Low-performing Students Research Articles

Development of a metacognition co-curriculum for a university course in introductory organic chemistry

Metacognition is a fundamental skill that allows advanced learners to adapt to diverse learning environments. Metacognition, however, can be domain specific and students may fail to generalize metacognitive skills across domains. Thus, students in higher education may require specific training to acquire relevant metacognitive skills in differing domains or may need cueing to engage their metacognitive skills and knowledge in new domains. The present report describes the development of a co-curricular metacognitive program for chemistry students and suggests how this program could be adopted by other chemistry courses or adapted for other domains in higher education. Several supports were introduced in this program including self-assessment of competence with learning task inventories (LTIs; i.e., detailed lists of learning tasks), self-assessments of confidence regarding in-class content questions, and performance predictions and postdictions on tests. In general, exposure to these supports resulted in overall performance and confidence gains. However, individual differences were evident with some students demonstrating greater learning gains than others. Initial Dunning-Kruger effects associated with pre-and postdictions, with low-performing students overestimating grades and high-performing students underestimating grades, decreased over exposure. A summary of the evolution of this metacognitive co-curricular program, the educational literature that steered it, and the differential impact on students is explained.

Serving adults who have difficulty reading: challenges faced by U.S. adult education administrators

AbstractRecent assessments suggest that 43 million adults in the United States have difficulty completing basic reading tasks. Although the federal Workforce Innovation and Opportunities Act (WIOA) prioritizes education for adults with the greatest needs, there is long-standing concern that federally-funded programs might not serve lower-performing students well. This study sought the perspectives of state administrators of WIOA-funded adult education to identify challenges in serving adults who have difficulty reading and areas for future research. Identifying challenges and creating a robust evidence base can center adults who have difficulty reading as important participants in the field of practice and support the creation of effective strategies for their inclusion and success in WIOA-funded programming.

Effect of Mnemonics in the Teaching of Araling Panlipunan 9 on Students’ Written Assessment Scores: A One-Group Study Design

This study investigated the effectiveness of mnemonics in teaching Araling Panlipunan 9 on students’ written assessment scores. It used a one-group pretest-posttest design, a quasi-experimental design of the quantitative research methodology. The study was conducted at a National High School in Apayao, Philippines. Purposive sampling was used to select 40 Grade 9 students as participants. The researchers used a validated assessment instrument adopted from the Department of Education modules. The results showed that the students improved their scores significantly after using mnemonics in teaching, as indicated by the increase in the mean average score, the rejection of the null hypothesis, and the positive correlation between mnemonic usage and performance. The study recommended continuing to use mnemonic techniques in teaching Araling Panlipunan 9, providing remedial support to low-performing students, and promoting active student engagement in the learning process.

The transformative potential of Artificial Intelligence for Education

Education today faces a range of challenges, but also unique opportunities, vis-à-vis the evolution of digital technologies. Traditional teaching methods struggle to meet the diverse needs of students, as classrooms comprise learners at different levels. The Education AI project represents an initiative aimed at integrating Artificial Intelligence (AI) into the classroom to address the diversity of students and the challenges faced by traditional teaching methods. The project's core objective is to enhance student learning outcomes by providing an adaptive and personalised learning experience, thereby making education more equitable and effective. This paper evaluates the project's impact on student learning, teacher workload, and its broader applicability in education. The study's methodology combines quantitative and qualitative data, employing a mixed-methods approach to provide a comprehensive analysis of the Education AI system's effectiveness. Preliminary findings indicate a notable improvement in student performance, particularly among low and medium-performing students, with quantitative data revealing an average improvement of 23% in assessments for low-performing students. The system's user-friendly interface, gamified elements, and immediate feedback mechanisms have been well-received, with a majority of students reporting enhanced engagement and understanding of mathematical concepts. Teachers have also reported positive experiences, highlighting the system's role in reducing their workload and facilitating personalised and adaptive teaching and learning. Qualitative feedback from interviews with teachers and students provides further insights into the system's impact, emphasising its varied and engaging question types, adaptivity, and the shift from paper-based to digital learning. The feedback also reveals a desire for expansion to other subjects and enhancements in the system's analytics for more targeted support.The Education AI project demonstrates the transformative potential of AI in the classroom, with significant improvements in student performance and positive reception from both students and teachers. The study highlights the importance of addressing technical challenges and expanding the system's capabilities for broader application. The project offers valuable insights into the future of education, where technology and human expertise converge to create a more personalised, adaptive engaging, and effective learning experience for all students. This initiative, underpinned by the transformative potential of AI for education, aims and informing and guiding future innovations in educational technology.

