This study investigated the Impact of Adjustments on Academic Performance of Senior Secondary School Students in Jere Local Government Area (LGA), Borno State, focusing on academic, social and emotional adjustments. Three (3) research questions were answered and 1 (one) hypothesis was tested in the study. Descriptive survey research design was used in the study. The target population for the study consists of all the 5,543 SS II and SS III senior secondary school students in Jere LGA. A sample of 500 students were selected using stratified simple random sampling technique in accordance with Kriejcie and Morgan (1970). A researcher-developed questionnaire titled ‘Students’ Adjustments and Academic Performance Questionnaire (SAAPQ)’ was used for data collection. The instrument was validated by three (3) experts in the field of educational psychology from University of Maiduguri and Borno State University. The instrument was pilot tested on 50 students and the reliability was calculated through test-retest method and reliability index of .76 was obtained. The instrument consists of four (4) sections and Eighteen (18) items. Data collected were analyzed using frequency count and percentage scores to assess the demographic information of students, descriptive statistics of (mean, standard deviation, frequency) to analyse the impact of adjustment on students’ academic performance and t-test was used to assess the gender difference in adjustment and academic outcomes. The hypothesis was tested at the 0.05 level of significance using the Statistical Package for Social Science. The findings of the study revealed that academic, social and emotional adjustments significantly impact the academic performance of senior secondary school students in Jere LGA, Borno State. Based on the findings, the study recommended that Ministry of Education and SUBEB should enhance counselling services, life skills education, teacher training on psychosocial support and adjustments and increased involvement from parents, communities and NGOs to foster better adjustment and academic success

Background: This experimental study explores the integration of Problem-Based Learning (PBL) and the Mathtastic application to enhance mathematical reasoning and collaborative skills among primary school students. Aims: Grounded in constructivist learning theory, the research aims to determine the effectiveness of a technology-supported PBL approach in fostering students’ higher-order thinking and teamwork abilities in the context of mathematics education. Methods: The study employed a quasi-experimental design with control and experimental groups comprising Grade 5 students from a public elementary school. The experimental group received mathematics instruction through PBL integrated with the Mathtastic app, while the control group was taught using conventional methods. Data were collected using pre- and post-tests on mathematical reasoning, a collaboration rubric, and observation sheets. Result: The results showed a statistically significant improvement in the reasoning and collaboration performance of students in the experimental group compared to those in the control group. These findings suggest that combining PBL with interactive educational technology like Mathtastic can create a more engaging and effective learning environment for young learners. Conclusion: The study provides practical implications for teachers seeking to integrate digital tools and inquiry-based approaches to enrich mathematics learning experiences at the primary level.

Inclusion of patient age and sex in single best answer (SBA) questions is standard practice, whereas ethnicity is typically included only when considered clinically relevant. This selective use may inadvertently cue students or introduce bias. This study examined whether including patient ethnicity in SBA questions influences student performance, response time and item psychometric properties. A prospective randomised controlled study was conducted using two versions of a final-year formative assessment. In each version, approximately half of the questions included the patient's ethnicity, while the corresponding questions in the alternate version omitted this information. Students were randomly assigned to one of the two exam versions. Two hundred and sixty-seven final-year medical students participated in the study. The mean score for questions without ethnicity was 80.3% and for questions with ethnicity was 79.8% (mean difference = -0.5 pp, p = 0.12). On average, students spent 1.6 seconds longer to answer questions with ethnicity compared to questions without ethnicity (p < 0.01). In SBA questions with and without ethnicity, there was no significant difference in mean facility (79.8% vs. 80.3%, p = 0.17) or mean item-total score point-biserial correlation (0.20 vs. 0.21, p = 0.63). These indicate that including patient ethnicity did not significantly impact overall student performance or mean item-level psychometric properties. However, it was associated with longer response times, suggesting a small but significant increase in cognitive load. Educators involved in assessment design should carefully consider this added cognitive burden when deciding whether to include patient ethnicity in future assessments.

This study looks at how language education increasingly uses data from school software and adaptive learning platforms. This creates a big teaching contradiction. While these systems claim to give personalized feedback on student performance, they often turn the complex, cultural process of learning a language into simple numbers that lack context. This can accidentally favor students who speak only one language and frame others as lacking skills. This article contends that technical data skills alone aren’t enough for teachers; instead, argue that teachers need more than just technical skills to read this data. They need a "Critical Data Literacy" (CDL), built on the habit of carefully reflecting on their practice. The researcher has made a three pillar framework: Interrogate, Contextualize and Humanize, as reflective fix. This framework will turn the instructors from passive information consumers to active proponents. The proposed method helps them read dashboard analytic through an equity lens, recover their professional judgment, and map assessment to the rich realities of multilingual learners. This ideology is about denial of the data but an appeal for a considerate, thoughtful and ethical incorporation of the data into humanistic work in language teaching, proposing a pragmatic anchor between critical theory and classroom practice.

