High-performing Students Research Articles

Improving the Performance of Public Schools: Evidence from Latin American Countries in PISA 2022

ABSTRACT The achievement gap between public and private schools can lead to inequality of opportunities for the most economically disadvantaged students. Nonetheless, students in some public schools achieve as high as students from private schools. Using PISA 2022 data, we investigate the determinants of high student performance in public schools in Latin America. Logit models applied at the regional level show how public schools that guarantee lower bullying, a higher sense of belonging, and growth mind-set increase the likelihood of their students being among the top-performing ones. Socioeconomic status remains a significant driver of a higher average student performance in public schools.

Hypercritical or Superficial? Investigating the Metacognitive Awareness of Indonesian Bilingual Students in Writing Class

Metacognition has long touched upon critical issues related to educational psychology and gained advancing recognition in language learning. However, empirical research on its effect on academic writing remains limited. This study investigated metacognitive awareness among 167 Indonesian university students enrolled in an academic writing class, focusing on two self-assessment patterns: superficial (overestimating abilities) and hypercritical (underestimating abilities). Using an explanatory sequential mixed methods design, the researchers first compared students' actual writing exam scores with their self-assessments, which mirrored the lecturer’s grading rubric. Students were grouped into quartiles based on performance, and a Wilcoxon Signed Rank Test was conducted to examine differences between their actual and perceived scores. Results revealed distinct patterns. Among lower-performing students (bottom quartile), only 11 out of 53 students (approximately 21%) exhibited superficial self-assessment. In contrast, among higher-performing students (top quartile), 61 out of 63 students (97%) demonstrated hypercritical self-assessment. These results suggest that while only a minority of low-performing students were superficial, the majority of high-performing students tended to be hypercritical about their writing abilities. Follow-up interviews with four selected students further explored the reasons behind these patterns. Qualitative analysis identified three main contributing factors: person and task variables, response to feedback, and self-regulation strategies. The findings underscore the importance of fostering metacognitive awareness and accurate self-assessment in foreign language writing instruction to promote effective learning and self-regulated development.

Implementing the digital multimodality of a Google Translate-assisted approach to online academic EFL reading

This study implemented a multimodal Google Translate- (GT) assisted approach incorporating multiple literacies to online academic reading on global warming and climate change for 152 non-English major students of English as a foreign language (EFL) in Taiwan. A five-step model of the digital multimodality of a GT-assisted approach was employed. The learning activities focused on vocabulary development and reading comprehension; they were evaluated based on students’ reading performance in the pretest, GT-assisted test, post- test and delayed cloze test asking students to show comprehension in their responses to a questionnaire survey. Results indicated that students had significantly better performance in their post-test scores, with a larger effect size ranging from 2.14 to 3.05. English proficiency had a significant and positive correlation with their reading scores in the pretest, GT-assisted test and post-tests, but not in the delayed test. Students’ retention was 51.6% in the delayed test conducted 7 weeks after the post-test, and their reading scores were significantly higher than theirs in the pretest, with a medium effect size of 0.54. Regardless of student college, they also showed highly favorable perceptions towards the digital multimodality assisted by the GT-based tools. High-performing students perceived multimodal strategies provided by GT, especially in translation and audio aids. Implications for practice or policy: The digital multimodality of a GT-assisted approach should be implemented in online academic EFL reading. Non-English major EFL students should make good use of multimodal GT-based tools for improving their vocabulary development and reading comprehension. EFL teachers should adopt technological resources and ensure their optimal utilisation to create a friendly and interactive learning environment where students’ engagement and interaction can be effectively enhanced.

Evaluation of Students Ability Through the Continuous Rasch Model

This paper proposes an extension of the traditional Rasch model to evaluate student abilities and question difficulties in educational settings that utilize continuous scoring systems. This enhanced model incorporates maximum likelihood estimation (MLE) to calculate ability and difficulty parameters with demonstrated consistency and asymptotic normality. Traditional assessments often fall short by not accounting for errors from high-performing students and providing limited insights into student abilities beyond right or wrong answers. The continuous Rasch model developed here accommodates the complexities of continuous data and broadens the applicability of the Rasch model beyond dichotomous data types, making it suitable for modern educational evaluations. The paper introduces a continuous version of the Rasch model, leveraging it to derive a more comprehensive understanding of the interactions between student performance and test difficulty. This model views each scored response as a sum of independent Bernoulli trials, thus enabling a nuanced analysis of scores within a continuous range. Such a framework is particularly beneficial in scenarios where traditional dichotomous scoring fails to capture the depth of student understanding and question complexity.

