Data Driven Instruction for Personalized Learning

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1 Data Driven Instruction for Personalized Learning
Sagar Kamarthi, PhD Northeastern University NAE FOEE, Irvine, CA, Sept , 2016

2 Basic Principles of Data Driven Instruction
Focus: Are students learning? Philosophy: Goal-directed actions with periodic feedback enhances student learning

3 Steps for Data Driven Instructions
Assessment Periodic assessment to gather meaningful data Analysis Identify causes of strengths and weakness Action Teach to address barrier to learning and fill knowledge gaps Culture Institutionalize data driven instructional practices

4 Personalized Learning
Personalized learning results from tailored instruction aligned with students’ aptitude, background, knowledge, and interests NAE has recognized personalized learning as one of the fourteen Grand Challenges

5 Need for Personalized Learning in Engineering Courses
Changing demographics Increasing numbers of Under-Represented Minority (URM) and woman students in engineering Transfer students from community colleges International students Diversity of student preferences and aptitudes

6 Challenges to Tailored Instruction
Scalability is a main challenge for tailored instructions Enormous time and effort are required to create teaching material for differentiated instruction Difficult to elicit information regarding individual student characteristics Traditional resources are inadequate to collect assessment data, analyze data, and offer individualized instruction

7 Mass Customized Instruction (MCI) Model
I borrowed strategies from the field of mass customization to address scalability issues associated with differentiated instruction

8 Mass Customization Mass customization offers individually tailored products and services on a large scale as opposed to offering one standard solution to all, or offering expensive custom solutions to a few

9 Example of Mass Customization
Dell Computers FroYo yogurt NikeiD custom shoes Lands’ End custom apparel

10 Key Strategies of Mass Customization
Solution space development Robust process design Choice navigation

11 Solution Space for Personalized Learning
Identifying student attributes along which their learning needs diverge the most

12 Solution Space for Personalized Learning

13 Robust Process Design for Personalized Learning
Seamless and dynamic integration of different instructional materials and resources Classroom lectures Video documentaries Multimedia interactive learning tools Hands on activities Supplementary reading material

14 Choice Navigation for Personalized Learning
Tools to help students determine their own learning needs and means with manageable number of choices

15 Implementation of MCI Model
I partially implemented MCI model in Manufacturing Systems (IE 4530) course

16 Dimensions for Solution Space
Dimensions along which the student needs differ the most Prior knowledge Motivation level

17 2D Solution Space Considered for Manufacturing Systems Course
Student’s Motivation Level (y) Student’s Prior Knowledge (x)

18 Discretized 2D Solution Space
x1 = Students with good prior knowledge x2= Students with some gaps in their prior knowledge x3 = Students with serious deficiencies in their prior knowledge y1 = Students with high motivation to learn subject y2 = Students with neutral motivation to learning subject y3 = Students with poor motivation to learn subject Student’s Prior Knowledge (x) Student’s Motivation Level (y) y1 x2 x3 y2 y3 x1

19 Customized Instruction
x1 = Students with good prior knowledge y1 = Students with high motivation to learn subject Read an interesting case study and discuss it with his/her team mate Build a digital simulation model to observe variation in the production line efficiency under various scenarios (with or without storage buffers)

20 Customized Instruction
x3 = Students with serious deficiencies in their prior knowledge y3 = Students with poor motivation to learn subject Design and build a candle stand using turning machine in the lab Conducted a physical simulation of manual assembly line to observe variation in the production line efficiency under various scenarios (with or without storage buffers)

21 Customized Instruction
x3 = Students with serious deficiencies in their prior knowledge y3 = Students with poor motivation to learn subject

22 Prior Knowledge Components
Manufacturing Systems and Techniques Course (IE 4530) Statistics Optimimization Models Engineering Echonomics Simulation Engineering Materials Manufacturing Processes and Systems Performance Metrics of Manufacturing System Manufacturing System Design and Analysis Manufacturing Outsourcing Automation and Numerical Control

23 Instructional Materials to Address Prior Knowledge Gaps
Instruction feature Instructional Material Teaching materials Text book Class handouts Cases studies Video, vides streams of lectures Teaching modes Lecture using blackboard Power-point presentation Cases discussion Hands-on lab experiment Supportive tools Simulation models Show-and tell physical models Out-of-class assignments Plant tours Collaboration tools Blackboard Digital discussion rooms File sharing/exchange tools Record Keeping Excel documents To communicate student specific recommendation of instruction material and activities To post grades and feedback

24 To Improve Motivation for Manufacturing
Students are assigned a term paper to research the State of manufacturing in the U.S. Factors influencing the emigration of manufacturing from the U.S. Barriers to return manufacturing to the U.S. Imperatives and strategies to keep manufacturing in the U.S.

25 Result of Motivation Activities
Once students complete the term paper they are typically more motivated to learn about manufacturing and willing to dedicate their energy to learn the subject

26 Data Driven Approach to Personalized Learning

27 Score for “I Learned a Lot” in Last Four Offerings of Mfg. Sys. Course

28 Challenges and Solutions to Implementing MCI Model
Requires a lot of effort to create an array of instructional material Solution: Supplementary material can be develop collaboratively as an open source effort by the engineering education community

29 Challenges and Solutions to Implementing MCI Model
It is not easy to design and create effective assessment and feedback instruments Solution: Engineering education research community can develop feedback instruments through a research grant

30 Challenges and Solutions to Implementing MCI Model
Require sophisticated information technology tools to track individual student assessment and prescription data Solution: In collaboration with information systems experts and engineering education researchers can develop IT tools for tracking individual student assessment and prescription data

31 Data Analytics for Personalized Learning
Predictive Analytics Affinity Analysis

32 Data Analytics for Personalized Learning
Common words in comments for high performance students Grit Creativity Curiosity Common words in comments for poor performance students Consistency Sufficiency Focus

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