The New Era of Education Big Data

Transcription

1 The New Era of Education Big Data Dr. Fok Wai Tung, Wilton Director, e-learning Technology Development Laboratory, Department of Electrical and Electronic Engineering, The University of Hong Kong

2 1 2 Evolution of ICT for learning Standalone content distribution on-line content distribution 3 interactive learning 4 Mobile Learning 5 Assessment for Learning 6 Educational Data Mining

3 Problems in traditional teaching Lack of interaction One-way teaching is boring Real-time interactions between teacher and students, students to students become difficult

4 Interactive teaching and learning for Collecting ideas and feedback Enable students to draw, write and share with class through The lecture screen The device of other students (Peer review) Past Present

5 Multiple dimension discussion Different groups can discuss different dimensions and share their views on the screen with proper classification, Strengths Weaknesses Opportunities Threats

6 Objectives of Assessment for Learning HKSAR Education Bureau to guide student approaches to study provide students with feedback on their progress to assess if students have achieved the Intended Learning Outcomes and the level

7 Formative vs Summative Assessment Formative assessment carry out informally or formally in daily classroom learning and teaching throughout the school term/year. to provide feedback to learning and teaching. Summative assessment conduct at the end of the learning and teaching process. focuses on the product of learning mainly and is primarily used for measuring what a student has learned and how much has been achieved at the end of the school term/yea

8 Problems in traditional assessment Long feedback loop Difficult to identify students mistakes on time Make learning ineffective Effective assessment demanded days weeks Wait for a few weeks to get teacher s feedback

9 Assessment as learning interactive quiz Students can answer quiz questions with different attribute and types (e.g. multiple choices and fill-inthe-blanks) Analytical tools: Question level Student level Class level

10 Analytical tools: Question level (Common mistakes) The common mistakes made by the students can be identified. Teachers can use this tool to know more about the learning progress and make appropriate reflections on the results.

11 Analytic tool: Class level (performance distribution) Display the distribution of the class performance result with histogram

12 In the next assessment 1 st Quiz 2 nd Quiz

13 Analyze the performance of individuals in different topics/areas (Radar chart) E.g. in the Maths e- textbook, it shows Score in each area Current score and cumulated score Changes across years Skill analysis ELEC2815 Macro economic Shape & Space Statistics Micro economic Financial Management Number Year 1 Year 2 Marketing Algebra Accounting Measure

14 Monitor the learning progress of students Number Shape&Space Measure Statistic Algebra P1 P2 P3 P4 P5 P6

15 Assessment as learning interactive quiz Advantages: Enhance assessment efficiency More statistical information Shorten Long feedback loop from weeks to minutes Conduct test Mark script Distribute result Explain answer Real-time assessment and give feedback

16 In book assessment EMADS E-textbook

17 Educational Data Mining

18 20 years ago When network point-of-sales machine became popular, customer shopping behavior can be easily captured Customer profiling/ segmentation Market basket analysis Loyalty program Personalized offering Sales prediction

19 Education data mining Training Institutes Schools E-publisher Cloud-based Educational Data Mining and Learning analytics Service Platform Big Data Warehouse Student profiling/ segmentation Question analysis/ association (Adaptive assessment) Personalized course/ Course Recommendation Learning State Analysis Performance prediction

20 I can read students brain now

21 Market Basket Analysis What is Market Basket Analysis? Market basket analysis is a tool of knowledge discovery about co-occurrence of nominal or categorical items. It is a data mining technique to derive association between two data sets.

22 Association rules criteria To obtain the association rules, we usually apply two criteria: Minimum support Minimum confidence E.g. We conclude there is an association when support is greater than a threshold, e.g. > 80% Buy\Al so buy A B C A - 20% 10% B 40% - 80% C 90% 30% A B C C B A Rule1: Buy C also buy B Rule2: Buy A also buy C (Ref:

23 Application #1: Student profiling association of different subjects Demographic data Decision tree engine Assessment Skill analysis ELEC2815 Macro economic Skill analysis ELEC2815 Macro economic Skill analysis ELEC2815 Financial Management Financial Year 1 Management Year 2 Financial Year 1 Management Year 2 Accounting Year 1 Year 2 Accounting Macro economic Micro economic Micro economic Marketing Marketing Accounting Students Profile Micro economic Marketing

24 Application #2: Adaptive Question Adaptive Question Engine Engine

25 Application #3: Student clustering/ learning behavior analysis

26 Advantage of Education Data Mining

27 The next focus of e-learning development Hardware Devices Infrastructure WiFi Contents EMADS Educational Data Mining

28 Workshop Date: 24 Jan 2015 (Sat) Time: 10am-12pm 10am etextbook and education data mining 11am Mobile Device Management Venue: HKU Centennial Campus Room Registration:

29 Q&A