Teacher value-added and gender gaps in educational outcomes

This paper uses rich administrative data from Chile to estimate teacher value added (TVA) on test scores and on an educational attainment index. We allow each teacher to have a different TVA for male and female students and show that differences in TVA explain an important part of the gender gaps we observe in test scores and postsecondary education trajectories. The gaps in gender-specific teaching effectiveness are especially pronounced in mathematics. Indeed, eliminating within-teacher differences in math test score VA would reduce the gender gap in math performance by 67%. We explore what could be behind these gaps in gender-specific TVA and find no significant differences in what makes teachers effective for male and female students. We do find, however, significant associations between teacher characteristics—e.g., gender and performance in the college admission exam—and practices—e.g., paying attention to low-performing students, congratulating students who improve, and having a good relationship with students—with teacher effectiveness. Finally, we also show that math teachers tend to be biased in favor of male students and that teachers with smaller gender biases are more effective for both, male and female students.

EBS 고교강의의 사교육비 경감효과와 이에 영향을 미치는 요인

Objectives The purpose of this study is to estimate the effect of EBS high school lectures on reducing shadow education expenses of high school students and identify factors influencing the effectiveness.
 Methods The analysis utilized survey results from 1,320 students and 1,000 parents, which were obtained through stratified sampling. The Chi-square test method was employed to analyze the causes of differences in the reduction effect based on demographic characteristics.
 Results First, the estimated average monthly shadow education expenses per student in South Korean high schools in 2022 were 556,467 KRW. The reduction effect of monthly shadow education expenses per student due to EBS high school lectures was estimated to be 67,432 KRW. The estimated reduction in shadow education expenses attributable to EBS high school lectures accounted for 12.15% of the overall shadow education expenses for high school students. Second, while the impact of EBS high school lectures on the participation in shadow education was not significant, it was found to have a considerable influence on the scale of shadow education expenses. Third, the reduction effect was smaller in groups with higher shadow education expenses and larger in groups with lower supplementary education expenses.
 Conclusions EBS high school lectures not only contribute to reducing shadow education expenses but also appear to play a role in addressing educational opportunity disparities. To enhance the reduction effect of EBS high school lectures on shadow education expenses, the differentiated lectures and expanded materials targeting both high-performing and low-performing students is suggested.

Analysis of Difficulty Index, Discrimination Index and Distractor Efficiency in Multiple Choice Questions

Background: The item analysis of multiple-choice questions (MCQs) is an essential tool that can provide input on validity and reliability of items. It helps to identify items, which can be revised or discarded, thus building a quality MCQ bank. Aim: The aim of the study to evaluate the quality of MCQs by analyzing DIF, DI and DE and to find out the association of MCQs having good difficulty and discrimination indices with DE. Method: The study was conducted in the Department of Maternity and Child Health Nursing at the College of Nursing during the academic year 2023, second semester. Students of level six who took the final exam of one-course second-semester academic year 2023, A total of 152 third-year Bachelor of Nursing. The item analysis explored the difficulty index (DIF I) and discrimination index (DI) with distractor effectiveness (DE). Data was kept confidential and the descriptive and inferential statistics will be computed using SPSS 22.0. Results: A total of 50 MCQs, the majority, that is, 74 (82%) MCQs had a good/acceptable level of difficulty with a mean DIF I of 94.94 ± 6.36 (mean ± SD); this indicates that, on average, the MCQs were relatively easy for the students, as the difficulty index is close to 100. The discrimination index, which assesses the ability of the MCQs to differentiate between high and low-performing students, had a mean of 0.11 and a standard deviation of 0.47. This relatively low mean value suggests that the MCQs had limited discriminatory power, meaning they did not effectively distinguish between students with different levels of performance. 150 (75%) were categorized as functional distractors (FDs). These FDs effectively served their purpose by distracting students from selecting the correct answer, challenging their knowledge and understanding. On the other hand, 50 distractors (25%) were classified as non-functional distractors (NFDs). Conclusion: The categorization of the MCQs based on distractor efficiency highlights the importance of having well-constructed and effective distractors. The presence of functional distractors challenges students' decision-making, while non-functional distractors need to be revised or replaced to improve the overall quality of the MCQs. This finding indicates a potential area for improvement in the construction and selection of MCQs to enhance their ability to differentiate student abilities

Psychometric properties of the reading self-efficacy scale in Peruvian students aged 10 to 16 years