Investigating the Association between Resilience and the 2D:4D Finger Length Ratio in Medical Students.

Investigating the Association between Resilience and the 2D:4D Finger Length Ratio in Medical Students.

Differential effects of GPT-based tools on comprehension of standardized passages

Due to the rapidly improving capability of large language models such as Generative Pre-trained Transformer models (GPT), artificial intelligence (AI) based tools have entered use in education at scale. However, empirical data are largely lacking on the effects of AI tools on learning. Here, we determine the impact of four GPT-based tools on college-aged participants’ reading comprehension of standardized American College Test (ACT)-derived passages using a randomized cross-over online study (n = 195). The four tools studied were AI-generated summaries, AI-generated outlines, a question-and-answer tutor chatbot, and a Socratic discussion chatbot. Consistent with our pre-registered hypotheses, we found a differential effect of AI tools as a function of baseline reading comprehension ability. AI tools significantly improved comprehension in lower performing participants and significantly worsened comprehension in higher performing participants. With respect to specific tools, low performers were most benefited by the Socratic chatbot while high performers were worsened most by the summary tool. These findings suggest that while AI tools have massive potential to enhance learning, blanket implementation may cause unintended harm to higher-performing students, calling for caution and further empirical study by developers and educators.

How Abstract Thinking and Divergent Thinking Affect Chinese Education

This paper explores the integration of abstract and divergent thinking into the Chinese educational system, traditionally dominated by rote memorization and high-stakes testing environments such as the Gaokao. Despite the success of this system in producing high-performing students on international assessments, it falls short in fostering creativity and critical thinking skills essential for the 21st-century global economy. Drawing on Vygotsky's Sociocultural Theory and Guilford's Theory of Divergent Thinking, this study proposes educational reforms incorporating inquiry-based and project-based learning to cultivate these skills. The research utilizes a mixed methods approach, collecting both quantitative and qualitative data from urban and rural schools to assess the impact of these pedagogical strategies. The findings suggest that integrating abstract and divergent thinking significantly enhances creative problem-solving and adaptability among students, urging a shift in teacher training and curriculum design to prioritize these cognitive skills.

What explains socioeconomic inequality in study abroad participation? New evidence from large-scale administrative data

ABSTRACT While studying abroad as part of a degree programme is increasingly common, there are widespread concerns about socioeconomic inequalities in participation. Using large-scale high-quality administrative data from Ireland, we show that students from affluent backgrounds are 1.5 times (46%) more likely to study abroad than non-affluent students. Applying a Gelbach decomposition, we find that prior academic performance and field of study explain most of the observed difference. We also show, for the first time, considerable heterogeneity in the relationship between participation and socioeconomic status by field of study and that inequalities are much greater for high-performing students.

Exploratory Factor Analysis of STEM’s Subjective Task Value and Expectation for Success Scale

This pilot study addresses the concerning trend of high-achieving students opting out of STEM courses, despite their capabilities and interests. Understanding the factors influencing this decision is crucial for enhancing STEM education and retention rates globally. This research is relevant as it validates the constructs of STEM subjective task value (STV) and STEM expectation for success (EFS), which are significant contributors to this phenomenon. We employed a purposive sampling approach, collecting data from 111 high-performing students enrolled in a public university in Malaysia. The study utilized Exploratory Factor Analysis (EFA) to establish reliable measures for the STV and EFS constructs, using a 10-interval scale for item responses. Data analysis was conducted with IBM SPSS version 28.0, applying the Principal Component extraction method with Varimax Rotation. We also performed Bartlett’s Test of Sphericity and assessed sampling adequacy using the Kaiser-Meyer-Olkin (KMO) measure. The reliability of the retained items was tested using Cronbach’s alpha. Our findings confirmed the validity and reliability of the instruments, retaining nine items for STEM subjective value and six for STEM expectation for success. This study provides a validated framework for measuring STV and EFS among high achievers in STEM, informing strategies to encourage greater enrollment in these fields.