This study investigates the relationship between time management, peer pressure and the academic performance of part-time students of Abraham Adesanya Polytechnic. Recognizing the unique challenges faced by part-time students in balancing academic responsibilities with work, family and social commitments, the research aims to evaluate how these factors influence academic success. A total of 52 questionnaires were distributed with the 40 valid responses analyzed. Descriptive statistics and linear regression analysis were employed to examine the data. The findings revealed that a significant relationship between peer pressure and academic performance (R=0.325, R2=0.106, p&lt; 0-05) as well as between time management and academic performance (R= 0.418, R2=0.175, p&lt;0.05) These results indicate that both peer influence and time management skills play critical roles in shaping the academic outcomes of part-time students. The study underscores the importance of developing effective time management strategies and creating awareness around peer-related challenges to enhance students’ success. Recommendations for Institutional support and further research are discussed.

This paper examines how education policies have shaped rural student performance in four developing countries—China, India, Uganda, and Peru—through a comparative literature review. Despite significant policy efforts, disparities in rural learning outcomes persist. The study identifies a research gap in understanding how policy design and local implementation interact to influence rural education quality. Using a narrative comparative approach, it synthesizes findings from peer-reviewed studies and policy evaluations published over the past two decades. The analysis highlights three cross-cutting challenges: ensuring teacher quality, sustaining rural teacher retention, and strengthening local implementation systems. A comparative perspective reveals that community participation, when combined with accountability and resources, can positively influence outcomes. Taken together, the cases suggest that effective reform requires long-term commitment, adaptability, and attention to rural realities. The review contributes to both scholarship and practice by offering policy lessons relevant to developing contexts such as Papua New Guinea, where similar institutional and geographic constraints persist.

&lt;span lang="EN-US"&gt;The application of digital technologies to teach mathematics is increasing and how educators use these tools and their impact on the performance of students needs to be sufficiently researched. In this study, we explore middle school mathematics teachers’ utilization of Algebra Nation (AN), an online instructional resource, to supplement instruction and improve the performance of their mathematics students, particularly in algebra. A survey was conducted involving 596 teachers and a quasi-experimental trial. We examined the relationship between AN use and Algebra I end-of-course (EOC) exam performance. The findings showed that engagement in AN observed through student login, video watches, and professional teacher development is associated with improved student’s success rates in mathematics. This study also revealed that challenges such as digital disparity, data privacy issues, and professional teacher development were identified to impact the effective implementation of digital technologies in teaching mathematics. This study recommends that there should be systematic integration of web resources, combined with targeted teacher training to significantly enhance mathematics instruction. These conclusions inform future technology adoption and policymaking, with emphasis on equal access and systemic teacher support.&lt;/span&gt;

Health science represents the fastest-growing industry in the United States, a trend not predicted to slow for the next decade. The high demand for health professionals is seen with courses in Anatomy and Physiology (A&P), which often have high enrollments, high diversity, and require active pedagogies, hands-on learning, diverse study techniques, and interactions with instructors and peers. In Spring 2020, however, COVID-19 caused major disruptions to everyday life around the world, forcing significant changes to academic curricula and how students were taught. Institutions were forced into all online instruction, thus eliminating hands-on experiences, altering student learning, and hindering student-student and student-instructor interactions. Now that universities are back to in-person classes, there is a great opportunity to evaluate the impacts that the COVID-19 pandemic had on student performance and learning. It is important to investigate the impacts of COVID-19 because it can provide information on how to improve pedagogies going forward, while also helping to prepare for when the next pandemic occurs. To identify and analyze the impacts of the COVID-19 pandemic on student performance and learning, this study compared student performances in A&P labs before and after COVID-19. For A&P I, practical scores and overall grades declined following the pandemic, not returning to prepandemic levels until Fall 2023 (4-5 semesters). For A&P II, practical scores and overall grades declined in the first semester following the pandemic but returned to prepandemic levels in Spring 2022 (1 semester). While this study focuses on university students and courses in a biological field, the findings can be applied to other academic fields and to students across educational levels.NEW & NOTEWORTHY It is important to investigate the impacts of COVID-19 because it can provide information on how to improve pedagogies going forward, while also helping to prepare for when the next pandemic occurs. Practical scores and overall grades declined following the pandemic for Anatomy and Physiology (A&P) I and II courses but returned to prepandemic levels significantly earlier for students in A&P II. The findings here can be applied to other academic fields and to students across educational levels.