BackgroundReading self-efficacy is a key factor for students’ academic performance and motivation and given the low reading performance of Peruvian students compared to the average, it is crucial to understand and improve their reading self-efficacy.ObjectiveTo evaluate the psychometric properties of the Reading Self-Efficacy Scale in a Peruvian sample.MethodologyUsing a sample of 560 students aged 10 to 16 (M = 13.5, SD = 1.93), confirmatory factor analysis (CFA) and gender-specific invariance analysis were conducted.ResultsA two-dimensional, second-order model was taken into consideration. Significant differences were discovered in gender invariance, suggesting that the scale is comparable between the genders.ConclusionThe validation of the Reading Self-Efficacy Scale in the Peruvian context provides a useful tool to assess and develop Peruvian students’ reading self-efficacy, with implications for professional practice and educational policy.

A Model for Competency-Based Grading and Its Effect on Student Outcomes in a Biomechanics Course.

Competency-based grading (CBG) can take different forms in different subject areas. We present a method for implementing CBG in a biomechanics course with nine primary learning objectives. Competency in each learning objective is measured by the student's ability to correctly answer knowledge questions and solve analytical problems in the field of biomechanics. The primary goal of implementing CBG was to provide more opportunities for lower-performing students to learn the material and to demonstrate that learning. To determine the efficacy of CBG to improve student learning, the primary measure was course grade distribution before and after implementation of CBG. The course grade distribution data indicated that CBG has primarily helped midperforming students to improve their grades. Because of the limitations of course grades as a measure of learning, we also performed analysis of student performance on successive attempts which indicated initial and secondary attempts are best, with student success declining on subsequent attempts. Anecdotally, many students improved performance, and thus their grade, on the (optional) final exam attempts. Limitations of the study include the limited course offerings with CBG (three), and that effects of COVID-19 may be confounding CBG data. Also, the approach places nearly all the grade on quizzes or exams. However, the approach could be modified to include homework grades, projects, and the like. Overall, the student learning in this course and implementation appears to be only positively affected, so this approach appears to have benefits in a biomechanics course.

Causes of Low Academic Performance in Adamu Augie College of Education, Argungu Kebbi State

Abstract 
 The study was to find out the causes of low academic performance of students in Adamu Augie College of Education, Argungu in Kebbi State. The population of the study consisted of 400 students of Adamu Augie College of Education, Argungu out of which 120 were randomly selected. A structural questionnaire was used for data collection. The data collected from the respondents were analyzed using frequency counts and mean scores for all the research questions.
 The findings revealed that all variable factors are responsible for the low academic performance of students except the involvement of parents in the Parents Teachers Association (P.T.A). Based on these findings, the following recommendations are made: Teachers should employ various teaching strategies as well and the teaching must be from simple to complex for a better understanding of their students, Teachers should develop positive attitudes towards the students under his/her control to ensure effective learning and better performance of their students and Parents should give more attention and proper monitoring to their children education.

Learning loss one year after school closures: evidence from the Basque Country

AbstractWe use census data on external assessments in primary and secondary schools in the Basque Country (Spain) to estimate learning losses due to the COVID-19 pandemic in March 2021, 1 year after school closures, which lasted from March to June 2020. Differences-in-differences with student and school-by-grade fixed effects show an average learning loss of 0.045 standard deviations, an effect that is smaller than short-run effects estimated by previous papers, and estimated after 6 months of one of the most successful school reopening campaigns among OECD countries. The effect is larger in Mathematics, moderate in Basque language, and none in Spanish language. Controlling for socioeconomic differences, learning losses are especially large in public schools, and also in private schools with a high percentage of low-performing students. On the other hand, we find a regression to the mean within schools, possibly due to a compressed curriculum during the whole period. Finally, and more importantly, we use unique novel data on student socio-emotional well-being and show for the first time that students with higher learning losses self-report significantly worse levels of socio-emotional well-being due to the pandemic.

Tracing Progress in Children’s Executive Functioning and Language Abilities Related to Reading Comprehension via ExeFun-READ Intervention

