Because of the technological advancement, online resources and software are hugely used in the educational sector. The [course_title] course teaches you how and when to use key methods for educational data mining and learning analytics on this data.
Throughout the course, you will explore the methods of data mining, learning analytics, learning-at-scale, student modelling, and artificial intelligence communities. The course also covers standard data mining methods which are frequently applied to educational data. Finally, the course teaches you when and how to use these methods along with the strength and weakness of these methods.
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.
Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of 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: Columbia University
|1.3: Classifiers, Part 1||00:12:00|
|1.4: Classifiers, Part 2||00:09:00|
|1.5: Case Study – San Pedro||00:10:00|
|1.6: Advanced Classifiers||00:05:00|
|2.1: Detector Confidence||00:08:00|
|2.2: Diagnostic Metrics, Part 1||00:09:00|
|2.3: Diagnostic Metrics, Part 2||00:13:00|
|2.4: Metrics for Regressors||00:10:00|
|2.5: Cross-Validation and Over-Fitting||00:08:00|
|2.6: Types of Validity||00:04:00|
|3.1: Ground Truth for Behavior Detection||00:08:00|
|3.2: Data Synchronization and Grain Size||00:08:00|
|3.3: Feature Engineering||00:10:00|
|3.4: Automated Feature Generation||00:10:00|
|3.5: Knowledge Engineering||00:10:00|
|4.1: Knowledge Inference||00:03:00|
|4.2: Bayesian Knowledge Tracing||00:12:00|
|4.3: Performance Factors Analysis||00:08:00|
|4.4: Item Response Theory||00:10:00|
|4.5: Advanced BKT||00:16:00|
|4.6: Recent Developments in Knowledge Inference||00:07:00|
|4.7: Memory Algorithms||00:06:00|
|5.1: Correlation Mining||00:12:00|
|5.2: Causal Mining||00:10:00|
|5.4: Sequential Pattern Mining||00:07:00|
|5.5: Network Analysis||00:10:00|
|6.1: Learning Curves||00:08:00|
|6.2: Scatterplots, Heat Maps, and Parameter Space Maps||00:07:00|
|6.3: State Space Diagrams||00:04:00|
|6.4: Other Awesome EDM Visualizations||00:05:00|
|Submit Your Assignment||00:00:00|
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