Application model of k-means clustering: insights into promotion strategy of vocational high school

Admission process is required in promoting the strategy to achieve the target. Through determining the strategic promotion, minimizing the cost in the marketing process could be reached with determining the appropriate promotion strategy. Data mining techniques in this initiative were applied to achieve in determining the promotional strategy. Using Clustering K-Means algorithm, it is one method of non-hierarchical clustering data in classifying student data into multiple clusters based on similarity of the data, so that student data that have the same characteristics are grouped in one cluster and that have different characteristics grouped in another cluster. Implementation using Weka Software is used to help find accurate values where the attributes include home address, school of origin, transportation, and reasons for choosing a school. The cluster of students was classified into five clusters in the following: the first cluster 22 students, the second cluster 10 students, the third cluster 10 students, the fourth cluster a total of 33 students, and the fifth cluster 25 students. The pattern of this result is supposed to contribute to enhance the significant data mining to support the strategic promotion in gaining new prospective students

INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score. Article visualizations

Data Mining for Studying the Impact of Reflection on Learning

Title: Data Mining for Studying the Impact of Reflection on Learning Keywords: educational data mining, Reflect, learning behaviour, impact Abstract On-line Web-based education learning systems generate a large amount of students' log data and profiles that could be useful for educators and students. Hence, data mining techniques that enable the extraction of hidden and potentially useful information in educational databases have been employed to explore educational data. A new promising area of research called educational data mining (EDM) has emerged. Reflect is a Web-based learning system that supports learning by reflection. Reflection is a process in which individuals explore their experiences in order to gain new understanding and appreciation, and research suggests that reflection improves learning. The Reflect system has been used at the University of Sydney’s School of Information Technology for several years as a source of learning and practice in addition to the classroom teaching. Using the data from a system that promotes reflection for learning (such as the Reflect system), this thesis focuses on the investigation of how reflection helps students in their learning. The main objective is to study students' learning behaviour associated with positive and negative outcomes (in exams) by utilising data mining techniques to search for previously unknown, potentially useful hidden information in the database. The approach in this study was, first, to explore the data by means of statistical analyses. Then, popular data mining algorithms such as the K-means and J48 algorithms were utilised to cluster and classify students according to their learning behaviours in using Reflect. The Apriori algorithm was also employed to find associations among the data attributes that lead to success. We were able to group and classify students according to their activities in the Reflect system, and identified some activities associated with student performance and learning outcomes (high, moderate or low exam marks). We concluded that the approach resulted in the identification of some learning behaviours that have important impacts on student performance

Computational Prediction Algorithms and Tools Used in Educational Data Mining: A Review

 في الأيام الأخيرة ، ظهرت مجموعة متنوعة من الأدوات لأغراض أداء التنقيب عن البيانات التعليمية (EDM). تظهر أنظمة التعليم الحالية أن هناك عدة عوامل تؤثر على أداء الطلاب. أولاً وقبل كل شيء ، يحتاج الطلاب إلى الدافع من أجل التعلم وهذا الدافع  يؤدي إلى نجاحهم. يعد التنبؤ بأداء الطلاب مجالًا مهمًا للبحث في استخراج البيانات التعليمية ، لا سيما من خلال تطبيق تقنيات التنقيب عن البيانات المختلفة. تركز غالبية أبحاث EDM على خوارزميات التنبؤ. يقدم العمل الحالي مراجعة لخوارزميات التنقيب في البيانات والأدوات التي تم تبنيها في  EDM . كما يوفر نظرة ثاقبة للخوارزميات وأدوات التنقيب عن البيانات القوية الأكثر استخدامًا في التنبؤ بأداء الطلاب . سيكون هذا مفيدًا بشكل أساسي للمعلمين والمرشدين والمؤسسات ، مما يزيد من مستويات الطلاب الدراسية.Abstract In recent days, a wide variety of tools have appeared for performing educational data mining (EDM) . The current education systems show that there are several factors affecting students’ performances. First and foremost, students need motivation in order to learn  and this  motivation results into their success.  The prediction of student performances is an important field of research in Educational Data Mining, particularly through the application of different data mining techniques. The majority of EDM research focuses on prediction algorithms. The current work presents a review of the data mining predicting algorithms and tools that have been adopted in EDM. It also provides insight into the algorithms and powerful data mining tools that most widely used in student performance prediction. This will mainly be of use for  educators, instructors and institutions, increasing the students’ levels of study

