Student Performance Data Research Articles

Enhancing the quality of teaching and learning through ChatGPT and similar large language models: Challenges, future prospects, and ethical considerations in education

The integration of Artificial Intelligence (AI) into the field of education has ushered in a transformative shift in the creation and dissemination of assignments. This research delves into the implications and complexities surrounding the incorporation of AI-enhanced ChatGPT submission in the realm of education. AI-enhanced ChatGPT submissions offer a multitude of benefits. First and foremost, they offer educators a convenient means to produce a wide array of content, encompassing quizzes, essay prompts, and problem sets, thus saving both time and effort. AI has the capacity to customize assignments according to the specific needs of individual students, ensuring personalized learning experiences. Additionally, AI's ability to analyze student performance data can aid educators in pinpointing areas where students might be facing challenges, allowing them to adapt their teaching methods accordingly. Nonetheless, this innovation also raises substantial concerns. The authenticity and quality of AI-generated submission may come under scrutiny, potentially giving rise to issues of plagiarism and academic integrity. It is crucial to ensure that AI-generated content aligns with educational objectives and standards. Before disseminating AI-generated submission to students, educators must meticulously curate and review them. Furthermore, the role of teachers in the educational process extends beyond merely assigning tasks. They provide mentorship, guidance, and feedback that surpass the capabilities of ChatGPT. This human touch is indispensable for nurturing critical thinking, fostering creativity, and enhancing emotional intelligence. Ethical considerations also play a pivotal role. Both students and educators must comprehend how AI-generated assignments are created, ensuring transparency in the process. Safeguards must be in place to safeguard data privacy, and measures need to be taken to address bias in AI algorithms. Hence, it is imperative for teachers and educational institutions to critically evaluate the place of AI in education and determine the circumstances under which AI-generated assignments can be integrated to enrich the educational landscape.

Digital Catalysts in Linguistic Didactics

This paper examines the effectiveness of innovative digital technologies in linguodidactics using the example of the WeTalk online school. The research is based on various scientific studies and our own practice. In this study, the authors use a mixed-methods approach to study the transformational performance of digital technology-based linguodidactics and its impact on language learning outcomes. Qualitative analysis of interviews with English language course teachers reveals the universal and transformative potential of digital tools, while quantitative analysis of student surveys and performance data provides empirical evidence of increased language proficiency. The results highlight the key role of digital innovation in promoting personalized learning, overcoming language barriers and developing intercultural competence. The discussion addresses the possibilities and limitations of digital technologies in language education, advocating ongoing professional development and partnerships to harness the full potential of technology in shaping effective language learning.

Impact of the Duration of Semester Examinations on Students’ Academic Performance in Colleges of Education in North-Central Nigeria

This study aimed to examine the impact of the duration of semester examinations on academic performance among students in four colleges of education in North Central Nigeria. A stratified sampling technique was applied to select 200 participants from the four Colleges of Education in the North-Central Zone of Nigeria (FCT COE Zuba, FCE Okene, COE Minna, and COE Akwanga). The four colleges of education were stratified based on location, and a proportional number of students were randomly selected from each stratum. The research employed a combination of focus group interview, questionnaires and examination scores to collect relevant data. Subsequently, the academic performance data of students from the short-duration calendar were compared to their performance data from the standard-duration calendar. Descriptive statistics were used to summarize the characteristics of the sample, such as mean, standard deviation, and frequency distribution. An analysis of variance (ANOVA) was conducted to compare the mean academic performance scores across different durations of semester examinations. The outcome of the study revealed that the current 4-week duration of semester examinations is the ideal duration, while a shorter duration was rejected. Moreover, the flexibility in duration based on subject complexity was also favored. The result further indicates that the duration of semester examinations does have a significant impact on academic performance. Therefore, educational institutions are advised to adopt a flexible approach to the duration of examinations, particularly for subjects with varied complexities and volumes, to promote better academic outcomes. The findings emphasize the need for educational institutions to adhere to a four-week duration of examinations to promote better academic outcomes.

