will my students finish my online course?...will my students finish my online course? dr. benilda...

Will my Students Finish My Online Course?

Dr. Benilda Eleonor V. Comendador PUP-OUS IODE Academic Program Head

Program Chair, Master in Information Technology

Manila Hotel April 26, 2018

I. Introduction

III. Results and Discussions

IV. Conclusion

II. The Developed eCLADSS

V. Future Works

OUTLINE

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I. Introduction

Mobile Learning to Ubiquitous Learning

1.0 Definition of Technical Writing/Writer

2.0 Differences between Technical Writing and Creative Writing

3.0 Fundamentals of a Technical Writer

4.0 Components of a Technical Documentation5.0 Methodologies in Technical Document

OutlineUBIQUITOUS TOOL FOR EDUCATIONresearch on :

➢ e-Learning technologies; ➢ knowledge discovery in

databases/data mining ; and ➢ learning analytics

getting popular in the academe because of its potential to improve services in the education domain.





OutlineUBIQUITOUS TOOL FOR EDUCATION

➢ These innovations can provide ubiquitous tool and powerful platform that may be used for higher educational institutions (Kumar, 2011 and Bayyou, 2013).

How EDM & Learning Analytics can help

Analytics can help colleges:

improve their services

increase student retention

increase student Grade

Why ? would colleges want to do this

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“THE COUNTRY’S 1ST POLYTECHNIC U” A Community Cloud-Based Course

Management System Using Platform as a Service (PaaS) Model Comendador and

Guillo ICCRD February 28, 2014

8

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Challenge: Massive data coming from the eLearning software (i.e., assignment, quiz, discussion forum and chat)

Goal: develop tools and methods which can support teachers and students to take action based on the evaluation of Educational Data.

Key Challenges in Integrating ICTs in Education

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Challenge: high attrition rate in Distance Education (DE) schools of which learners are predominantly full time employee part-time students

Goal: to develop an eLearning platform software that will provide personal and spontaneous learning


Challenge: limited research has been conducted on methods to improve the success and retention of DE students.

Goal: to develop an eCLADSS that uses f e a t u r e s e l e c t i o n a n d classification techniques for LMS to improve online teaching and learning activities


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Related Works

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Project Strategy Challenges

Development of College Completion Model Based on K-means Clustering Algorithm

(Paz, Gerardo, and Tanguilig , 2014)

model was based on high school general weighted average, college entrance test, gender, scholarship grant, grade point average in first year first and second semester

N e e d a d d i t i o n a l predictor variables that may have effect on the retention and college completion of students

The inclusion of the records of currently enrol led students i s highly recommended to monitor their progression and for early intervention for those who may be considered at-risk.

Related Works

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Project Strategy

Data Mining: A Prediction For Performance Improvement In Online Learning Systems

(Jailia & Tyagi ,2013 )

➢ showed how using data mining techniques can help discovering pedagogically r e l e v a n t k n o w l e d g e contained in databases obtained from Web-based educat ional systems or Online Learning Systems

Related Works

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Project Strategy

Educational Data Mining System For Advising Higher Education Student

(Nagy, Aly, & Hegazy, 2013)

➢ proposed a Student Advisory Framework that uti l izes classification and clustering to build an intelligent system

➢p r o v i d e p i e c e s o f

consultations to a first year university student to pursue certain education track where he/she will likely to succeed in.

Related Works

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Project Strategy

Mining educational data for predicting higher secondary school student’s grade using ID3 algorithm

(Devi, Deepa & Kalaiarasi, 2015)

➢ developed a system that will predict students’ higher secondary grades based on academic and personal details of the students.

➢➢ used ID3 decision tree

algorithm

Related Works





✓most of the methods applied have included educational data mining using:

Neural Networks

Regression Analysis

Discriminant Analysis

Random Forest

Decision Trees

EDUCATIONAL DATA MINING





OutlineEDUCATIONAL DATA MINING

➢ missing attribute values (noisy data) are usual scenario in mining data, either through errors made when the values were recorded or because they were considered irrelevant to the specific case.

(Patidar and Tiwari,2013)





OutlineEDUCATIONAL DATA MINING

(Patidar and Tiwari,2013)

noisy data should be properly handled because it lead to :

(1)loss of efficiency;

(2)complications in handling and analyzing the data;

(3)bias resulting from differences between missing and complete data.

Motivation

➢Much of the work found in literature and surveyed has utilized enrollment data on traditional face to face classes on courses offered in secondary and tertiary education for academic analytics.

