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]

A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction

Using Data Mining in Educational Administration - A Case Study on Improving School Attendance

open access articlePupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, show that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness of Data Mining is explained in migration the educational data into knowledge. The experiments goal at first to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance of Data Mining solutions in a real application