An error complementarity-based iterative learning approach via categorical boosting for student performance prediction

Differentiated instruction on undergraduate students based on classification and prediction of students performance using PSO-BP neural network

Artificial intelligence for student performance prediction in blended learning: A systematic literature review

Mathematics anxiety is a concern in higher education, often linked to students’ academics and emotional well-being. This study examined the mathematics anxiety level and academic performance of students enrolled in GEC 002-Mathematics in the Modern World, a general education course at the University of La Salette. It specifically explored students’ demographic profile, academic performance, and mathematics anxiety level across the following factors: physical and emotional, assessment, and social. Differences or relationships between their anxiety levels and their profile or performance were also tested. This descriptive-correlational study involved 265 students from five departments, selected through stratified sampling. Data gathered through a questionnaire on mathematics anxiety and students’ grades in GEC 002 were analyzed using frequency and percentage, weighted mean, independent samples t-test, one-way ANOVA, and Pearson correlation. Most students performed well academically, with moderate overall anxiety. Assessment-related factors ranked highest, followed by social factors, while physical and emotional symptoms were less evident. No significant differences in mathematics anxiety were found across age, gender, or department. However, students with “excellent” academic performance reported significantly lower anxiety (p = 0.003). A weak, statistically significant positive correlation emerged between mathematics anxiety and academic performance, suggesting that, in this context, anxiety may function as a slight motivator rather than a debilitating hindrance, implying other factors play a considerable role. An intervention program was proposed to promote academic success and emotional well-being. Although limited to a single institution and based on self-reported, cross-sectional data, the study highlights the importance of addressing mathematics anxiety in HEIs.

In this study, we examined factors surrounding grade inflation, a concern that continues to persist in institutions of higher education. We argue that while GPAs may be rising, it is due to a multitude of internal and external influences, including environmental pressures, student, faculty, program, and curriculum variables, as well as several others, and not necessarily to the awarding of improper grades. We proposed a model consisting of 13 factors that theoretically have a positive or negative influence on grade point averages (GPAs), as well as an additional four that require further investigation. We then used historical GPA data from a small university in the Western United States to test and refine the model. The data consists of over eight million data points and covers the period from the Fall of 2006 through the Spring of 2020. We found no evidence of rising GPAs in the aggregate data and observed both falling and rising GPAs in the two colleges of the case university. We found a significant negative effect on GPAs for four of our propositions: open enrollment, adjunct faculty, hard discipline, and online; and three significant positive effects on GPA for three others: traditional undergraduate 100- to 200-level courses, persistence, and graduate students. We also demonstrate that numerous factors have contributed to the rise in GPAs over the past few decades. The question remains as to whether the grades that students receive are indeed reflective of the quality of work submitted; however, the ultimate proof is graduates’ success in the marketplace. We hope that other institutions of higher education will use and adapt our model to investigate potential grade inflation in their own organizations. Keywords: higher education, grade inflation, model of grade inflation, student performance, faculty performance, online, in-seat, traditional learner, adult learner

This study aimed to assess which domains of data-driven strategic planning significantly influenced school improvement metrics in Panabo North District using a non-experimental quantitative design with a descriptive-correlational method. A total of 168 public elementary teachers were selected through stratified random sampling, and data were gathered using validated and pilot-tested survey questionnaires. Results showed that data-driven strategic planning was extensively practiced in the district, with the implementation of data-informed interventions as the highest-rated indicator. However, engagement in continuous professional development was rated moderately extensive, signaling the need for more consistent teacher training. The extent of school improvement metrics was also extensive, particularly in student engagement and teacher collaboration, while parental involvement was rated only moderately extensive. A significant positive relationship was found between data-driven strategic planning and school improvement metrics, especially in the areas of continuous professional development, data utilization, and collaborative data interpretation. Continuous professional development emerged as the strongest predictor of school improvement. However, the implementation of data-informed interventions did not show significant influence on school metrics. These findings emphasize the value of strengthening teacher learning and parental involvement. They also suggest that reinforcing data-driven culture may lead to improved instructional practices and school outcomes. Keywords: Data-Driven Strategic Planning; School Improvement Metrics; Continuous Professional Development; Student Performance Data; Collaboration

This study examines the impact of broken homes on the academic performance of Junior High School students in Cape Coast Metropolis, Ghana. Employing a descriptive research design, data were collected via self-administered questionnaires from a randomly selected sample of 89 students at University Junior High School. The study identified various types of broken homes, including divorced or separated parents, single-parent households, guardianship arrangements, and frequent household changes, and explored their influence on students’ academic focus and achievement. Findings reveal that broken home situations often lead to emotional distress, financial worries, and reduced academic concentration, contributing to declines in performance. Parental involvement was found to be a mitigating factor, while school support resources were perceived as inadequate. Based on these findings, recommendations urge educational stakeholders to conduct systematic surveys, enhance counselling services, and foster collaborations among teachers, parents, and psychologists to support students from broken homes. Future studies should expand sample sizes, incorporate mixed methods, and explore socioeconomic and coping factors that influence academic resilience