One important internal factor influencing reading comprehension is a child’s executive functioning. The primary objective of this paper is to evaluate the effectiveness of a cognitive stimulation program, ExeFun-READ (a program focusing on executive functioning stimulation via the L1 (Slovak language) curriculum with a specific focus on enhancing reading comprehension). The program is explicitly based on the assumed bi-directional relationship between executive functioning and language abilities related to reading comprehension. The program is domain-specific; the curriculum of L1 (Slovak) is a curricular area in which cognitive mediation occurs. The study will investigate whether the domain-specific ExeFun-READ intervention had a positive effect on children’s executive functioning and language subcomponents of reading comprehension. Keeping in mind ecological validity, ExeFun-READ was designed primarily for educational purposes, specifically professional tutoring for low-performing students. The intervention consists of 30 units; each unit lasts for 45–60 min. A stimulation unit approximates a teaching unit rather than a clinical experimental intervention. In total, 151 low-performing students attending grade four from seven elementary schools took part in the project. The study employs a pre-test–training–post-test design with three conditions: experimental, active control, and passive control. In the current study, the intervention led to improved language abilities related to reading comprehension. Significant improvements were found in vocabulary (semantic knowledge), completion of sentences (syntactic knowledge), and classification of terms (verbal fluency and inferencing) in the group of children that received the ExeFun-READ intervention. In terms of executive functioning, the improvement only extended to switching fluency.

What Learning Strategies are Used by Programming Students? A Qualitative Study Grounded on the Self-regulation of Learning Theory

Self-regulation of learning (SRL) is an essential ability for academic success in multiple educational contexts, including programming education. However, understanding how students regulate themselves during programming learning is still limited. This exploratory research aimed to investigate the regulatory strategies externalized by 51 students enrolled in an introductory programming course. The objective was to identify the SRL strategies used by these students during multiple phases of the learning process and compare the SRL behavior of high and low-performers. The following research questions guided this investigation: (RQ1) What regulation of learning strategies are used by programming students?; and (RQ2) How do the SRL strategies used by high and low-performing students differ?. The findings demonstrate that learning to program involves complex psychological resources (e.g., cognition, metacognition, behavior, motivation, and emotion) and that students present heterogeneity in their SRL repertoire. In addition, high and low-performing students showed significant differences in how they regulate, which can contribute to understanding the factors that may contribute to learning programming. Lastly, we argue that for analyzing SRL strategies, it is necessary to consider the specificities of programming education, which motivated the development of a conceptual framework to describe the identified strategies and regulatory phases in this learning domain.

A comparative analysis of student, educator, and simulated parent ratings of video-recorded medical student consultations in pediatrics

BackgroundSimulation-based education (SBE) affords learners opportunities to develop communication skills, including those related to pediatrics. Feedback is an integral part of SBE, and while much research into feedback from multiple sources exists, the findings are mixed. The aim of this comparative study was to replicate some of this work in a novel area, pediatric medical education, to better understand how multisource feedback (self, educator, and simulated parent) may inform learning and curriculum design.MethodsDuring their pediatric rotation, medical students participated in a consultation with a simulated parent, engaged in video-assisted self-reflection, and received feedback from both an educator and the simulated parent through an e-learning platform. The Pediatric Consultation Skills Assessment Tool (PCAT) was used for self-assessment and educator feedback, and the Consultation and Relational Empathy (CARE) measure was used for simulated parent feedback.ResultsOur results showed that high-performing students underrated their performance, and low-performing students overrated their performance. Feedback from multiple sources helps to identify both areas of weakness in student performance and areas of weakness in student self-appraisal. Overall, general areas of weakness identified for the learners related to making contingency plans and providing easy-to-understand explanations for simulated parents. Some simulated parent feedback did not align with educator and student ratings, highlighting the value of including the simulated parent perspective. Our findings question whether a third party can reliably judge the simulated parent’s level of understanding.ConclusionMultisource feedback allows students to develop layered insights into their performance and supports self-appraisal. Aggregating feedback through an e-learning platform allows educators to gain greater insights into the strengths and weakness of students and design a more tailored teaching plan to support student needs.

Curriculum Analytics of Course Choices:

In a higher education context, students are expected to take charge of their learning by deciding “what” to learn and “how” to learn. While the learning analytics (LA) community has seen increasing research on the “how” to learn part (i.e., researching methods for supporting students in their learning journey), the “what” to learn part is still underinvestigated. We present a case study of curriculum analytics and its application to a dataset of 243 students of the bachelor’s program in the broad discipline of health sciences to explore the effects of course choices on students’ academic performance. Using curriculum metrics such as grading stringency, course temporal position, and duration, we investigated how course choices differed between high- and low-performing students using both temporal and sequential analysis methods. We found that high-performing students were likely to pick an elective course of low difficulty. It appeared that these students were more strategic in terms of their course choices than their low-performing peers. Generally, low-performing students seemed to have made suboptimal choices when selecting elective courses; e.g., when they picked an elective course of high difficulty, they were less likely to pick a following course of low difficulty. The findings of this study have design implications for researchers, program directors, and coordinators, because they can use the results to (i) update the course sequencing, (ii) guide students about course choices based on their current GPA (such as through course recommendation dashboards), (iii) identify bottleneck courses, and (iv) assist higher education institutions in planning a more balanced course roadmap to help students manage their workload effectively.