A Comparative Analysis of Decision Tree Classification Algorithms for Blended Learning Analytics in WEKA

This study explores the application of decision tree classification algorithms for analyzing student performance data within a blended learning environment. The analysis, conducted using WEKA 3.8.6, focused on four attributes believed to influence student performance: course type, course level outcome (CLO), topic learning outcome (TLO), and level of assessment. A comparative analysis of J48, Random Forest, and SimpleCart algorithms revealed valuable insights. J48 demonstrated efficiency in model building, while Random Forest offered a balance between interpretability and accuracy. SimpleCart achieved the highest classification accuracy but could be less interpretable. The selection of the optimal algorithm depends on the analytical goals. J48 is suitable for rapid exploration, while SimpleCart prioritizes accuracy. Random Forest offers a compromise for scenarios where both understanding and accuracy are important. This study provides a foundation for understanding student performance through decision trees and highlights opportunities for further exploration using additional attributes, rule-based learners, and other machine learning algorithms. By leveraging these techniques, educators within blended learning environments can gain a deeper understanding of student performance and tailor their practices to optimize learning outcomes.

Kemampuan Pemecahan Masalah Sosial Siswa Kelas 12 SMA Negeri 1 Ciruas

The purpose of this research is to measure the performance of 12th grade students at SMA Negeri 1 Ciruas in the subject of biodiversity and to assess the 12th grade students ability to solve problems through environmentally based learning activities. The research procedure involves using pre-tests and post-tests. The pre-test is used to determine the students initial abilities. The post-test is conducted to assess the students problem solving abilities after learning through Problem Based Learning using Student Discussion Sheets (LKPD). Student performance data, obtained from answering Student Discussion Sheets (LKPD), is analyzed descriptively and qualitatively. Scores obtained from all assessed aspects are then converted to determine the groups assessment criteria. The problem solving abilities of 12th grade students at SMA Negeri Ciruas fall into the "Good" category, and there is an improvement in scores between the pre-test and post-test evaluations.
 

Analysis of the Influence of Some External Factors on Students Mathematics Achievement Based on Decision Tree Algorithm

With the continuous progress of the educational system in various countries, people attach more and more importance to education. In todays school education, the math score of students is an important indicator for the assessment of student learning outcomes, on the one hand, can be a real, objective reflect students actual learning and teachers teaching level, on the other hand can also choose for students after learning methods, teachers to play a good role in guiding the teaching plan. This study analyzes the student performance data of math students in kaggle, explores the factors that affect the students performance of this course from many aspects, and then puts forward reasonable suggestions and applies them to practical teaching so as to improve the teaching quality. All of the data the research has been getting is from Kaggle math students. The data set contains nearly 400 pieces of relevant educational data about students, and there are dozens of factors that affect the performance of each student, such as parental cohabitation, educational support and the desire for higher education. The topic of our study is to analyze the impact of students family situations and educational support on their math performance by using the C4.5 algorithm in the decision tree algorithm as an auxiliary tool. And this paper finally came to this interesting conclusion: motivated students with poor parental relationships and little educational support from school and family tend to do better in math.

A Study on the Pre- and Post-Pandemic Media of Instruction and Learning Effectiveness in Information Systems Courses

The objective of this study is to understand the effectiveness of medium of instruction based on student performance in courses offered face-to-face prior to the pandemic and virtual hybrid mode after the onset of the pandemic from a media richness theoretical perspective. This study analyzed data from 1157 students from 43 Information Systems courses over a 6-year pre- and post-pandemic period. This study analyzed student performance data from face to face and virtual hybrid courses across medium of instruction, type of course, course level, and gender. Overall, students performed better in face-to-face classes prior to the pandemic than the virtual hybrid courses. Across gender, and type of course, and course levels, face-to-face medium of instruction was found to be best for student performance. The post-hoc interviews with students and faculty indicated that face-to-face was the choice of instruction medium for technical courses and virtual hybrid mode was preferred for theory intensive conceptual courses, confirming the results from the data analysis.