Use of Learning Analytics in K–12 Mathematics Education

The generation, use, and analysis of educational data comes with many promises and opportunities, especially where digital materials allow usage of learning analytics (LA) as a tool in data-based decision-making (DBDM). However, there are questions about the interplay between teachers, students, context, and technology. Therefore, this paper presents an exploratory systematic scoping review to investigate findings regarding LA usage in digital materials, teaching, and learning in K–12 mathematics education. In all, 3,654 records were identified, of which 19 studies met all the inclusion criteria. Results show that LA research in mathematics education is an emerging field where applications of LA are used in many contexts across many curricula content and standards of K–12 mathematics education, supporting a wide variety of teacher data use. Teaching with DBDM is mainly focused on supervision and guidance and LA usage had a generally positive effect on student learning with high-performing students benefiting most. We highlight a need for further research to develop knowledge of LA usage in classroom practice that considers both teacher and student perspectives in relation to design and affordances of digital learning systems. Finally, we propose a new class of LA, which we define as guiding analytics for learners, which harnesses the potential of LA for promoting achievement and independent learning.

Evaluation of modified essay questions (MEQs) as an assessment tool in third-year medical students’ modular summative assessment

AimWhether case-based modified essay questions (MEQs) are crucial to summative assessment in medical curriculum is still debatable. The current study aimed to evaluate third-year medical students’ performance in case-based MEQs and multiple-choice questions (MCQs) in summative assessment in the endocrine module.MethodsStudents’ scores in mid and final module MEQs and MCQs were analyzed over four successive years from 2018/2019 to 2021/2022, where comparisons were made between students’ scores in MEQs and MCQs, and between scores of students of different categories. Moreover, the correlation between MEQs and MCQ scores and total grades was evaluated.ResultsThe study revealed better students’ performance in MCQs compared to MEQs, and this pattern persisted over the study period reflecting that case-based MEQs were challenging to students. High-performing students got higher scores in MEQs than the low performers, denoting the ability of MEQs to discriminate between high and low grades. It was also observed that MEQs correlated significantly with students’ final grades similar to the MCQs indicating its importance as an assessment tool. Concerning the COVID year, students got higher grades in MCQs and lower scores in MEQs compared to other years of the study period which clarifies that MCQs offer a reliable assessment tool during exceptional circumstances.ConclusionImplementing case-based MEQs and MCQs in the summative assessment of medical curriculum favored discrimination between high and low scorers, minimized chances for students’ guessing and fostered deep learning and analytical comprehension of knowledge, and ensured medical students’ exposure to various item types. The current findings highlight the potential of using well constructed case-based MEQs in high stake medical exams together with MCQs for comprehensive assessments of problem solving skills.

Unveiling the divide: Analyzing critical thinking skills in literature and commerce students

Skill-oriented education molds learners in a holistic manner, where they are challenged to think critically, reflect analytically, interrogate precisely and contemplate from various dimensions. The goal of the current work is to examine the insightful analysis of undergraduate literature learners with those of commerce students. Sixty high-performing students were selected from various colleges located in Bengaluru. Feedback on undergraduate literature/commerce students’ level of critical thinking has been gathered through a questionnaire. According to the study, gamification and digital storytelling might enhance the critical thinking abilities of these students. Here, three different pedagogies, case studies, logical reasoning questions, and multiple-choice questions (MCQ) were used to assess the skill sets of these two groups. The study suggests methods that can be used to help undergraduate literature and commerce students become more adept at critical thinking.

Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course

The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This contribution presents an exploratory and correlational study conducted with 37 computer science students who used ChatGPT as a support tool to learn database administration. The article addresses three questions: The first one explores the degree of use of ChatGPT among computer science students to learn database administration, the second one explores the profile of students who get the most out of tools like ChatGPT to deal with database administration activities, and the third one explores how the utilization of ChatGPT can impact in academic performance. To empirically shed light on these questions the student’s grades and a comprehensive questionnaire were employed as research instruments. The obtained results indicate that traditional learning resources, such as teacher’s explanations and student’s reports, were widely used and correlated positively with student’s grades. The usage and perceived utility of ChatGPT were moderate, but positive correlations between students’ grades and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students. This indicate that high-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with a lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities.

