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Learning analytics is the use of data, analysis, and predictive modelling to improve teaching and learning. The purpose of learning analytics is to use data for enhancing learning.
The [course_title] course covers the essential concepts of learning analytics and shows how it is used in various context in education. For example, you will see the use of Learning Analytics in the automated intervention or to inform instructors and to promote scientific discovery.
Instead of these, the course introduces you to the tools and methods you need to know for enhancing learning and securing your students’ privacy and other rights.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.
Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
Course Credit: University of Texas Arlington
Course Curriculum
Module: 01 | |||
Archive of ProSolo & Assessment in DALMOOC | 00:22:00 | ||
Welcome to Week 1 Hangout Archive | 00:33:00 | ||
Introduction to DALMOOC Topics | 00:05:00 | ||
Introduction to Learning Analytics | 00:07:00 | ||
Getting Started With Data Analytics Tools | 00:03:00 | ||
Week 2 Introduction | 00:02:00 | ||
The Data / Analytics Cycle | 00:07:00 | ||
Visualization & Dashboards | 00:07:00 | ||
Systems-Level Considerations | 00:06:00 | ||
Tony Hirst Hangout Archive | 01:01:00 | ||
Module: 02 | |||
Jason Schumacher Hangout Archive | 00:28:00 | ||
Week 2 Hangout Archive | 00:30:00 | ||
Introduction in Social Network Analysis | 00:05:00 | ||
Network Structure and Data Sources | 00:08:00 | ||
Network Measures | 00:08:00 | ||
Network Modularity and Community Identification | 00:06:00 | ||
Gephi Community Introduction | 00:04:00 | ||
Gephi – An Introduction Tour | 00:17:00 | ||
Gephi – Modularity Analysis | 00:11:00 | ||
Gephi Modularity Tutorial by Jennifer Golbeck | 00:09:00 | ||
Shane Dawson Hangout Archive | 00:31:00 | ||
Module: 03 | |||
Week 3 Hangout Archive | 00:30:00 | ||
Shane Dawson | 00:04:00 | ||
Jenny McDonald | 00:06:00 | ||
Week 4 Introduction | 00:04:00 | ||
Social Network Analysis and Learning Design | 00:06:00 | ||
Social Network Analysis and Sense of Community | 00:05:00 | ||
Social Network Analysis and Creative Potential | 00:04:00 | ||
Social Network Analysis and Academic Performance | 00:05:00 | ||
Social Network Analysis and Social Presence | 00:06:00 | ||
Social Network Analysis and Understanding of MOOCs | 00:08:00 | ||
Module: 04 | |||
Results of Analytics of the Data Collected in the First Three Weeks of DALMOOC Hangout Archive | 00:31:00 | ||
Week 4 Hangout Archive | 00:32:00 | ||
Zach Pardos | 00:06:00 | ||
Phil Winne | 00:06:00 | ||
Regressors | 00:09:00 | ||
Classifiers (part 1) | 00:10:00 | ||
Classifiers (part 2) | 00:07:00 | ||
Case Study in Classification | 00:10:00 | ||
Cross-Validation and Over-Fitting | 00:06:00 | ||
Week 5 Hangout Archive | 00:19:00 | ||
Christopher Brooks | 00:04:00 | ||
Module: 05 | |||
Negin Mirriahi | 00:06:00 | ||
Behavior Detection Introduction | 00:03:00 | ||
Ground Truth | 00:06:00 | ||
Feature Engineering | 00:09:00 | ||
Diagnostic Metrics: Kappa and Accuracy | 00:09:00 | ||
Diagnostic Metrics: ROC and A’ | 00:09:00 | ||
Diagnostic Metrics: Correlation and RMSE | 00:11:00 | ||
Knowledge Engineering and Data Mining | 00:08:00 | ||
Over Validity Considerations | 00:05:00 | ||
Week 6 Hangout Archive | 00:30:00 | ||
Module: 06 | |||
Caroline Haythornthwaite | 00:06:00 | ||
Tiffany Barnes | 00:06:00 | ||
Text Mining Introduction | 00:14:00 | ||
Exploration of Collaborative Learning Process Analysis (research highlight) | 00:17:00 | ||
Text Mining Conceptual Overview of Techniques | 00:14:00 | ||
Tools and Resources | 00:07:00 | ||
LightSIDE: A Quick Tour | 00:11:00 | ||
Exploration of Student Attitudes in MOOCs (research highlight) | 00:15:00 | ||
Week 7 Hangout Archive | 00:31:00 | ||
Janice Gobert | 00:06:00 | ||
Module: 07 | |||
Mykola Pechenizkiy | 00:06:00 | ||
Data Preparation | 00:11:00 | ||
Getting a Sense of Your Data | 00:08:00 | ||
Exploring Basic Text Feature Extraction | 00:20:00 | ||
Interpreting Feature Weights | 00:09:00 | ||
Comparing Performance of Alternative Models | 00:04:00 | ||
Advanced Feature Extraction | 00:15:00 | ||
Week 8 Hangout Archive | 00:30:00 | ||
Elle Wang | 00:06:00 | ||
Week 9 Hangout Archive | 00:30:00 | ||
Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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