Data‐Driven Decision‐Making Practice in Higher Education Institutions in Ethiopia

ABSTRACTThis study investigates the practice of data‐driven decision‐making in higher education institutions in Ethiopia. It presents the results of a mixed‐methods investigation that was conducted at two public universities in Ethiopia. Data was collected via a survey questionnaire from 91 faculty members, nine ICT experts, four higher education registrar experts, and 194 graduating undergraduate students. The study also involved interviews with two directors from the university's education program relevance and enhancement office and four senior officers from library departments. The findings revealed that the majority of the instructors used manual data analysis and basic software, such as MS Excel, to analyze student performance, achievement, and assessment data. Institutions use many disconnected in‐house systems for different activities without intelligent tools or data warehouses to support data analytics. The results indicate deficiency in activities that promote data analytics and data‐driven decision‐making, such as capacity‐building training and leadership support within institutions.

Parental absence and student academic performance in cross-national perspective: Heterogeneous forms of family separation and the buffering possibilities of grandparents

Many children around the world reside separately from one parent or both parents. Analyzing cross-national student performance and survey data, we investigate whether forms of family separation show distinctive associations with academic performance. Three findings emerge: 1) Students separated from both parents are most academically disadvantaged, while those living separately from mothers are usually more disadvantaged than those living without fathers. 2) In households with co-resident grandparents and in national contexts conducive to multi-generational or extended family co-residence, disadvantages associated with father-absent and dual-parent-absent households attenuate. 3) Academic disadvantages associated with parental absence attenuate as national emigration exceeds immigration.

Result of Comparative Investigation between Face-to-Face Teaching Method and Simultaneous Online and Live Teaching Method

This is a concise summary of this investigation, which is a comparative research and analysis in a bivariate model of approval rates between in-person and remote (distance) students, with simultaneous live online transmission. The reference database is the management system and student registry in the graduate program at Federal University of Paraná (UFPR), aiming to compare the performance rates between in-person students and a synchronous remote class (live and online). The investigated model is an innovative pedagogical approach, featuring an interface with a television-like format that is broadcasted live. This approach differs from pedagogical models such as distance learning, hybrid, or synchronous. Thus, a direct comparison with the results of distance learning models can provide evidence to measure student performance data. In this model, remote students attend live courses without temporal flexibility, only spatial flexibility. This work presents the results of a comparative study based on a bivariate model, considering approval rates between students attending remote (live online) and in-person classes. The aim of this research is, considering the comparative approval rates of students and possible statistical correlations, to propose adjustments and contributions for better guidance of the hybrid and synchronous online class system. Additionally, it examines the impact of technology on teaching and learning outcomes.

The Influence of AI-Powered Adaptive Learning Platforms on Student Performance in Chinese Classrooms

This study delved into the impact of AI-powered adaptive learning platforms on student performance within the context of Chinese classrooms. Utilizing a comprehensive retrospective approach, the research examined the effects of these innovative platforms on academic achievements, engagement levels, and overall learning outcomes. In the past decade, the integration of artificial intelligence (AI) into educational technology has revolutionized traditional teaching methods. Adaptive learning platforms, driven by AI algorithms, customize learning experiences to cater to individual student needs and learning paces. This study examined a diverse range of Chinese classrooms, encompassing various grade levels and subjects, to ascertain the widespread influence of such platforms. Data collection involved the analysis of historical student performance data from multiple academic years. The study assessed both quantitative metrics, such as test scores and grade improvements, and qualitative indicators, including student feedback and engagement patterns. Through rigorous statistical analyses, the study established correlations between the utilization of AI-powered adaptive learning platforms and enhanced student performance. The findings revealed a significant positive association between the integration of AI-powered platforms and student outcomes. Students exposed to personalized learning experiences exhibited improved academic achievements compared to those in traditional classrooms. Furthermore, the study identified heightened levels of engagement and motivation, as the adaptability of these platforms catered to individual learning preferences. In conclusion, this research underscores the transformative role of AI-powered adaptive learning platforms in Chinese classrooms, highlighting their ability to elevate student performance and engagement. As China continues to embrace technological advancements in education, this study offers valuable insights into the evolution of pedagogical approaches and their positive outcomes on student learning in the past. Keywords: AI-powered, Adaptive learning platforms, Student performance, Chinese classrooms, Influence

