Comparison of University Students' Graphic Interpretation Skills

Graphic interpretation is as critical in physics education as problem-solving. However, we know that today's classes focus more on problem-solving. This study uses a survey to determine college students' graphic interpretation skills. The study consists of two phases. The first phase includes the development and statistical analysis of the survey. The second phase includes comparing and discussing the data resulting from the application of the developed survey. The research data were analyzed using both exploratory factor analysis and confirmatory factor analysis techniques. The survey on graphic interpretation skills, including the understanding and analysis processes, consisted of 17 items based on analysis results. The survey data were collected using purposive sampling from 113 college volunteers during the fall semester of 2022-2023 at Dokuz Eylul University in Turkey. The participants consisted of 57 geoscience students and 56 mining students. The survey results showed that the kinematic interpretation skills of mining engineering students were higher than those of geoscience students. These differences between geoscience and mining engineering students in cognitive, affective, and psychomotor behaviors were discussed

Predicting Success Study Using Students GPA Category

. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government). Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students' performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining) will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the university may recommend actions to be taken to improve the student achievement index and graduation rates

Predicting Success Study Using Students GPA Category

Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government). Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining) will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the  university may recommend actions to be taken to improve the student  achievement index and graduation rates.Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithm

Computer assisted assessment and the role it plays in educational decision-making and educational justice: a case study of one teacher training college in Zimbabwe

A ZJER research on computer - assisted assessments and educational decision-making in the Zimbabwe education system.Although the use of computers in data-driven decision making in education was initially focused on education's core business i.e. computer aided learning (CAL), educational leaders are now using this approach to transform other aspects of their operations e.g. computer-assisted assessment (CAA). The full potential of CAA has yet to be realized and its implementation within higher education can be fraught with difficulties. This paper draws on a research that was carried out in one teachers' college in Zimbabwe. The main aim was to engage with the final grading system used on the teaching practice phase ofa group of600 newly qualified teachers with a view of identifying how the computer was being used to allow humans to benefit from machine decision-making without losing the opportunity for rational thought. This was driven by a sincere conviction that better data-driven decisions in education benefit everyone, including the learners, teachers, administrators, patrons, taxpayers and the state. The researcher employed an approach commonly used in IT, which is called Data Mining. The findings seem to point to a grading system which is using a computer more as a data capture and calculation instrument without questioning the moral argument for letting the computer decide. Such a grading system has potential for loss of human autonomy and for being unfair to the subjects

The effect of student self -described learning styles within two models of teaching in an introductory data mining course

This dissertation examines the roles of learning styles and teaching methodologies within a data mining educational program designed for non-Computer Science undergraduate college students. The experimental design is framed by a discussion of the history and development of data mining and education, as well as a vision for its future.;Data mining is a relatively new discipline which has grown out of the fields of database management and data warehousing, statistics, logic, and decision sciences. Over the course of its approximately 15 year history, data mining has emerged from its genesis within the academic and commercial research and development arenas to become a widely accepted and utilized method of exploratory data analysis for management, strategic planning and decision support. Over the first several years of its development, data mining remained the province of computer scientists and professional statisticians at large corporations and research universities around the world. Beginning in about 1989, these data mining pioneers developed many of data mining\u27s standards and methodologies on large datasets using mainframe computing systems. Throughout the 1990s, as both the hardware and software tools required for the realization of data mining have become increasingly accessible, powerful and affordable, the pool of potential data miners has expanded rapidly. Today, even individuals and small businesses can exploit the power of data mining using freely acquirable open source software packages capable of running on personal computers.;During the growth and development of data mining methodologies however, little research has been dedicated specifically to the pedagogical approaches used in teaching data mining. Educational programs that have evolved have largely remained within Computer Science departments and have often targeted graduate students as an audience. This dissertation seeks to examine the possibility of successful teaching data mining concepts and techniques to a non-Computer Science undergraduate audience. The study approached this research question by delivering a lesson on the data mining topic of Association Rules to 86 participants who are representative of the target audience. These participants were randomly assigned to receive the Association Rules lesson through either a Direct Instruction or a Concept Attainment teaching approach. The students completed Kolb\u27s Learning Styles Inventory, participated in the data mining lesson, and then completed a quiz on the concepts and techniques of Association Rules. A t-test was used to determine if significant differences existed between the scores generated under the two teaching models, and an ANOVA was conducted to identify significant differences between the four learning style groups from Kolb\u27s instrument. In addition to these two statistical tests, the data were also mined using Association Rules and Decision Tree methods.;In both statistical tests, we failed to reject the null hypothesis, finding no significant differences in quiz scores between the two teaching models or among the four learning style groups. Further investigation into the differences among learning styles within teaching models however did reveal that the Assimilator learning style students who received their instruction via Direct Instruction did score significantly higher on the quiz than did their learning style counterparts who received the lesson via Concept Attainment. This finding suggests that although we cannot rely solely on one instructional approach as consistently more effective than the other, there may be instances where the correct instructional choice will positively benefit some learners with certain learning styles. The results of the data mining activities also support this assertion. Association Rules mining yielded no strong relationships between teaching models, learning styles and quiz scores, but Decision Tree mining did reveal a similar pattern of higher scores earned by Assimilator learners within Direct Instruction.;The findings of this study show that effectively teaching data mining concepts to undergraduate non-Computer Science students will not be as simple as choosing one teaching methodology over another or targeting a specific learning style group. Rather, designing instructional activities using teaching methodologies which closely align with predominant learning styles in a classroom should prove more effective. Perhaps the most significant finding of the study is that elementary data mining concepts and techniques can be effectively taught to the target audience. Finally, we recommend that additional teaching methodologies and perhaps different learning style assessments could be tested in the same way as those selected for this study