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This study focused on providing a ➢ Decision Support System (DSS) that

integrates Multiple Linear Regression (MLR) and J48

classification models of data mining t o a n a l y z e a n d p r e d i c t t h e performance of distance students scientifically.

Motivation

Problem Recognition

Data Collection and Pre-

processing (Data Mining)

Analysis and Visualization

Action

Warehouse Model DB

Decision Support System Model Based on Learning Analytics

By Acebedo and Martin (2015)

Framework of the Study

Prepare Data to

Discover Knowledge

University LMS

Data Preprocessing

(Feature Selection Techniques: CH,IG,GR)

Student Performance

Prediction Model

Data Mining Techniques

(RepTree, CART, J48 and MLR )

ExtractedData

Performance of the Classifiers

1

Performance of the Classifiers2

Model Database

Test &

Implement the

Predictive Model

J48 Classifier ModelMultipile Linear Regression Model

J48 Decision Tree Algorithm And Multiple

Linear Regression

J48 Classifier ModelMultipile Linear Regression Model

eCourse Learning Analytics

Decision Support Sytem

(eCLADSS)

3

Predictors to Student Online

Performance

Student Performance(Variable Response)

Decision Support System Concept

School Administrator’s

Appropriate Intervention

Improved Student Scholastic

Performance

System Features of eCLADSS

System Features of eCLADSS

ISO/IEC 9126-based Quality

Model for Software

Evaluation

Evaluate

eCLADSS by the

Respondents

4

Evaluated eCLADSS

Statistical Test of Data (Percentage, WM, Ranking

Method)

Data Pre-Processing Phase

➢ The LMS user’s log file and other student’s related activities from the Moodle database consisting of 99,233 records were extracted.

➢The author focused on the records of the 248 students from the three (3) programs in the PUP Open University System

Data Pre-Processing Phase

Derived Attributes from the Student’s Interaction in eLearning Platform

• Log Freq• Mat Access Freq• Exam condition• Exam points• Activity points• Final Grade• Final Mark

Extracted from Academic Institutions Management

Systems (AIMS )

• Student year of birth • Gender• Department• Study Year• Type of study• Employment• Registration

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eCourse Learning Analytics Decision Support

System (eCLADSS)

eCLADSS Site Home Page

33eCLADSS Home Page

eCLADSS Home Page for LMS Users

Data Extraction

Data Extraction and Integration

Data Transformation

Merge Data Columns

UI of Features Selection

Data Discretization Process

Features Selection

eCLADSS Dashboard

Performance Prediction Interface with J48 Classification Model

Performance Prediction Interface w/ J48 Model

Dataset Selection Interface for LMS Administrator

Performance Prediction Interface Using MLR Model

Prediction from LMS Data

eCLADSS Dashboard for LMS Users

Performance Prediction for Student who will likely to Fail in the Course

Performance Prediction for Student who will likely to Pass in the Course

eCLADSS with Navigation Block for LMS Users

Interface for Online Examination

Exam. Report in Bar Chart Format

Item Analysis of the Exam. For Course

Specialist

Downloadable Result of Exam in xls or csv format

For Course Specialist

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D. Evaluation of eCLADSS

Type of Respondent

Couse Specialist 4 2% OUS Administrator 2 1% OUS Staff 0 0% Student 181 97%

Respondents

Manila 163 87.2% Quezon City 21 11.2% Lopez 0 0% Maragodon 0 0% Sablayan 0 0% Sta. Rosa 0 0% Sto. Tomas 0

Yes 130 70.7% No 54 29.3%

Less than 5 yrs 183 97.9% Less than 6-10 yrs 2 1.1% 11-15 yrs 0 0% 16-20 yrs 2 1.1% 21-25 yrs 0 0% More than 25 yrs 0 0%

Respondent’s Program

MEM 0 0% MPA 0 0% MSIT 31 16.6% MC 0 0% MSCM 7 3.7% ABBrC/BBrC 21 11.2% BSEntrep 59 31.6% PBDIT 13 7% Other 56 29.9%

FORMULA:

Weighted Mean (WM) = f (x 1 + x2 + x3 + … + xn) N

Total_Q 29 Items

Summary of the Evaluation of eCLADSS by the Respondents

Suggestions for Improvement

OtherChat/Messaging facility for parents and students

Additional module for registrar’s tasks

SMS notification to students regarding their grades

SMS notification to students regarding their grades 131 69.3%Additional module for registrar’s tasks 14 7.4%Chat/Messaging facility for parents and students 26 13.8%Other 18 9.5%

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CONCLUSION

OBJ.#1 Develop a student performance predictive model using Multiple Linear Regression (MLR) and J48 classification algorithm

Conclusion: It has been demonstrated in the study that the MLR and J48 prediction models provided a powerful educational tool that can analyze and predict the performance of the DE learners scientifically.