This review article analyzes the evidence on the impact of curricular adaptations and inclusive strategies on the mathematical performance of students with cognitive disabilities in primary and secondary education. A literature review protocol was developed with explicit eligibility criteria, searches in specialized databases, two-stage screening, standardized data extraction, quality assessment, and narrative and quantitative synthesis using effect size estimation and moderator analysis. The included studies show that adjusting objectives, content, sequences, and assessment, combined with explicit teaching, concrete-representational-abstract sequencing, universal design for learning, peer tutoring, and digital supports, produces significant improvements in accuracy, fluency, problem solving, and learning retention, with particularly robust effects on numbering and operations. The magnitude of the benefits depends on the intensity of the intervention, the fidelity of implementation, and the presence of distributed practice and continuous formative assessment, conditions that favor generalization to new tasks and contexts. It is concluded that curricular adaptations are the central mechanism for operationalizing mathematical inclusion, although research gaps remain in longitudinal follow-ups, cost-effectiveness, and equity analysis.

Timely educational interventions and improved learning outcomes heavily rely on the ability to accurately predict student academic performance using hybrid machine learning models. This systematic review addresses a critical gap in the existing literature by evaluating recent advancements in this domain, with a strong emphasis on both model performance and methodological rigor. During this process current research efforts was analyzed by examining various approaches employed in recent studies and assessing the effectiveness of several hybrid models in forecasting student performance. This systematic review is focused on studies indexed within the span of 20 years. Following the removal of duplicates and irrelevant entries, a total of 1,004 studies was used with predefined keywords. The review primarily concentrated on key model performance indicators, including accuracy, sensitivity, specificity, F1-score, mean squared error (MSE), mean absolute error (MAE), and R-squared (R²). It also examined the programming languages used predominantly Python and noted recurring methodological challenges. Among the Hybrid models assessed, Random Forest and Neural Network consistently demonstrated superior predictive accuracy. However, due to differences in datasets and evaluation strategies, performance metrics varied significantly across the studies. Common limitations identified in the reviewed works included data imbalance, model overfitting, and challenges associated with hyperparameter tuning. Although hybrid models exhibit considerable potential for accurately predicting student academic outcomes, the review emphasizes the urgent need for stronger methodological consistency and standardized evaluation to enhance the reliability and generalizability of future research. Addressing issues such as dataset imbalance, improving model generalizability, and implementing robust hyperparameter optimization techniques should be central to upcoming studies. Furthermore, refining review methodologies can help ensure the validity and replicability of findings in this evolving field.

Comparative evaluation of competency-based and real-patient education in undergraduate clinical clerkships: insights from student feedback and performance

This study explores the perceived usefulness and usability of Artificial Intelligence (AI) tools among university students in Sri Lanka and how these perceptions relate to learners’ performance. As AI adoption expands across different sectors of society, understanding its benefits in the educational context is especially important in developing countries like Sri Lanka. Using a quantitative approach, this research aims to measure students’ use of AI tools, assess their perceived usefulness for academic tasks, and analyze how these perceptions correlate with students’ self-reported academic performance. Descriptive analyses and statistical analyses, including correlation analysis, linear regression, t-tests, and Analysis of Variance (ANOVA), were conducted to investigate the relationships between perceived AI benefits, usage patterns, academic performance, and demographic factors among university students in Sri Lanka. The findings are expected to reveal a positive relationship between perceptions of AI tool usage and improvements in student learning and performance, which would imply that AI could be a valuable resource in higher education. Additionally, the study will investigate demographic differences in perceptions of AI and its utilization. Ultimately, the results will provide concrete evidence to support the strategic design and structured implementation of an AI enhanced online learning platform in Sri Lankan universities, offering students a more innovative and effective learning experience. The findings of this study emphasize the importance of structured AI literacy programmes and policy level strategies for integrating AI tools into Sri Lankan higher education to enhance the learning quality.

The main objective of the study was to determine the relationship between academic integrity and scholastic performance of senior high school students in Pindasan National High School. The proponents used a quantitative non-experimental particularly descriptive-correlational method of research and utilized the researcher – made questionnaires. The study employed statistical tools; Cronbach Alpha, Average Weighted Mean, Pearson-r, and T test. The respondents were composed of one hundred eighty two (182) senior high school students for the school year 2022-2023. It was determined that there is a significant relationship between academic integrity and scholastic performance of senior high school students in Pindasan National High School. It was found out that the level of academic integrity and scholastic performance of senior high school students is descriptively high. There is no significant difference on academic integrity and scholastic performance when grouped according to year level however there is a significant difference when analyzed by gender.