Writing Strategies of Chinese Undergraduate English Major Students

English writing is very important for Chinese students since it is a crucial part of many tests, such as the College Entrance Examination for high school students in China, College English Tests (CET), and the Unified National Graduate Entrance Examination. Some students frequently employ writing strategies such as memorizing sample phrases, sentences, or essay templates to cope with exams. Some studies confirmed that there is a certain relationship between students’ use of writing strategies and their writing performance. There are some high-performing students who usually can not only express certain content in a coherent structure but can also organize the sentences well with little to no grammatical mistakes when writing in English. The participants of this case study are undergraduate students who are high-performing writers and English majors. They completed two writing tasks, followed the think-aloud protocol, and joined the semi-structured interview. Results determined the strategies of the participants, such as meta-cognitive, cognitive, rhetorical, and affective/social strategies. Findings have important implications for teaching and learning writing.

The effectiveness of Gen AI in assisting students’ knowledge construction in humanities and social sciences courses: learning behaviour analysis

ABSTRACT Currently, generative AI has undergone rapid development. Numerous studies have attested to the benefits of Gen AI in programming, mathematics and other disciplines. However, since Gen AI mostly uses English as the intrinsic training parameter, it is more effective in facilitating the teaching of courses that use international common notation, but few scholars have researched the fitness of Gen AI-assisted teaching of humanities courses in Chinese-language environments. To address these gaps, this study examined the learning behaviours of 30 students using Gen AI to help them answer questions on economic law tests using the Lag Sequential Analysis. The results show that the following: (1) The use of Gen AI to aid learning in an economic law course did not significantly improve the cognitive level of academics from the perspective of knowledge construction. (2) According to the characteristics of students’ behavioural paths via Gen AI-assisted learning, their behavioural patterns can be classified into autonomous and innovative, moderate, and lacking innovation. (3) Different learning modes when Gen AI-assisted teaching was used affected the final results, which were as follows: High-performing students favoured the autonomous and innovative pattern, medium-performing students favoured the moderate pattern, and low-performing students favoured the lacking innovation pattern.

Unskilled and unaware? Differences in metacognitive awareness between high and low-ability students in STEM

IntroductionMetacognition, or the ability to monitor and control one's cognitive processes, is critical for learning in self-regulated contexts, particularly in introductory STEM courses. The ability to accurately make predictions about one's ability and performance can determine the effectiveness in which students effectively prepare for exams and employ good study strategies. The Dunning-Kruger pattern, where low-performing individuals are more overconfident and less accurate at the ability to predict their performance than high-performing individuals, is robustly found in studies examining metacognitive monitoring. The extent to which the Dunning-Kruger pattern can be explained by the lack of metacognitive awareness is not yet established in the literature. In other words, it is unclear from prior work whether low-performing students are “unskilled and unaware” or simply “unskilled but subjectively aware.” In addition, arguments about whether this pattern is a psychological phenomenon or a statistical artifact of the measurement of metacognition can be found in the literature.MethodsStudents enrolled in three different physics courses made predictions about their exam scores immediately before and after taking each of the three exams in the course. Student predictions were compared to their exam scores to exam metacognitive accuracy. A new method for examining the cause of the Dunning-Kruger effect was tested by examining how students adjust their metacognitive predictions after taking exams.ResultsIn all contexts low-performing students were more overconfident and less accurate at making metacognitive predictions than high-performing students. In addition, these students were less able to efficiently adjust their metacognitive predictions after taking an exam.DiscussionThe results of the study provide evidence for the Dunning-Kruger effect being a psychological phenomenon. In addition, findings from this study align with the position that the skills needed to accurately monitor one's performance are the same as those needed for accurate performance in the first place, thus providing support for the “unskilled and unaware” hypothesis.