Online case-based learning in medical education: a scoping review

BackgroundCase-Based Learning (CBL) in medical education is a teaching approach that engages students as learners through active learning in small, collaborative groups to solve cases from clinical patients. Due to the challenges afforded by the COVID-19 pandemic, small group learning such as CBL, transitioned quickly to include technology-enhanced learning to enable distance delivery, with little information on how to apply pedagogical frameworks and use learning theories to design and deliver online content.MethodsTo extend understanding of online CBL a scoping review protocol following the PRISMA-ScR framework explored the literature that describes the use of online CBL application in medical education and the outcomes, perceptions, and learning theories. A literature search was conducted in January 2022 followed by a subsequent review in October 2022. After peer review using the PRESS guidelines, the CASP appraisal tool was used to assess the rigor of each study design.ResultsThe scoping review identified literature published between 2010 and 2022 (n = 13 articles), on online CBL in the field of medical education with 11 observational studies describing student and facilitator perceptions and two randomized controlled studies. Positive perceptions of online learning included a flexible work-life balance, connection with learners, and improved accessibility. Negative experiences of online CBL included poor internet access, a distracting learning environment, and loss of communication. In the studies that collected student performance data, results showed equivalent or improved outcomes compared to the control. The CASP appraisal tool highlighted the deficiencies in most study designs, lack of framework or learning theory, and poor reproducibility of the methods to answer the research questions.ConclusionThis scoping review identified literature to describe the academic outcomes, and student and facilitator perceptions of online CBL in medical education. However, the CASP tool uncovered deficiencies in study descriptions and design leading to poor quality evidence in this area. The authors provide recommendations for frameworks and learning theories for the future implementation of online CBL.

Student’s Public Speaking Ability Through Presentation Task

This study attempts to describe the ability of speaking skill as oral communication in public by giving the task of individual presentation performance. This study addresses to the result of speaking course as a part of Intermediate English subject. It was conducted to Renewable Energy Engineering students in the academic year of 2022-2023 as well as their main objective of English Study. The researcher is interested to have the data of public speaking achievement because the importance of public speaking skill regarding inability of students to speak in public or perform oral presentation. When all oral presentations are marked as evidence that students' speaking abilities could meet the needs of the industry, this has become more difficult. Unfortunately, graduates nowadays do not possess the degree of oral presenting abilities that are fulfil the demand of stake holder. In this research, the research study is designed as descriptive quantitative research to have the data of students public speaking ability and describe their performance. The researcher used random sampling and Task based Test instrument to have the data of student public speaking ability and also observation to have the data of student public speaking performance. The research finding would be included of the student performance and the task result by speaking rubric which is included the speaking skill components. The results of research showed that by giving presentation task, it has good level for students’ public speaking ability and the result of presentation task builds the Renewable Energy Engineering student’s confidence to perform public speaking.

Impact of practice quizzes on student perceptions and assessment outcomes of a third-year pharmacotherapy course

Background: Practice quizzes have been associated with student learning, comprehension, and retention. This study aims to assess the impact of practice quiz utilisation for an infectious disease (ID) pharmacotherapy course. Methods: Student performance data for practice quizzes and assessments were collected. A survey was administered regarding student perception information of the practice quizzes. Results: Most students (95%) completed at least one quiz. For most of the quizzes (15/20), the number of quiz attempts among students who met the 80% competency bar was statistically higher than that of those who did not meet competency on course tests. Most students agreed/strongly agreed the quizzes impacted examination study behaviours (86%), performance (89%), and confidence (90%). Students supported additional course implementation of quizzes (97%), planned to utilise available practice quizzes in the future (96%), and would recommend them to other students (91%). Conclusion: ID practice quizzes were well received by students and may be a valuable learning tool.

