Educational Data Mining and Applications, Fall 2025

This course is one of the advanced courses in the Micro Courses on Educational Big Data. It introduces the basic concepts and principles of data mining, including: data preprocessing, frequent pattern mining, classification, and clustering. Also, students need to use tools such as Weka and Python for educational big data analytics. With the programming exercises and term project, students will be able to learn the fundamental ideas of educational data analysis and real applications with practical tools.
The course is offered at undergraduate level.

Notes on online recording of course sessions

All students enrolled in this course will be added to the team in Microsoft Teams for the corresponding course number.
The channel of the course in Microsoft Teams has been created: [Course Number: 348012].

Course Information

Instructor: Jenq-Haur Wang
E-mail: mailbox at the domain of our university, and my username is jhwang
Office: R1534, Technology Building (ext. 4238)
Office Hours: Tue. and Fri. 9:10-12:00am
Class Hours: Tue. 13:10-15:00pm, Thu. 10:10-11:00am, R301, Third Teaching Building
TA: Miss Chung (R1424, Technology Building)
E-mail: (TBA)
Prerequisite: data structures (and algorithms), database systems, discrete math (probability), operating systems, and working experience of high-level programming languages
Textbook: (Selected chapters)
- Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th ed., Morgan Kaufmann Publishers, Oct. 2022. ([DM4])
References:
- Tom White, Hadoop: The Definitive Guide, 4th ed., O'Reilly Media, 2015.
- Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010.
- Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, 3rd Edition, Cambridge University Press, Feb. 2020.
- Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, Learning Spark: Lightning-Fast Big Data Analysis, 2nd ed., O'Reilly Media, Jul. 2020.
- Others: online documents (Hadoop, Spark), tutorials, and selected academic papers.

Latest News

Aug. 14, 2025: The homepage for Educational Data Mining and Applications (Fall 2025) has been setup.

(Tentative) Schedule

* All slides can be downloaded at the iSchool+ plaform in NTUT.

Week	Date	Content	Reading	Note
1	Sep. 9 & 11, 2025	Course Overview Ch.1, Introduction
2	Sep. 16 & 18, 2025	Ch.2, Data, Measurements, and Data Preprocessing	DM4, Ch.2
3	Sep. 23 & 25, 2025	(Case Study: Preprocessing Educational Open Data)	DM4, Ch.2
4	Sep. 30 & Oct. 2, 2025	Ch.4, Pattern Mining: Basic Concepts and Methods	DM4, Ch.4	HW#1 Term Project Proposal
5	Oct. 7 & 9, 2025	Ch.4	DM4, Ch.4
6	Oct. 14 & 16, 2025	Ch.5, Pattern Mining: Advanced Methods (Case Study: Frequen Pattern Mining on Educaitonal Data)	DM4, Ch.5 (selected)	Due: HW#1 HW#2 Due: Team Registration
7	Oct. 21 & 23, 2025	Ch.6, Classification: Basic Concepts	DM4, Ch.6
8	Oct. 28 & 30, 2025	10/28: Invited Talk (TBD) (Case Study: Classifying Educational Data)	DM4, Ch.6	Due: HW#2 HW#3
9	Nov. 4 & 6, 2025	(11/4: Midterm Exam)
10	Nov. 11 & 13, 2025	Ch.7, Classification: Advanced Methods	DM4, Ch.7 (selected sections)	Due: HW#3 Due: Proposal
11	Nov. 18 & 20, 2025	(11/18: Leave for WI-IAT 2025) Ch.8, Cluster Analysis: Basic Concepts and Methods	DM4, Ch.8	HW#4
12	Nov. 25 & 27, 2025	(Case Study: Clustering Educational Data) Ch.9, Cluster Analysis: Advanced Methods		DM4, Ch.9 (selected sections)
13	Dec. 2 & 4, 2025	Distribtued Platforms: Hadoop, Spark MapReduce Programming (Lab: Spark cluster demo)		Due: HW#4
14	Dec. 9 & 11, 2025	Spark Programming (Lab: classification using Spark) Result Visualization and Interpretation
15	Dec. 16 & 18, 2025	Term Project Presentation (Week 1)
16	Dec. 23 & 25, 2025	Term Project Presentation (Week 2) (12/25: Leave for Constitution Day)
17	Dec. 30, 2025 & Jan. 1, 2026	Term Project Presentation (Week 3) (1/1: Leave for New Year's Day)
18	Jan. 6 & 8, 2026	Term Project Presentation (Week 4)

Programming Assignments and Projects

Please hand in your assignment before deadline according to the following instructions.

Submission Instructions

NOTE: Programs or projects in electronic files must be submitted directly to i-School+.

If you cannot successfully submit your work, please contact with the TA and the instructor.

Homeworks

There will be several written assignments and programming exercises that target at different data analysis tasks.

HW#1 : Ch.2 Data, Measurements, and Data Preprocessing [DM4]
HW#2 : Ch.4 Pattern Mining: Basic Concepts and Methods [DM4]
HW#3 : Ch.6-7 Classification [DM4]
(HW#4: Ch. 8 Clustering [DM4])

Projects

Term Project

Item	Description	Time
Proposal	You are required to submit a proposal for term project one week after midterm exam.	Nov. 11, 2025 (Tue.)
Topics	Two options: Project for data analysis or related system development Joining competitions as your term project. You can check the details of recent competitions as potential topics for term project.
Schedule	Due to our time limits, we have to start the term project presentation as early as Dec. 16, 2025 (Mon.). * [NOTE] All presentations must be finished within the scheduled time slots, which will be the last 3-4 weeks in this semester. No other time slots will be avbailable.	Dec. 16, 18, 23, 30, 2025 & Jan. 6, 8, 2026
Report	Each team is required to upload the final report after finishing your presentation. The final report should contain at least the following: presentation slides, and source code, and documents containing installation/execution instructions, team members and task responsibility	Jan. 9, 2026 (Fri.)

Exams

Midterm Exam: Nov. 3-7, 2025
- Date: Nov. 4, 2025 (Tue.)
- Time: 13:10-15:00pm
- Location: R301, Third Teaching Building
- Range: Ch. 1-2, 4-7 (TBD)
Final Exam: Jan. 5-9, 2026
- Note: There will be no final exam in this course. Instead, you are required to finish a term project for system development or competition.

Scores

Please check the homework submission site for more details.

E-mail: jhwang AT ntut . edu . tw
Created: Aug. 14, 2025.
Last Updated: Sep. 8, 2025.