Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)

Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

Student Performance Prediction Using Educational Data Mining Techniques

Educational sector produces data in large amount that is too voluminous and complex to understand. There is a need to efficiently filter and prioritize the data so as to deliver the relevant information to get rid of information overloading. Data mining searches through the large amount of dynamically generated data to present users with the useful and understandable patterns and trends. It has the power to use the raw data effectively which has been produced by universities, to draw the hidden patterns and the relationships among the attributes that are used in predicting the student performance, their behaviour effectively. In this paper the data mining techniques have been briefly described. The literature review of educational data mining is also done. This paper, implements data mining techniques such as Naive bayes and Support vector machine to predict the student performance

Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

Implementation of Lead Time Improvement in the Cutting Production Process using Clustering Data Mining and Lean Manufacturing

In steel companies, there are some processes in the production such as cutting, machining and heat treatment. The production process begins with cutting process and then machining process and the last one is the heat treatment process. It becomes a background that gives the challenge and needs continuous improvement on all aspects, mainly in the cutting process. The research is conducted at one of steel companies located in Pulogadung Industrial Estate. The purpose of the research is to know the factors which cause process lead time cannot be achieved. The methods used are clustering data mining and lean manufacturing. Clustering method can be used to focus on big data and find out clusters or the same pattern. Those clusters are processed with Weka software and using K-means algorithm. Improvement ideas will be implemented to the formed clusters using lean manufacturing such as Single Minute Exchange of Dies (SMED) which have been mapped through value stream mapping, 5S, and Kanban beforehand. The materials’ dimension on the production process is affecting the cutting process lead time. The thicker material diameters will need a longer time to process. With the methods used, lead time of cutting process increased from 3449 minutes to 2165 minutes (3 days to be 2 days). Meanwhile if the “SMED” activities are implemented, the cutting process lead time increased from 187 minutes to 136 minutes. Keywords: Clustering; SMED; Lean Manufacturing; Process Lead Time; Value Stream Mappin

Educational Data Clustering Menggunakan K-Means pada Seleksi Penerimaan Peserta Didik Baru Madrasah Aliyah Negeri Unggulan

The National Students admissions (SNPDB) for Madrasah Aliyah is managed by the Directorate of Madrasah Curriculum, Facilities, Institutions and Student Affairs. It is essential for the Directorate and Madrasah to explore patterns and knowledge from admission data in formulating policies and programs from to MAN. Educational Data Clustering (EDC) is a data mining method that is implemented in the education area. K-means is applied to group students based on the results of learning potential and academic potential tests that will be used for development program and student admission policies at MAN-IC. The best results from the experiments tested with Silhouette dividing the data into 2 clusters are excellent and good. The Silhouete value indicates the cluster structure in the medium predicate.. The results present the distribution of clusters in 23 MAN-IC, distribution of personality profiles of prospective students, as well as recommendations for conducting tests in Madrasah.Seleksi Nasional Peserta Didik Baru (SNPDB) Madrasah Aliyah Negeri Unggulan dikelola oleh Direktorat Kurikulum, Sarana, Kelembagaan dan Kesiswaan Madrasah. Menjadi penting bagi Direktorat dan Madrasah untuk menggali pola dan pengetahuan dari data seleksi  dalam penyusunan kebijakan dan program pada MAN Unggulan.. MAN Insan Cendekia (MAN-IC) merupakan madrasah aliyah negeri unggulan yang paling diminati. Educational Data Clustering (EDC) merupakan metode data mining yang dimplementasikan dibidang pendidikan. K-means diterapkan untuk mengelompokkan Siswa berdasarkan hasil tes potensi belajar dan potensi akademik yang akan digunakan untuk penyusunan program dan kebijakan seleksi siswa pada MAN-IC. Hasil terbaik dari ekperimen yang diuji dengan Silhouette membagi data menjadi 2 klaster sangat baik dan baik.  Nilai Silhouete menunjukkan struktur klaster pada predikat medium. Hasil pengelompokan menyajikan sebaran klaster di 23 MAN-IC, sebaran profil kepribadian dari calon peserta didik, serta rekomendasi untuk pelaksanaan tes di Madrasah

Gender voice classification with huge accuracy rate

Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script

Educational Data Mining To Improve The Academic Performance in Higher Education

Globalization and Innovation are mainly consider the great interest public sector and private business in the world especially in the higher education institutions. Educational Data Mining is mainly one of the business processes nowadays that attempt to bring the global innovation through improving and enhancing their processes and procedures to fulfill all the requirements and needs of the students as well as the institutions. The Educational Data Mining considered mostly concern with any research concerning the applications of the data mining and developing innovative techniques for data mining (DM) in the educational sector. This study mainly combined the use of the powerful online E-learning management system (Moodle) with data mining tools to improve the performance and effectiveness of the learning and teaching manners by using the innovative daily data that collected from the educational institutions

A Case Study of Using Big Data Processing in Education: Method of Matching Members by Optimizing Collaborative Learning Environment

The purpose of this paper is to optimize the combination of members for collaborative learning that utilized learning management system (LMS), a kind of social media. It is considered that there is a problem of this combinatorial optimization because of various discrete elements in education and it is difficult to find exact solution. Then, we have solved this problem by the method of approximate solution in nursing science class with big data processing, for instance, individual traits, learning outcome, and so on. The result shows continuously learning effects. We will report in this fundamental research how to gather learners’ various data and optimize matching members of team by local searching. It might be explained how to solve problems of combinatorial optimization by AI

Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community