Performance increases in mathematics within an intelligent tutoring system during COVID-19 related school closures: a large-scale longitudinal evaluation

The closure of schools due to COVID-19 disrupted learning routines of thousands of students, resulting in reported performance decreases, especially among lower-performing students. However, some studies on students’ performance within intelligent tutoring systems (ITS) also found significant performance increases for times of school closures as compared to before. However, little is known about students’ longitudinal performance trajectories within ITS. Accordingly, we evaluated longitudinal data from German students (n ≈ 2,700 students; n ≈ 5 million problems) enrolled in an ITS for learning mathematics from January 2017 until the end of May 2021 to investigate the effect of periods of school closures (first and second) on students’ performance within the ITS during, between, and after school closures. We observed significant performance increases for both lower- and higher-performing students during, between, and after COVID-19 related school closures. Importantly, these improvements were more pronounced for lower-performing students compared to higher-performing students. Together, these results suggest that ITS may have helped to maintain mathematics learning, particularly for lower-performing students during COVID-19 related school closures and that these beneficial effects persisted at least for the following months when schools opened again. As such, the use of ITS for learning mathematics seems an appropriate approach for distance learning during times of crisis.

A comparative study of online gaming between high-performing and low-performing students—A case from China

Children are a nation's future. In the digital era, online gaming has become deeply ingrained in the lives of children, significantly impacting them. Thus, comprehensively examining this situation is imperative. However, the existing research has predominantly focused on analyzing online gaming among underachieving students. This narrow focus has magnified the negative implications of gaming among this group while overlooking the underlying structural realities. To address this, in contrast to prior studies emphasizing underachieving students' gaming behavior, this paper takes a holistic and group-specific approach. Based on academic performance and using purposive sampling, 35 participants (18 students, 12 parents, and 5 teachers) were selected for a comprehensive investigation. This study compares the gaming behavior, significance, external assessments, and the urban–rural and socioeconomic factors associated with underachieving and high-achieving students. The study reveals stark differences in gaming behavior between the two groups. Underachieving students tend to engage in excessive gaming, whereas their high-achieving counterparts exhibit more moderate gaming habits. Further analysis uncovers distinct meanings associated with online gaming for these two groups. Underachieving students' gaming patterns align with the established theory of compensatory gaming, fulfilling their lack of achievement and meaning in real life. Conversely, high-achieving students conform to the enrichment theory, viewing gaming as a means to enrich their learning and overall lives, rather than a substitute for what is lacking. The disparity in gaming behavior is further amplified by the differential evaluations provided by parents and teachers. Moreover, this study underscores the deeper structural distinctions between these two groups rooted in urban–rural backgrounds and family education.

Enhancing Student Engagement in a Flipped Hindi Classroom through Classroom Response Systems

Flipped learning, known for promoting critical thinking and learner-centric environments, faces a challenge in crafting effective in-class strategies, particularly in language classes. This study presents a flipped learning class centered around instant feedback integration. The goal is to enhance student engagement in a Hindi flipped classroom by combining Classroom Response System (CRS) with deliberate instructional strategies. While CRS is recognized for promoting active learning, limited research exists on designing effective in-class activities using CRS in a flipped setting. In this Hindi class, the flipped learning implementation extended beyond video lectures, encompassing quizzes and student-generated questions. This innovative approach emphasizes student autonomy, engagement, and critical thinking to foster active participation. A survey was conducted to evaluate the perceptions of 33 students enrolled in the ‘Intermediate Hindi I’ course regarding the CRS-based flipped learning, with focus on Satisfaction (S), Engagement (E), and Relevance (R). Employing a mixed-methods sequential explanatory design, the study revealed significant correlations between academic performance scores, attendance rates, and interest in CRS-based flipped learning. Higher academic performances were associated with increased interest, while lower academic performances showed diminished interest. A Focus Group Interview with low-performing students further supported these findings. Overall, this research sheds light on the effectiveness of CRS-based flipped learning in a language class, emphasizing the impact on student outcomes based on academic performances and attendance.

Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine Learning

Today, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test’s grade level, the best accuracy obtained was 89% and concerned data regarding the students’ interaction with the LMS together with students’ grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome.

Does Ability Sorting Improve Efficiency of Low-performing Students Evidence from PISA 2022 Survey

Does Ability Sorting Improve Efficiency of Low-performing Students Evidence from PISA 2022 Survey