Student Mining Using K-Means Clustering: A Basis for Improving Higher Education Marketing Strategies

This study aims to enhance marketing strategies in higher education institutions by applying data mining techniques, specifically K-means clustering. The research focuses on Mindanao State University - Lanao del Norte Agricultural College (MSU-LNAC), a tertiary institution in Northern Mindanao, Philippines, with the objective of increasing enrollment. The study utilizes the K-means algorithm to group attributes into different clusters. The clustering analysis provides valuable insights into the characteristics and preferences of the surveyed student population. Based on the findings, recommendations are presented to guide targeted marketing efforts, such as geographic targeting, collaborations with senior high schools, financial assistance programs, and the development of marketing campaigns that emphasize the institution's strengths and advantages. By implementing these recommendations, MSU-LNAC can enhance its recruitment and marketing strategies to attract and retain students effectively

Understanding Learners\u27 Motivation through Machine Learning Analysis on Reflection Writing

Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students\u27 performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. intrinsic). These results will be compared against other measures of motivation, including student self-report, faculty observation, and externally validated surveys. As part of a longer-term study, this pilot work sheds light on the key question for student success and retention: how does student motivation evolve through the 3rd and 4th years in college? The purpose of this research project is to gain insights into learners’ motivation levels and how it evolves during the last two years in college, as well as to extend current Educational Data Mining research and Machine Learning analysis described in the literature. It is significant on two fronts: 1) we will extend the ability of ML in analyzing reflective written artifacts to explore student physiological and emotional development; 2) the longitudinal study will help monitor the progressive change of motivation in college students in a PBL environment. Preliminary results from an initial pilot study are promising. By analyzing written reflection journal entries from previous students, the ML algorithm has differentiated keywords into three student motivation levels: “high”, “neutral” and “low”. Using supervised classes, for example, the ML algorithm differentiated words in the highly motivated student text such as “team” and “learning”, while the text coded as low motivation included “use”, “pushed” and “nothing”. For our future research, we aim to create a dictionary that identifies words/phrases related to positive/negative motivation. We will extend the pilot study to a longitudinal evaluation of student motivation over four semesters of engineering education as well as prediction of student success in a PBL environment

Freeport education diplomacy with AMINEF in promoting gender equality specifically for Papuan women

Freeport Indonesia's mission is to be committed to being creative in processing the transformation of natural resources to provide prosperity and contribute to aspects of sustainable development through various mining practices by prioritizing the welfare and peace of employees and communities, human resource development, social and environmental responsibility, as well as safety and welfare in the operating area. The paper aims at identifying the Education Diplomacy of PT. Freeport Indonesia with AMINEF in improving gender equality in Papua and to analyze the barriers faced by women especially Papuan Women in taking part of the AMINEF scholarship specifically the Community College Initiative Program. The method used in this research is descriptive qualitative, by selecting interview techniques and literature study in data collection, as well as using the theory of Education Diplomacy, Multinational Corporation, and Gender Equality as theoretical framework on the whole process of implementing the AMINEF program in facing obstacles in achieving gender equality in Papua. The result of the research reveals that there has been no gender equality policy among three actors: the government, Freeport, and AMINEF which leads to low capability of developing Papuan people. Freeport itself merely concentrates on its production without paying attention on gender equality

METODE KLASIFIKASI DATA MINING ALGORITMA C4.5 DAN PART UNTUK PREDIKSI WAKTU KELULUSAN MAHASISWA DI UNIVERSITAS DARWAN ALI

College is one of the most influental education aspect for a nation’s improvement. The quality of a college is important to support and explore student potential. The quality of a college helps student to prepare theirselves in working world. One of the qualities of a college can be seen from the punctuality of graduating time for students. It is become important for a college to find out the factors that influence the punctuality of graduating time for students. Darwan Ali University is one of university located in Sampit, Central Kalimantan. Based on their Information System Management, in 2011, there are 707 new students. In 2015 only 290 students passed. It shows that only 41% of students graduate on time. The source of this research data comes from Management Information System of Darwan Ali University. The purpose of this research is to find the rules which affects the accuracy of student graduation. The data used in this study include departement of study programs, the GPA from first to fourth semester, and gender of students. This study uses two algorithms, namely the C4.5 and PART algorithms. The researcher also found that the C4.5 algorithm has better accuracy than PART, with an accuracy level of 83.004 %. Keywords: Classification, data mining, C4.5, PART