Selección de tutores académicos en la educación superior usando árboles de decisión

ABSTRACTIn this paper, we present a method for the tutoring process in order to improve academic tutoring in higher education. The method includes identifying the main skills of tutors in an automated manner using decision trees, one of the most used algorithms in the machine learning community for solving several real-world problems with high accuracy. In our study, the decision tree algorithm was able to identify those skills and personal affinities between students and tutors. Experiments were carried out using a data set of 277 students and 19 tutors, which were selected by random sampling and voluntary participation, respectively. Preliminary results show that the most important attributes for tutors are communication, self-direction and digital skills. At the same time, we introduce a tutoring process where the tutor assignment is based on these attributes, assuming that it can help to strengthen the student's skills demanded by today's society. In the same way, the decision tree obtained can be used to create cluster of tutors and clusters of students based on their personal abilities and affinities using other machine learning algorithms. The application of the suggested tutoring process could set the tone to see the tutoring process individually without linking it to processes of academic performance or school dropout.RESUMEN  En este documento se presenta un método para mejorar el proceso de tutoría académica en la educación superior. El método incluye a identificación de las habilidades principales de los tutores de forma automática utilizando el algoritmo árboles de decisión, uno de los algoritmos más utilizados en la comunidad de aprendizaje automático para resolver problemas del mundo real con gran precisión. En el estudio, el algoritmo arboles de decisión fue capaz de identificar las habilidades y afinidades entre estudiantes y tutores. Los experimentos se llevaron a cabo utilizando un conjunto de datos de 277 estudiantes y 19 tutores, mismos que fueron seleccionados por muestreo aleatorio simple y participación voluntaria en el caso de los tutores. Los resultados preliminares muestran que los atributos más importantes para los tutores son la comunicación, la autodirección y las habilidades digitales. Al mismo tiempo, se presenta un proceso de tutoría en el que la asignación del tutor se basa en estos atributos, asumiendo que puede ayudar a fortalecer las habilidades de los estudiantes que demanda la sociedad actual. De la misma forma, el árbol de decisión obtenido se puede utilizar para agrupar a tutores y estudiantes basados en sus habilidades y afinidades personales utilizando otros algoritmos de aprendizaje automático. La aplicación del proceso de tutoría sugerido podría dar la pauta para ver el proceso de tutoría de manera individual sin vincularla a procesos de desempeño académico o deserción escolar.ABSTRACTIn this paper, we present a method for the tutoring process in order to improve academic tutoring in higher education. The method includes identifying the main skills of tutors in an automated manner using decision trees, one of the most used algorithms in the machine learning community for solving several real-world problems with high accuracy. In our study, the decision tree algorithm was able to identify those skills and personal affinities between students and tutors. Experiments were carried out using a data set of 277 students and 19 tutors, which were selected by random sampling and voluntary participation, respectively. Preliminary results show that the most important attributes for tutors are communication, self-direction and digital skills. At the same time, we introduce a tutoring process where the tutor assignment is based on these attributes, assuming that it can help to strengthen the student's skills demanded by today's society. In the same way, the decision tree obtained can be used to create cluster of tutors and clusters of students based on their personal abilities and affinities using other machine learning algorithms. The application of the suggested tutoring process could set the tone to see the tutoring process individually without linking it to processes of academic performance or school dropout

A Predictive Model using Machine Learning Algorithm in Identifying Student's Probability on Passing Semestral Course

