scholarly journals Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

2018 ◽  
Vol 7 (2.29) ◽  
pp. 398 ◽  
Author(s):  
Nadirah Mohamad ◽  
Nor Bahiah Ahmad ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Data Mining ◽  
Student Retention ◽  
Online Courses ◽  
Predictive Analytics ◽  
Promising Result ◽  
Intervention Programs ◽  
Retention Rates ◽  
Learning Program ◽  
Interaction Factor ◽  
Using Data

Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.  

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

2018 ◽  
Vol 7 (2.29) ◽  
pp. 1113
Author(s):  
Nadirah Mohamad ◽  
Nor Bahiah Ahmad ◽  
Dayang Norhayati Abang Jawawi
Keyword(s):  
Data Mining ◽  
Student Retention ◽  
Online Courses ◽  
Predictive Analytics ◽  
Promising Result ◽  
Intervention Programs ◽  
Retention Rates ◽  
Learning Program ◽  
Interaction Factor ◽  
Using Data

Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

Classroom Preferences

2010 ◽  
pp. 1268-1279
Author(s):  
Chuleeporn Changchit ◽  
Tim Klaus
Keyword(s):  
Student Retention ◽  
Online Instruction ◽  
Online Courses ◽  
Retention Rates ◽  
Online Course ◽  
Online Programs ◽  
Traditional Courses ◽  
Course Offerings

Advances in technology have enabled instructors to design online courses that better meet the needs of students. Online courses generally are adaptations of traditional courses; some courses are more suitable for such online instruction. As the trend of online course offerings continues, universities must understand factors that lead to students’ preferences since online courses can be costly to develop and implement and inappropriate online coursescan lead to lower student retention rates. This study focuses on students’ perceptions of online courses. The results identify issues that affect students’ perceptions and this study concludes by suggesting ways for universities to design online programs that better suit the desires of students.

Factors Encouraging or Discouraging Students from Taking Online Classes

2010 ◽  
pp. 55-67 ◽  
Author(s):  
Chuleeporn Changchit ◽  
Tim Klaus
Keyword(s):  
Student Retention ◽  
Online Courses ◽  
Communication Technologies ◽  
Retention Rates ◽  
Online Classes ◽  
Continuing Higher Education ◽  
Online Programs ◽  
Individual Variations ◽  
Traditional Courses ◽  
Use Of The Internet

Advances in communication technologies, such as widespread use of the Internet, have opened new avenues for continuing higher education. These advances have allowed educators to provide for and satisfy individual variations in learning. Generally, online courses are adaptations of traditional courses; some courses are more suitable than others for such online instruction. As the trend to offer online courses continues, understanding the factors that lead to students’ preference can be useful. Online courses can be costly to develop and to implement, and inappropriately designating courses for online participation can lead to lower student retention rates. This study focuses on students’ perceptions of online courses. The results identify issues that affect students’ perceptions, and this study concludes by suggesting ways for universities to design online programs that better suit the desires of students.

The PAR Framework Proof of Concept: Initial Findings from a Multi-Institutional Analysis of Federated Postsecondary Data

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Philip Ice ◽  
Sebastián Díaz ◽  
Karen Swan ◽  
Melissa Burgess ◽  
Mike Sharkey ◽  
...  
Keyword(s):  
Online Learning ◽  
Student Retention ◽  
Predictive Analytics ◽  
Postsecondary Institutions ◽  
Learning Program ◽  
Online Learners ◽  
Face To Face ◽  
Reporting Framework ◽  
Determining Factors ◽  
Face To Face Instruction

Despite high enrollment numbers, postsecondary completion rates have generally remained unchanged for the past 30 years and half of these students do not attain a degree within six years of initial enrollment. Although online learning has provided students with a convenient alternative to face-to-face instruction, there remain significant questions regarding online learning program quality, particularly when considering patterns of student retention and progression. By aggregating student and course data into one dataset, six postsecondary institutions worked together toward determining factors that contribute to retention, progression, and completion of online learners with specific purposes: (1) to reach consensus on a common set of variables among the six institutions that inform student retention, progression and completion; (2) to explore advantages and/or disadvantages of particular statistical and methodological approaches to assessing factors related to retention, progression and completion. In the relatively short timeframe of the study, 33 convenience variables informing retention, progression, and completion were identified and defined by the six participating institutions. This initiative, named the Predictive Analytics Reporting Framework (PAR) and the initial statistical analyses utilized are described in this paper.

A predictive analytics of physicians prescription and pharmacies sales correlation using data mining

2019 ◽  
Vol 13 (3) ◽  
pp. 346-363
Author(s):  
Babak Sohrabi ◽  
Iman Raeesi Vanani ◽  
Nastaran Nikaein ◽  
Saeideh Kakavand
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pharmaceutical Industry ◽  
Predictive Analytics ◽  
Customer Relationship ◽  
Sales Managers ◽  
Content Type ◽  
Using Data ◽  
The Impact ◽  
The Relationship

Purpose In the pharmaceutical industry, marketing and sales managers often deal with massive amounts of marketing and sales data. One of their biggest concerns is to recognize the impact of actions taken on sold-out products. Data mining discovers and extracts useful patterns from such large data sets to find hidden and worthy patterns for the decision-making. This paper, too, aims to demonstrate the ability of data-mining process in improving the decision-making quality in the pharmaceutical industry. Design/methodology/approach This research is descriptive in terms of the method applied, as well as the investigation of the existing situation and the use of real data and their description. In fact, the study is quantitative and descriptive, from the point of view of its data type and method. This research is also applicable in terms of purpose. The target population of this research is the data of a pharmaceutical company in Iran. Here, the cross-industry standard process for data mining methodology was used for data mining and data modeling. Findings With the help of different data-mining techniques, the authors could examine the effect of the visit of doctors overlooking the pharmacies and the target was set for medical representatives on the pharmaceutical sales. For that matter, the authors used two types of classification rules: decision tree and neural network. After the modeling of algorithms, it was determined that the two aforementioned rules can perform the classification with high precision. The results of the tree ID3 were analyzed to identify the variables and path of this relationship. Originality/value To the best of the authors’ knowledge, this is one of the first studies to provide the real-world direct empirical evidence of “Analytics of Physicians Prescription and Pharmacies Sales Correlation Using Data Mining.” The results showed that the most influential variables of “the relationship between doctors and their visits to pharmacies,” “the length of customer relationship” and “the relationship between the sale of pharmacies and the target set for medical representatives” were “deviation from the implementation plan.” Therefore, marketing and sales managers must pay special attention to these factors while planning and targeting for representatives. The authors could focus only on a small part of this study.

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