Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

ADHD and the Nature of Self-Control

https://cyberleninka.org/article/n/566686.pdf

A Review on Predicting Student's Performance Using Data Mining Techniques

The flipped classroom is a well-recognized learning mode that enables effective practice and interactions among teachers and students in the class by switching the in-class instructional time and out-of-class practicing time. However, owing to their lack of self-regulated competence, most students might fail to browse and comprehend the instructional materials out of class by themselves. In this paper, a self-regulated flipped classroom approach is proposed to help students schedule their out-of-class time to effectively read and comprehend the learning content before class, such that they are capable of interacting with their peers and teachers in class for in-depth discussions. In order to evaluate the effectiveness of the proposed approach, a quasi-experimental design was employed in an elementary school Mathematics course. The experimental group students learned with the self-regulated flipped classroom approach, while the control group students learned with the conventional flipped classroom approach. The study was conducted using a quantitative approach. The instruments used were a performance test, and questionnaires of self-efficacy and self-regulation. The experimental results indicated that the post-test score of the experimental group was significantly higher than that of the control group. It was also found that the higher self-regulation students showed significantly different learning achievements when learning with different approaches, while there was no significant difference between lower self-regulation students with the different learning approaches. Moreover, the experimental group showed significantly higher self-efficacy than the control group. In addition, the learning log analysis results further showed that, conforming to the objective of the self-regulated strategy, the students would determine the goals for the next learning phase based on their current performance. To sum up, the findings of this study indicate that integrating the self-regulated strategy into flipped learning can improve students' self-efficacy as well as their strategies of planning and using study time, and hence they can learn effectively and have better learning achievements. A self-regulated flipped classroom approach was proposed.The approach helped students effectively schedule their out-of-class time.An experiment was conducted in an elementary school math course.The approach improved the students' learning achievement, self-efficacy and self-regulation.The approach helped the students determine learning goals and performance.

A self-regulated flipped classroom approach to improving students' learning performance in a mathematics course

The use of Learning Management Systems has grown exponentially in the last several years and has come to have a strong effect on the teaching-learning process, particularly in higher education. The present study intends to examine students' asynchronous learning processes via an Educational Data Mining approach using data extracted from the Moodle logs of students who were grouped according to similar behaviors regarding effort, time spent working, and procrastination. The behaviors were then matched with different levels of achievement.First, the different patterns of students' involvement in the learning process in a Learning Management System were clustered. Second, the different variables selected from the Moodle records were studied to see if they were equally suitable for the configuration of student clusters. Third, the relationships between those patterns to students' final marks were examined.After analyzing the log data gathered from a Moodle 2.0 course in which 140 undergraduate students were enrolled, four different patterns of learning with different final marks were found. Additional results showed that there are variables more related to achievement and more suitable to group the students on the basis of which the different groups were characterized, namely, two Task Oriented Groups (socially or individually focused) and two Non Task Oriented Groups (procrastinators or non-procrastinators). These results have implications in the design of interventions for improving students' learning processes and achievement in LMSs. This work tends to broaden the knowledge about the learning process in LMSs from an EDM approach.We examine students' interactions with Moodle and their relationship with achievement.We studied the different interaction patterns of students with the LMS in an authentic learning context.We identify the variables from the Moodle's records more suitable to difference de students.We find out patterns of learning related to different achievement.

Students' LMS interaction patterns and their relationship with achievement

Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues It describes the data, tools, techniques and models used in EPM In addition, the main work in this area is described and grouped by educational application domains

A survey on educational process mining

This research was supported by the Spanish Ministry of Education and Science (Projects: EDU2010-16231 and PSI-2011-23395).

Teachers’ Feedback on Homework, Homework-Related Behaviors, and Academic Achievement

This paper tests the efficacy of an intervention program in virtual format intended to train studying and self-regulation strategies in university students. The aim of this intervention is to promote a series of strategies which allow students to manage their learning processes in a more proficient and autonomous way. The program has been developed in Moodle format and hosted by the Virtual Campus of the University of Oviedo. The present study had a semi-experimental design, included an experimental group (n=167) and a control one (n=206), and used pretest and posttest measures (selfregulated learning strategies' declarative knowledge, self-regulated learning macro-strategy planningexecution-assessment, self-regulated learning strategies on text, surface and deep learning approaches, and academic achievement). Data suggest that the students enrolled in the training program, comparing with students in the control group, showed a significant improvement in their declarative knowledge, general and on text use of learning strategies, increased their deep approach to learning, decreased their use of a surface approach and, in what concerns to academic achievement, statistically significant differences have been found in favour of the experimental group.

Implementation of training programs in self-regulated learning strategies in Moodle format: Results of a experience in higher education

In this paper, we propose to use clustering to improve educational process mining. We want to improve both the performance and comprehensibility of the models obtained. We have used data from 84 undergraduate students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle's usage summary and/or the students' final marks in the course. Then, we propose to use data from Moodle's logs about each cluster/group of students separately in order to be able to obtain more specific and accurate models of students' behaviour. The results show that the fitness of the specific models is greater than the general model obtained using all the data, and the comprehensibility of the models can be also improved in some cases.

Rebeca Cerezo

Papers

Students' LMS interaction patterns and their relationship with achievement

A survey on educational process mining

Teachers’ Feedback on Homework, Homework-Related Behaviors, and Academic Achievement

Implementation of training programs in self-regulated learning strategies in Moodle format: Results of a experience in higher education

Clustering for improving educational process mining