Purpose: The used of an integrated academic information system in higher education has been proven in improving quality education which results to generates enormous data that can be used to discover new knowledge through data mining concepts, techniques, and machine learning algorithm. This study aims to determine a predictive model to learn students' probability to pass their courses taken at the earliest stage of the semester. Method: To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveying knowledge and uplifting student's academic performance, the proponent applies and strictly followed the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This study employs classification for data mining techniques, and decision tree for algorithm. Results: With the utilization of the newly discovered predictive model, the prediction of students' probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score, which shows that the model used in the prediction is reliable, accurate, and recommendable. Conclusion: Considering the indicators and the results, it can be noted that the prediction model used in this study is highly acceptable. The data mining techniques provides effective and efficient innovative tools in analyzing and predicting student performances. The model used in this study will greatly affect the way educators understand and identify the weakness of their students in the class, the way they improved the effectiveness of their learning processes gearing to their students, bring down academic failure rates, and help institution administrators modify their learning system outcomes. Recommendations: Full automation of prediction results accessible by the students, faculty, and institution administrators for fast management decision making should take place. Further study for the inclusion of some student`s demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed

Learning analyticsin a virtual learning environment : the challenge of mapping socio-affective scenarios

Virtual courses are increasingly being offered in Brazil, making it imperative to develop technological resources and research to help in the teaching and learning processes in this modality. One approach is to analyze student's socio-affective profile in Virtual Learning Environments (VLE). The co-operative learning network (ROODA) VLE has two features called the Social Map (SM) and Affective Map (AM), which can both contribute to the visualization of data regarding social interaction indicators and students' moods in the environment. The SM presents the social relations formed through indicators, which are the absence; collaboration; the distance from the class; evasion; informal groups and popularity, enabling the identification of the participating subjects in the form of sociograms. The AM identifies students' moods graphically through indicators, which are excitement, discouragement, satisfaction, and dissatisfaction. Thus, this article aims to map the possible recurrent socio-affective scenarios in a VLE using Learning Analytics (LA). LA is defined as measurement, collection, analysis, and reporting of data about students and their contexts to understand as well as optimize learning and the environments in which it occurs. It can also contribute to the understanding of student's learning profile, based on social and affective aspects, thus allowing the teacher to develop pedagogical strategies consistent with the needs of each subject. The importance of integrating the possible social and affective scenarios was verified using LA, making it possible to deepen the comprehension of the subjective and qualitative questions regarding the students' interactions in the VLE. In this study, the scenarios are understood as the intersection between the Affective Map and Social Map indicators identified in a VLE. It has both a qualitative and quantitative approach. The choice is qualitatively justified because the research object involves social and affective phenomena that were subjectively expressed in texts and social interactions manifested in the ROODA VLE. It is quantitatively justified by the need to measure the mapping of socio-affective indicators through social parameters and moods applying LA. The subjects were undergraduate students who participated in distance learning courses at a Brazilian public university that used the ROODA VLE in the second semester of 2019. Data were collected from social and affective maps to identify if there was a relationship between them. As a result, based on the existing indicators of social interactions and moods, the socio-affective indicators were created using LA in order to analyze the students’ behavior in relation to the forms of interaction and communication that occur in the ROODA VLE

Data-driven misconception discovery in constraint-based intelligent tutoring systems

Students often have misconceptions in the domain they are studying. Misconception identification is a difficult task but allows teachers to create strategies to appropriately address misconceptions held by students. This project investigates a data-driven technique to discover students' misconceptions in interactions with constraint-based Intelligent Tutoring Systems(ITSs). This analysis has not previously been done. EER-Tutor is one such constraint-based ITS, which teaches conceptual database design using Enhanced Entity-Relationship (EER) data modelling. As with any ITS, a lot of data about each student's interaction within EER-Tutor are available: as individual student models, containing constraint histories, and logs, containing detailed information about each student action. This work can be extended to other ITSs and their relevant domains