Recommendation: Since the score obtained from the participation in the online activities were found to be good predictors of the student performance, these areas should be given more attention as assessment tasks are concerned.

OBJ.#2 Test and implement the predictive model

Conclusion: The developed eCLADSS produced accurate J48 and MLR models which was exhibited during testing and simulation of real institutional data and displayed the same output with that of the two reliable application programs the Microsoft Excel and WEKA.

Recommendation: The developed eCLADSS may be utilized collectively with the existing university LMS to enhance the DE students’ performance.

OBJ.#3 Develop an eCLADSS to identify at risk-students for early intervention and attrition

prevention

Conclusion: eCLADSS was successful in integrating feature selection and classification techniques as it provides a good platform for generation of academic model that can be adapted by other educational institutions because of its model generation, selection procedures and user-oriented interface that provides a customizable attribute selection and facilitates flexible interactive visual exploration of the accumulated data.

OBJ.#3 Develop an eCLADSS to identify at risk-students for early intervention and attrition

prevention

Recommendation: Further research will be needed to validate the models

generated. The next research may concentrate on greater number of instances using the other feature selection techniques or other variables and may explore other data mining algorithms.

OBJ.#4 Evaluate the eCLADSS by the respondents in terms of the following: functionality, usability, reliability and portability of the output.

Conclusion: The study has shown that developed eCLADSS satisfied its implied functions and is useful, reliable and portable.

Recommendation: The school administrators should involve more faculty members in the study so that more data can be generated and analyzed.

Future Works

Decision Support System can be enhanced by integrating it to other data derived for the improvement of students’ services such as the data produced in students’ admission, guidance, and other online services of the University.

[1] Comendador, B.E.V., Sison, A.M. & Medina, R.P. (2016). Adoption of feature selection and classification techniques in a decision support system, In Proceedings of the 2016 8th International Conference on Education Technology and Computers (ICETC 2016) Nanyang Executive Centre in NTU, Singapore, September 29, 2016.

International Journal of Information and Education Technology IJIET, Vol.7, No. 11, November 2017 ISSN 2010-3869, www.ijiet.org

Papers Presented and Published

813 Received Printed

copy on March 6,2017

Paper Presented and Published

[2] Comendador, B.E.V., Rabago, L.W. & Tanguilig III, B. T, (2016). An educational model based on knowledge discovery in databases (KDD) to predict learner’s behavior using classification techniques, In Proceedings of the IEEE International Conference On Signal Processing, Communications And Computing, H o n g k o n g , S A R , C h i n a , 7 2 5 - 7 3 0 , 978-1-5090-2708 August 7. Available Online: http://ieeexplore.ieee.org/document/7753623/

Available Online http://

ieeexplore.ieee.or g/document/7753623/

Paper Presented and Published [3] Sumande, C. T., Castolo, C. L. and Comendador, B.

E. V. (2016). The ICT level of confidence of course specialists in distance education: The Polytechnic university of the Philippines exper ience, Proceedings o f the 2015 International Conference on e-Commerce, e-Administration, e-Society, e-Education and e-Technology – Fall Session (e-CASE & e-Tech 2015),Kyoto Japan.

Turkish Online Journal of Distance Education-TOJDE, vol. 17, no. 4, pp. 175-189, October 2016.

Available Online: http://files.eric.ed.gov/fulltext/EJ1117060.pdf

Paper Presented and Published [4] Comendador, B. E. V. and Guillo, A. C. (2014). A

community cloud-based course management system using platform as a service (paas) for higher educational institutions,

International Journal of Information and Education Technology (IJIET,ISSN: 2010-3689 International Association of Computer Science and Information Community Press Vol. 4, No. 6, December 2014 DOI: 10.7763/IJIET.2014.V4.454

Available Online: http://www.ijiet.org/papers/454-F015.pdf

Implementation Report

BBTE class as they explore the eCLADSS features during software

demonstration conducted by Dr.Comendador at the ICT Computer Laboratory, NALLRC, PUP Manila.

Students starting to explore the eCourse Learning Analytics Decision

Support System (eCLADSS)

78

“It is God who arms me with

strength and makes my way perfect “.

Psalm 18:32 NIV

Thank you for your Attention ! Q and A

http://emabini.pup. edu.ph

will my students finish my online course?...will my students finish my online course? dr. benilda...

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