledge economy predominate, mastery of English, fortified by its potent global stance, emerges as pivotal for multinational communication. Pursuant to this paradigm, English educators are impelled to refine teaching methodologies and accentuate perpetual learning. A comprehensive investigation into bilingual learning outcomes and efficacy employing Grammar Translation Method (GTM), Cognitive Direct Method (CDM), and Eclectic Bilingual Approaches (EBA) is herein presented. Methodologically, a quantitative experimental design complemented by qualitative interviewing was employed over a six-month experimental project, involving ninety-three university students enrolled in an intensive English language programme. The cohort was stratified into three distinctive learning groups: those exposed to GTM, CDM, and EBA, respectively. A determination of the most potent approach for English instruction represented the focal intent of this inquiry. Interviews, conducted by the researcher and teaching assistants, aimed to unearth the motivational substrates underpinning students’ English language acquisition endeavors. A meticulous cross-analysis proffers efficient language learning models, underscoring the pertinence of innovative learning approaches for English.
This study aimed to ascertain the learning model adopted by university lecturers in the digital era. Utilising an action research design, a mixed-method approach was employed with 32 students participating. Data were collected through two cycles of learning outcomes using online informal language learning (OILL) integrated with smartphones. These outcomes and observations were documented through photographs, video recordings, and classroom observation forms. Descriptive and content analyses were employed for evaluation and interpretation. The results revealed that a majority of students perceived the collaborative learning model, which integrates OILL with smartphones, as a technology-driven process that facilitated more flexible learning in the classroom. Crucial to this model's success was the level of student engagement, which influenced their behaviour towards OILL and smartphone use. Students in this study exhibited positive attitudes, evidenced by their enhanced self-direction, motivation, and improvements in various linguistic skills, critical thinking, and teamwork. The persistent use of the OILL and smartphone collaborative learning model by lecturers during the pandemic was observed, indicating its perceived superiority over traditional learning models, especially given the technological communication and interaction challenges experienced during the pandemic. The study underscores the importance of considering behavioural factors and the quality of OILL and smartphone applications in influencing student learning behaviour and teaching models. Therefore, the integration of OILL applications into a blended or hybrid teaching environment is suggested as an effective strategy for enhancing the quality of education in today's digital classrooms. It is recommended that future research adopt a quantitative approach with a more extensive sample to further elucidate the dynamics of learning outcomes associated with the use of OILL integrated with smartphones in the digital age.
During the COVID-19 pandemic, a profound impact was experienced in various domains, including education. The abrupt shift to remote learning presented challenges, especially in subjects necessitating practical problem-solving, commonly executed on traditional boards. It was observed that students at the College of Economics particularly grappled with Mathematics and Statistics during online sessions. This study aims to demonstrate the effectiveness of MS Excel in facilitating online, application-driven learning in Mathematics and Statistics. Initially, emphasis is placed on articulating basic mathematical expressions, ensuring full visibility of formulae for students. Subsequently, an illustrative approach is adopted to elucidate the resolution of intricate systems characterized by multiple equations and variables. This framework provides students with a foundation, aiding in the application of the acquired knowledge. Additionally, the versatility of MS Excel is highlighted by detailing its potential in deploying various statistical functions. For contextual relevance, data concerning human mobility during the pandemic, disseminated by Google for 135 nations globally, is employed. This research not only bridges the pedagogical gap but also offers a resilient teaching tool in uncertain times.
This study investigates the correlation between familial backgrounds and academic performance among secondary school students. Previous research supports the assertion that parental involvement constitutes a significant factor in a child's educational journey, representing their primary exposure to societal and educational structures. To achieve the research objective, three main research questions were addressed. A survey-based approach was adopted, implementing a random sampling technique to select a total of 66 respondents, consisting of 40 females and 26 males. Data were gathered through questionnaires and subsequently analyzed using descriptive statistics. The data were presented in tabular form to facilitate a clearer understanding of students' perceptions of familial background and its potential effects on academic achievement. The study revealed a pronounced positive correlation between family background and student academic performance within the learning institution. The study concludes that familial socioeconomic status plays a pivotal role in student academic outcomes. Thus, a collaborative approach involving both parents and educators in children's educational activities is recommended to enhance academic achievement.
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