Guess for Success? Application of a Mixture Model to Test-Wiseness on Multiple-Choice Exams

The use of large lecture halls in business and economic education often dictates the use of multiple-choice exams to measure student learning. This study asserts that student performance on these types of exams can be viewed as the result of the process of elimination of incorrect answers, rather than the selection of the correct answer. More specifically, how students respond on a multiple-choice test can be broken down into the fractions of questions where no wrong answers can be eliminated (i.e., random guessing), one wrong answer can be eliminated, two wrong answers can be eliminated, and all wrong answers can be eliminated. The results from an empirical model, representing a mixture of binomials in which the probability of a correct choice depends on the number of incorrect choices eliminated, we find, using student performance data from a final exam in principles of microeconomics consisting of 100 multiple choice questions, that the responses to all of the questions on the exam can be characterized by some form of guessing, with more than 26 percent of questions being completed using purely random guessing.

Data-Driven Decision-Making (DDDM) for Higher Education Assessments: A Case Study

The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an important and powerful role in students’ educational experience and evaluation of their academic abilities. However, there are many stigmas associated with both “traditional” and alternative assessment methods. Rethinking assessments is increasingly happening worldwide to keep up with the shift in current teaching and learning paradigms due to new possibilities of using digital technologies and a continuous improvement of student engagement. Many educational decisions such as a change in assessment from traditional summative exams to alternate methods require appropriate rationale and justification. In this paper, we adopt data-driven decision-making (DDDM) as a process for rethinking assessment methods and implementing assessment transformations innovatively in an HE environment. We make use of student performance data to make an informed decision for moving from exam-based assessments to nonexam assessment methods. We demonstrate the application of the DDDM approach for an educational institute by analyzing the impact of transforming the assessments of 13 out of 27 subjects offered in a Bachelor of Information Technology (BIT) program as a case study. A comparison of data analysis performed before, during, and after the COVID-19 pandemic using different student learning measures such as failure rates and mean marks provides meaningful insights into the impact of assessment transformations. Our implementation of the DDDM model along with examining the influencing factors of student learning through assessment transformations in an HE environment is the first of its kind. With many HE providers facing several challenges due to the adoption of blended learning, this pilot study based on a DDDM approach encourages innovation in classroom teaching and assessment redesign. In addition, it opens further research in implementing such evidence-based practices for future classroom innovations and assessment transformations towards achieving higher levels of educational quality.

Assessment of Effective Teaching Using by TOPSIS method

Effective teaching is a fundamental component of quality education. Educational institutions and stakeholders are constantly seeking ways to assess and enhance teaching practices to ensure optimal learning outcomes for students. This paper provides an overview of the various approaches and methods used for assessing effective teaching. It delves into the significance of aligning assessment strategies with specific learning goals and discusses the importance of considering both quantitative and qualitative measures. The paper highlights the role of student feedback in evaluating teaching effectiveness. It explores the use of surveys and evaluations to gather insights into instructional methods, classroom management, and the overall learning experience. Additionally, the incorporation of peer evaluations and self-assessment by instructors is examined as valuable tools for comprehensive teaching assessment. Furthermore, the paper addresses the growing influence of technology in teaching assessment. The utilization of learning analytics and data-driven insights offers a new dimension to evaluating teaching effectiveness. This includes the analysis of student performance data, engagement metrics, and online interaction patterns. It involves evaluating and understanding the various factors that contribute to effective teaching and its impact on student learning outcomes. Student Learning Outcomes: Effective teaching directly influences student learning outcomes. Research in this area helps identify teaching practices that lead to improved academic achievement, critical thinking skills, problem-solving abilities, and overall educational success. Educational Quality: High-quality teaching is a cornerstone of a strong education system. Understanding what makes teaching effective enables educational institutions to provide a better learning experience, attract and retain skilled educators, and enhance their reputation for producing successful graduates. TOPSIS, This method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. Alternative taken as Student Evaluations, Peer Reviews, Self-Assessment, Classroom Observations Evaluation preference taken as Enhanced Learning Outcomes, Continuous Improvement, Subjective Bias, Time-Consuming. From the result Student performance data is got the first rank and classroom observation is having the lowest rank.

