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 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.
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.
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.
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.
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