행동기준 평정척도(BARS)를 적용한 대학생 핵심역량 측정도구 개발 및 타당화 연구: D대학의 의사소통역량을 중심으로

Objectives This study attempted to develop the core competency’s measurement tool and secure its validity by focusing on the D University’s communication capabilities and applying the behaviorally anchored rating scale based on experiences or behaviors closely related to students’ university education and life. Methods This study applied appropriate research methods according to a six-step’s systematic research procedure, including core competency analysis and competency modeling, analysis of behavioral characteristics of excellent students through FGI for professors and BEI for students, development of core competency’s measurement tool through expert council, validation through expert delphi survey, pilot test and analysis, and finalizations stage of system, definition, and measurement tool. Results The research results are as follows. First, the core competency’s system and definition with a high consistent were derived through a systematic competency modeling process. Second, the behavioral characteristics of hyperformers(high-performing students) were analyzed through interview with professors and students and reflected in the behaviorally anchored rating scale. Third, the core competency’s measurement tool(draft) with a high consistent were confirmed. Fourth, a core competency measurement tool(revised version) was tentatively derived by verifying its validity through two expert delphi surveys. Fifth, a pilot test was conducted to improve effectiveness of the core competency’s measurement tool, and reliability and validity were secured. Sixth, the finalizations stage was completed, and the system, definition, and measurement tool of core competencies were confirmed. Conclusions By developing a core competency’s measurement tool based on the behaviorally anchored rating scale focusing on D University’s communication capabilities and then revealing the process of securing reliability and validity, It could play a significant role in suggesting alternative methods to objectively measure the core competencies of each university.

Integrating formal and informal subsystems: A case study of Promise City’s early care and education model

This case study explores effective practices within a district’s early care and education model. Interviews were conducted with eight parents of high-performing students and seven district ECE partners from Promise City. The results of this case study lead to the development of a model that comprises best practices within the (a) informal ECE subsystem, (b) formal ECE subsystem, and (c) intersection of the informal and formal subsystems. The model incorporates the best practices identified by Ma et al’s. (2016) meta-analysis, as well as unique best practices found within Promise City. The findings highlight the importance of both informal and formal learning environments and actively working to bridge these environments.

Exploring Debugging Challenges and Strategies Using Structural Topic Model: A Comparative Analysis of High and Low-Performing Students

Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students’ trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online undergraduate CT course. Participants used Scratch, a block-based programming language, and their journals from five assignments were analyzed using Term Frequency-Inverse Document Frequency and Structural Topic Modeling. High-performing students engaged with diverse topics and specific blocks tied to their weekly projects while low-performing students faced repetitive and broad challenges, such as understanding motion blocks and broadcast concepts. These patterns reveal that low-performing students struggle particularly during the ‘diagnose the fault’ phase of debugging, often hindering their progress in the final stage. Such challenges highlight the necessity for targeted educational interventions to improve the debugging proficiency and overall CT skills. The study underscores the importance of further research into students’ logical thinking processes during code review and debugging, suggesting the use of think-aloud protocols and detailed tracking of debugging practices for deeper insights. This research contributes to the field by showing that differentiated instruction and strategic support can enhance debugging skills across different student performance levels.

Predictive Model for Identification and Analysis of Factors Impacting Students Academic Performance Using Machine Learning Algorithms

Background: Education is a vital component of both societal and individual growth. To create effective learning environments, it is essential to understand the factors that influence student achievement. However, the application of machine learning algorithms can drive positive change, providing higher education institutions with effective solutions to their challenges. Aim: This research develops a predictive model to identify and analyze the factors affecting student academic performance, aiming to provide institutional administrators and lecturers with a better understanding of the key factors impacting student success. Method: A comprehensive dataset was collected from undergraduate students at Modibbo Adama University (MAU), Yola, comprising student-related, home-related, lecturer-related, and institution-related factors. Model development was carried out using Python in Google Colab, a cloud-based Jupyter notebook environment. Classification algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Method (GBM), and Random Forest (RF) were applied to predict student academic performance. Four evaluation metrics namely; accuracy, precision, recall, and f1-score were used to analyze the models. Results: The Random Forest model outperformed the other machine learning models, achieving an overall accuracy of 95%. For predicting low-performing students, the model achieved a precision of 0.9677, recall of 0.9474, and f1-score of 0.9574. For high-performing students, the precision was 0.9324, recall was 0.9583, and f1-score was 0.9452. These strong performance metrics across both low and high performing student groups demonstrate the effectiveness of the Random Forest model in accurately predicting student academic performance based on the factors considered in the study. Feature importance analysis identified lecture attendance, sponsor support, quality of facilities, and lecturer clarification as the most influential factors on student performance. Other features, such as accommodation, employment status, and program preference, were found to have a low impact. These findings emphasize the importance of considering a comprehensive set of student, lecturer, institution, and home-related factors to sustain a conducive learning environment and enhance educational practices.