Features of distance learning in medical education - Systematic review

Due to the COVID-19 pandemic, higher educational institutions have moved from the usual mode of study to online learning. Therefore, the purpose of this systematic review was to investigate the effectiveness of distance learning for students and teachers in the context of the COVID-19 pandemic. Data search was carried out in databases – PubMed, Google Scholar using the search strategy “distance learning”, “effectiveness of distance learning”, “online education”, “distance learning in medical education”, “feedback from students and teachers about distance learning”, “distance learning during COVID-19”. The screening study has found 174 records after removing duplicates. The qualitative synthesis included 15 studies. Analysis of the included studies was performed using the Risk of Bias Quality Assessment Tool for Cohort Studies and the Assessment Tool for case series and cross- sectional studies. Quantitative student performance data regarding distance learning is synthesized in 6 scientific publications using (structured questionnaire, FSHM (Fellowship in Secondary Hospital Medicine) program, online questionnaire, distance education perception scale). There were 3 studies which found that modular control of the distance learning has contributed to academic success of students and the effectiveness of exams (69.5%), compared to the paper version (68.81%). The other 3 studies describe the effectiveness of distance learning as a good way to gain theoretical knowledge. The following 5 studies describe students' preferences for distance learning, namely: flexibility of study time, improved quality of education, student motivation for online learning, safety of avoiding contact, and potential impact of infection. Among the shortcomings identified there are technological problems (poor Internet connection), a sense of isolation, increasing training load, technostress. The analysis of teachers’ feedback on the distance learning was performed in 4 studies. 61.1% of teachers are satisfied with online education, but 95.8% of researchers preferred face-to-face classes. According to this review, distance learning demonstrated a better impact on medical students' learning outcomes, ease of monitoring, flexibility of time and place, compared to traditional learning. In the presented results, the influencing factors of the selected studies are described, the positive and negative aspects of each research design are evaluated.

Important Evaluation Model of University Mathematics Curriculum

Mathematics is the foundation of all subjects, and every great progress of human beings has the strong support of mathematics. Mathematics has shown an extremely important role in university education and the importance of mathematics is increasing. With the differentiation of objective environment, the personalization of individual disciplines, the subjective learning attitude, and the interactive influence between various disciplines, a lot of precise statistical requirements are put forward. In this context, the study of the importance model of university mathematics curriculum is helpful for college students to better study and plan, and also provides a reference for other industries such as education. This paper combines the performance data of mathematics students of each major and each class. It also combines the relevant characteristics of college students themselves, and puts forward suggestions on the connection of middle school and university mathematics and the subsequent study.

Leveraging Big Data for Educational Improvement: Opportunities, Challenges, and Future Directions

Big Data analysis in education has the potential to enhance student academic performance by providing personalized learning experiences. By collecting and analyzing student behaviour and performance data, educators can identify areas where students are struggling and develop targeted interventions to help them improve their learning outcomes. For instance, educators can use big data to identify each student’s learning style and tailor their teaching methodology to suit their needs. This personalized approach can enhance student engagement and motivation, leading to improved academic performance. Big Data can also be used to improve the grading system by eliminating manual grading errors and providing objective and accurate grading based on data analysis. Using automated grading systems allows educators to save time and reduce grading bias, leading to fairer and more consistent grading practices. This can also help educators identify areas where students need additional support, and provide targeted feedback to help students improve their performance. Big Data can also be used to reduce dropout rates by providing early warning indicators to identify students who are at risk of dropping out. By analyzing data on attendance, grades, and other factors, educators can identify students who may be struggling and provide targeted support to help them succeed. This can help reduce dropout rates and improve student retention rates. Overall, the use of Big Data analysis in education has numerous benefits, including enhancing student academic performance, improving grading practices, increasing student engagement and motivation, and reducing dropout rates. However, it is important to ensure that student privacy and data security are prioritized in the use of Big Data in education. By taking a responsible and ethical approach, educators and institutions can harness the power of Big Data to improve the quality of education and prepare students for success in the future